index.xml

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    <title>SynoSys</title>
    <link>https://synosys.github.io/</link>
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    <description>SynoSys</description>
    <generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© 2025 Center Synergy of Systems</copyright><lastBuildDate>Sat, 01 Jun 2030 13:00:00 +0000</lastBuildDate>
    <image>
      <url>https://synosys.github.io/media/sharing.png</url>
      <title>SynoSys</title>
      <link>https://synosys.github.io/</link>
    </image>
    
    <item>
      <title>Example Talk</title>
      <link>https://synosys.github.io/talk/example/</link>
      <pubDate>Sat, 01 Jun 2030 13:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/talk/example/</guid>
      <description>&lt;p&gt;Slides can be added in a few ways:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Create&lt;/strong&gt; slides using Academic&amp;rsquo;s &lt;a href=&#34;https://sourcethemes.com/academic/docs/managing-content/#create-slides&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;&lt;em&gt;Slides&lt;/em&gt;&lt;/a&gt; feature and link using &lt;code&gt;slides&lt;/code&gt; parameter in the front matter of the talk file&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Upload&lt;/strong&gt; an existing slide deck to &lt;code&gt;static/&lt;/code&gt; and link using &lt;code&gt;url_slides&lt;/code&gt; parameter in the front matter of the talk file&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Embed&lt;/strong&gt; your slides (e.g. Google Slides) or presentation video on this page using &lt;a href=&#34;https://sourcethemes.com/academic/docs/writing-markdown-latex/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;shortcodes&lt;/a&gt;.&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;Further talk details can easily be added to this page using &lt;em&gt;Markdown&lt;/em&gt; and $\rm \LaTeX$ math code.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Wearable data reveals distinct characteristics of individuals with persistent symptoms after a SARS-CoV-2 infection </title>
      <link>https://synosys.github.io/publication/sars-cov-2-infection-2025/</link>
      <pubDate>Tue, 18 Mar 2025 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/sars-cov-2-infection-2025/</guid>
      <description></description>
    </item>
    
    <item>
      <title>€2.3 Million Funding for SynoSys to Advance Long-COVID Research</title>
      <link>https://synosys.github.io/news/funding/</link>
      <pubDate>Thu, 19 Dec 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/news/funding/</guid>
      <description>&lt;p&gt;A major milestone for us: We have been awarded €2.3 million in funding from the Federal Ministry of Health (BMG) for the SynoSys.PC project, aimed at advancing the understanding of Long-COVID and advancing patient care.&lt;/p&gt;
&lt;p&gt;By harnessing high-resolution wearable data and applying methods from network science, data analytics and AI, SynoSys, in collaboration with the University Hospital Jena, seeks to develop innovative approaches and gain significant insights to the underlying mechanisms of Long-Covid and Post-Covid-Condition (PCC).&lt;/p&gt;
&lt;p&gt;The project builds on one of the largest datasets of its kind, with high-resolution individual time series of wearable data, e.g. heart rate, physical activity and sleep from more than 500,000 participants. Key goals include identifying wearable-based indicators of persistent symptoms and establishing an open competence center to drive the integration of digital technologies into health research and care.&lt;/p&gt;
&lt;p&gt;This project represents a significant leap toward leveraging digital health tools and smart technologies for public health. SynoSys is proud to be at the forefront of shaping the future of healthcare and we are super excited to start pushing the envelope in this area.
&lt;img src=&#34;bmg-logo-funding.jpg&#34; alt=&#34;BMG funding logo&#34;&gt;&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>€2.3 Million Funding for SynoSys to Advance Long-COVID Research</title>
      <link>https://synosys.github.io/project/funding-pc/</link>
      <pubDate>Thu, 19 Dec 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/project/funding-pc/</guid>
      <description>&lt;p&gt;A major milestone for us: We have been awarded €2.3 million in funding from the Federal Ministry of Health (BMG) for the SynoSys.PC project, aimed at advancing the understanding of Long-COVID and advancing patient care.&lt;/p&gt;
&lt;p&gt;By harnessing high-resolution wearable data and applying methods from network science, data analytics and AI, SynoSys, in collaboration with the University Hospital Jena, seeks to develop innovative approaches and gain significant insights to the underlying mechanisms of Long-Covid and Post-Covid-Condition (PCC).&lt;/p&gt;
&lt;p&gt;The project builds on one of the largest datasets of its kind, with high-resolution individual time series of wearable data, e.g. heart rate, physical activity and sleep from more than 500,000 participants. Key goals include identifying wearable-based indicators of persistent symptoms and establishing an open competence center to drive the integration of digital technologies into health research and care.&lt;/p&gt;
&lt;p&gt;This project represents a significant leap toward leveraging digital health tools and smart technologies for public health. SynoSys is proud to be at the forefront of shaping the future of healthcare and we are super excited to start pushing the envelope in this area.
&lt;img src=&#34;bmg-logo-funding.jpg&#34; alt=&#34;BMG funding logo&#34;&gt;&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Breaking Boundaries: Prof. Brockmann’s Vision for Complexity Research</title>
      <link>https://synosys.github.io/news/breaking_boundaries/</link>
      <pubDate>Thu, 19 Dec 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/news/breaking_boundaries/</guid>
      <description>&lt;p&gt;On February 6, 2025, Professor Dirk Brockmann, head of our Center Synergy of Systems, delivered his university-wide inaugural lecture at TU Dresden, attracting over 100 attendees from various disciplines.&lt;/p&gt;
&lt;p&gt;Under the intriguing title &amp;ldquo;Doing Science Like a Fungus – Complexity Research in the 21st Century&amp;rdquo;, Dirk Brockmann provided fascinating insights into complex systems and the hidden rules that govern social and natural phenomena. He emphasized the importance of interdisciplinary research, combining methods from physics, biology, and social sciences to better understand and address pressing global challenges. &lt;img src=&#34;featured2.JPG&#34; alt=&#34;Brockman-with Audience&#34;&gt;&lt;/p&gt;
&lt;p&gt;One particularly interesting aspect of his lecture was his discussion on the connection between science and magic. He explained that when people witness something they perceive as magical—something they believed to be impossible—it sharpens their senses and heightens their awareness. This, he argued, is precisely what scientists must do in their research: approach the unknown with a sense of wonder, question assumptions, and stay open to unexpected discoveries. Just as magic challenges our understanding of reality, scientific breakthroughs emerge when we push beyond the boundaries of what we think we know. Doing science with a compass rather than a map.
&lt;img src=&#34;featured3.JPG&#34; alt=&#34;Brockman-with Audience&#34;&gt;&lt;/p&gt;
&lt;p&gt;The event, held at the Fritz-Foerster-Bau of TU Dresden, provided an engaging platform for discussions, followed by a lively Q&amp;amp;A session and networking opportunity.
Prof. Brockmann’s inspiring lecture set the tone for his future work at SynoSys, demonstrating how complexity science can drive impactful research and innovative solutions.&lt;/p&gt;
&lt;p&gt;For those who missed the event, the full lecture is available online: &lt;a href=&#34;https://www.youtube.com/watch?v=iwMYuq4np6Y&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Watch here&lt;/a&gt;.&lt;/p&gt;
</description>
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    <item>
      <title>Do Alice Weidel and the AfD benefit from Musk&#39;s attention on X?</title>
      <link>https://synosys.github.io/news/x-election/</link>
      <pubDate>Fri, 29 Nov 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/news/x-election/</guid>
      <description>&lt;p&gt;Has Elon Musk manipulated X to give the AfD more reach? He has made it clear in posts, articles and election events that he is a fan - and at the same time, Alice Weidel’s reach on X has increased significantly. Could these developments be linked? This blog post describes a data analysis in which we take a closer look at which accounts have received how much reach and whether Musk’s influence can be determined. Where does the sudden increase in Weidel’s reach on X come from?&lt;/p&gt;
&lt;p&gt;Note: This work has not yet been peer reviewed.&lt;/p&gt;
&lt;p&gt;In recent weeks, there has been a lot of fuss about Elon Musk’s possible influence in favour of the AfD. First, on 20 December, he wrote on his platform X (formerly Twitter) that only the AfD can save Germany. On 28 December, Die Welt published a guest article by Musk in which he expressed the same position. Finally, on 9 January, he held a public conversation on X with the AfD candidate Alice Weidel.&lt;/p&gt;
&lt;p&gt;As already reported in one place or another, Weidel gained a lot of reach on X. The German parliamentary administration then announced that it would investigate whether Musk is influencing the election campaign. At the same time, the EU Commission is demanding insight into possible changes to X’s recommendation algorithms. This article analyses the possible impact of Musk’s activities on the reach of the AfD and Weidel in particular on social media.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Do Community Notes have a party preference?</title>
      <link>https://synosys.github.io/news/community_notes/</link>
      <pubDate>Fri, 29 Nov 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/news/community_notes/</guid>
      <description>&lt;p&gt;&lt;strong&gt;Community notes are supposed to curb disinformation on X - but they themselves follow political patterns. A new data analysis shows that Green Party posts are particularly criticised, but their notes are hardly considered helpful. At the same time, there is no clear political trend in the notes that are actually published. Why is this the case? The article analyses patterns in the distribution and rating of the notes and sheds light on the dynamics that can be derived from the data.&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Note: This work has not yet been peer reviewed.&lt;/strong&gt;&lt;/p&gt;
&lt;h2 id=&#34;community-notes-instead-of-fact-checks&#34;&gt;Community Notes instead of Fact-checks?&lt;/h2&gt;
&lt;p&gt;At the beginning of the year, &lt;a href=&#34;https://about.fb.com/news/2025/01/meta-more-speech-fewer-mistakes/&#34; target=&#34;_blank&#34;&gt; Meta&amp;rsquo;s CEO Mark Zuckerberg announced &lt;/a&gt; that the company was ending its collaboration with independent fact-checkers replacing them with X-style Community Notes (Notes for short). Zuckerberg claimed that fact-checking limited free speech. However, &lt;a href=&#34;https://netzpolitik.org/2025/zuckerbergs-kehrtwende-meta-goes-maga/&#34; target=&#34;_blank&#34;&gt;it seems more likely&lt;/a&gt; that this is a concession to Trump and the Republican Party, who accuse fact-checkers of being politically motivated.&lt;/p&gt;
&lt;p&gt;The accusation that fact-checking is biased in favour of the left or liberal camp is not new and is also made in &lt;a href=&#34;https://www.nzz.ch/meinung/faktenchecks-zuckerberg-musk-trump-medien-aktivismus-ard-faktenfinder-russland-nordstream-propaganda-fake-news-ld.1867044&#34; target=&#34;_blank&#34;&gt;German-speaking countries&lt;/a&gt;. Of course, fact-checking, like any other journalistic genre, is not free of errors and distortions. However, it is often overlooked that disinformation is frequently associated with right-wing and populist attitudes (Lasser et al. 2022, Törnberg and Chueri 2025). If fact-checks tend to scrutinise right-wing actors, this does not necessarily reveal a bias, but could also reflect an actual asymmetry (Mosleh et al. 2024). For example, a recent study shows that helpful Notes are also more likely to be found in posts from the Republicans (Renault et al. 2025).&lt;/p&gt;
&lt;p&gt;This blog post presents the results of a data analysis of German-language Notes that were rated as helpful or unhelpful at least 20 times and published between January 2024 and January 2025 inclusive. As Notes will also be launched on Facebook and Instagram in the future (initially only in the US), it is worth asking whether there is a focus on parties from a particular ideological spectrum.&lt;/p&gt;
&lt;h2 id=&#34;how-do-community-notes-work-on-x&#34;&gt;How do Community Notes work on X?&lt;/h2&gt;
&lt;p&gt;Notes are found as comments on posts and are intended to point out their shortcomings. The Notes for this data analysis show posts as incorrect or unverified (Fig. 1). Notes are written by previously registered users. To register, the account must be older than six months and a verified phone number is required. Because anyone can participate, Notes are often described as the wisdom of the crowd and contrasted with fact-checks as an unbiased alternative. However, this interpretation overlooks the fact that the users who write Notes are not free of political views. Their assessments and comments may therefore be influenced by their own interests or ideological biases (Allen et al. 2022).&lt;/p&gt;
&lt;p&gt;&lt;img src=&#34;https://synosys.github.io/Söder_Community_Note.png&#34; width=&#34;50%&#34; height=&#34;700px&#34; frameborder=&#34;0&#34;&gt;&lt;/img&gt;&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Figure 1&lt;/strong&gt; &lt;a href=&#34;https://x.com/Markus_Soeder/status/1879106313956765889&#34; target=&#34;_blank&#34;&gt;Post&lt;/a&gt; with a community note that was rated as helpful and therefore published.&lt;/p&gt;
&lt;p&gt;Precisely because a single registration is enough to write Notes for all posts on X, this is not yet a sign of quality for their correctness. For this reason, registered users can not only write their own Notes, but also rate the Notes of others as helpful or not. To arrive at a final rating, X uses a so-called bridging algorithm. This algorithm takes into account the position of registered users on the political spectrum and requires contributors with different viewpoints to rate the note as helpful (Wojcik et al. 2022). This process is often successful in reflecting the political spectrum, but it also ensures that significantly fewer Notes are published than are actually written.&lt;/p&gt;
&lt;h2 id=&#34;which-accounts-of-german-parties-and-politicians-receive-community-notes&#34;&gt;Which accounts of German parties and politicians receive Community Notes?&lt;/h2&gt;
&lt;iframe src=&#34;https://synosys.github.io/Barplots.html&#34; width=&#34;100%&#34; height=&#34;600px&#34; frameborder=&#34;0&#34;&gt;&lt;/iframe&gt;
&lt;p&gt;&lt;strong&gt;Figure 2&lt;/strong&gt; Number of posts with Community Notes.&lt;/p&gt;
&lt;p&gt;In the following, I present the results of the data analysis. In total, I analysed almost 9000 Notes, of which almost 1700 were considered helpful and therefore published. Figure 2a shows the accounts with the most Notes. Of all accounts on X, Markus Söder, the leader of the CSU and Chief Minister of Bavaria, received the most Notes. However, this changes when it comes to helpful Notes (Fig. 2b). Here, with the exception of Julian Reichelt, former editor-in-chief of Bild and founder of Nius, rather unknown accounts are among the top-ranked accounts. Political accounts, namely those of the CSU and Markus Söder, are only found from 8th place onwards. With this in mind, and also looking at Fig. 2c and Fig. 2d, we can see that the accounts with the most Notes and those with the most helpful Notes are in some cases very different.&lt;/p&gt;
&lt;h2 id=&#34;which-parties-receive-the-most-community-notes&#34;&gt;Which parties receive the most Community Notes?&lt;/h2&gt;
&lt;iframe src=&#34;https://synosys.github.io/Barplots_Parties.html&#34; width=&#34;100%&#34; height=&#34;500px&#34; frameborder=&#34;0&#34;&gt;&lt;/iframe&gt;
&lt;p&gt;&lt;strong&gt;Figure 3&lt;/strong&gt; Number of posts with Community Notes.&lt;/p&gt;
&lt;p&gt;Figure 3 shows the Notes of German politicians by party. Party scores are often strongly influenced by individual politicians. As shown in Figure 2, for the CSU it is mainly the official party account and that of Markus Söder. For the SPD it is mainly Ralf Stegner, member of the Bundestag, and Arno Gottschalk, financial policy spokesman for the Bremen parliamentary group. A rough analysis of the content shows that Stegner&amp;rsquo;s posts mainly relate to the Russian war in Ukraine.&lt;/p&gt;
&lt;p&gt;As before, the ranking of most Notes differs if you only look at the helpful ones. The Greens lead the ranking of Notes, closely followe&lt;/p&gt;
</description>
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      <title>New multi-author preprint about who should govern online environments</title>
      <link>https://synosys.github.io/news/choice_architects/</link>
      <pubDate>Fri, 29 Nov 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/news/choice_architects/</guid>
      <description>&lt;p&gt;In this multi-author paper, spearheaded by &lt;a href=&#34;https://www.mpib-berlin.mpg.de/person/friederike-stock/419405&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Friederike Stock&lt;/a&gt;, and as part of the &lt;a href=&#34;https://jrp.pscholars.org/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Junior Researcher Program&lt;/a&gt; with young researchers from 26 different countries and the project was supervised by Philipp Lorenz-Spreen. Little is known about who users themselves think should control their online environments, and under what circumstances. In our preregistered study, participants across 26 countries (N = 11,686) decided between combinations of three possible choice architects—governments, platforms, and individuals—and three objectives—societal, commercial, and personal—in seven real-world contexts. Across all countries, people strongly prefer to set their own rules for their online choice architectures. Find the full preprint &lt;a href=&#34;https://osf.io/preprints/osf/haqu9&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;here&lt;/a&gt;.&lt;/p&gt;
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      <title>New Research Explores News Values in Perceived Misinformation Across 24 Countries</title>
      <link>https://synosys.github.io/news/fake_news_24/</link>
      <pubDate>Fri, 29 Nov 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/news/fake_news_24/</guid>
      <description>&lt;p&gt;Sami Nenno, Postdoc in the Junior Research Group Computational Social Science at SynoSys, Center Synergy of Systems, has co-authored an insightful new paper, All the (Fake) News That’s Fit to Share? News Values in Perceived Misinformation Across Twenty-Four Countries. Co-written with Cornelius Puschmann, the study has been published in The International Journal of Press/Politics and investigates the relationship between journalistic news values and perceived misinformation in diverse national contexts.&lt;/p&gt;
&lt;p&gt;The research addresses a notable gap in the understanding of how misinformation is shaped and perceived globally. While much of the previous scholarship has focused on misinformation within WEIRD (Western, Educated, Industrialized, Rich, and Democratic) countries, this study expands the scope by including both WEIRD and non-WEIRD regions. Using a dataset of over 770,000 Facebook-shared URLs flagged as misinformation by users, the study analyzes the prevalence of five key news values: conflict, negativity, proximity, individualization, and informativeness. Through computational analysis, the authors explore how these news values appear in flagged and unflagged content and assess their variation across 24 countries.&lt;/p&gt;
&lt;p&gt;The research reveals that flagged content, often perceived as misinformation, tends to emphasize conflict and negativity more strongly than unflagged content. Other news values, such as proximity and individualization, also appear more prominently in flagged items, though with varying intensity. Significant differences in the prevalence of news values were observed between WEIRD and non-WEIRD countries. For instance, proximity and negativity were found to be more pronounced in WEIRD countries, whereas individualization was more prevalent in non-WEIRD contexts. The study also highlights unique patterns in countries such as the United States and Brazil, where conflict and negativity were particularly dominant in flagged content.&lt;/p&gt;
&lt;p&gt;The findings challenge existing frameworks of news values, which were primarily developed within Western contexts, by showing that these models may not fully account for the diversity of global media landscapes. This emphasizes the need for more inclusive theoretical models that better reflect regional and cultural differences.&lt;/p&gt;
&lt;p&gt;This study contributes to the growing field of misinformation research by providing a global perspective on how news values intersect with perceived misinformation. It highlights the influence of journalistic styles and national media systems on the characteristics of flagged content, offering valuable insights for academics, journalists, and policymakers seeking to address the challenges posed by misinformation.&lt;/p&gt;
&lt;p&gt;This publication was part of Sami Nenno&amp;rsquo;s PhD project and was written during his time at the University of Bremen and at the Alexander von Humboldt Institute for Internet and Society. He recently joined Synosys to continue his work. The paper is accessible online at &lt;a href=&#34;https://journals.sagepub.com/doi/10.1177/19401612241311893&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;this link&lt;/a&gt;.&lt;/p&gt;
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      <title>New Study Reveals Who’s Most Vulnerable to Misinformation—and why</title>
      <link>https://synosys.github.io/news/misinformation/</link>
      <pubDate>Fri, 29 Nov 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/news/misinformation/</guid>
      <description>&lt;p&gt;Our junior research group leader Philipp Lorenz-Spreen, whose work focuses on the digital information environment and among other things on misinformation, has co-authored a comprehensive meta-analysis of over 256,000 decisions from thousands of participants that sheds light on who is most susceptible to misinformation and the factors behind it. Their research reveals that susceptibility depends less on formal education and more on individual and contextual factors, such as age, analytical thinking, and political alignment. Older adults and those with strong analytical skills tend to discern truth from falsehood better, though they are also more skeptical, which means they classify more news as false overall. A tendency called “true news bias” emerges when people encounter news aligning with their political beliefs or previously seen content, making them more prone to believe information without scrutiny. These findings offer valuable insights for his further studies of the spreading dynamics of misinformation and the development of interventions that help people to navigate the information environment of the internet. &lt;a href=&#34;https://doi.org/10.1073/pnas.2409329121&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Read in full here&lt;/a&gt;.&lt;/p&gt;
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      <title>Inferring country-specific import risk of diseases from the world air transportation network</title>
      <link>https://synosys.github.io/publication/klamser-2024-inferring/</link>
      <pubDate>Sun, 24 Nov 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/klamser-2024-inferring/</guid>
      <description></description>
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      <title></title>
      <link>https://synosys.github.io/job/</link>
      <pubDate>Tue, 29 Oct 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/job/</guid>
      <description>&lt;p&gt;We’re not hiring at the moment, but we’re always looking for talented and motivated individuals. If you’re interested in joining us, we encourage you to check this page regularly for updates on new opportunities. You can also follow our work and stay connected—we’d love to hear from you in the future!&lt;/p&gt;
&lt;!--
We are thrilled to announce that SynoSys has been awarded €2.3 million in funding from the Federal Ministry of Health (BMG) for the innovative **SynoSys.PC project**. This pioneering initiative aims to revolutionize our understanding of Long-COVID and improve patient care through cutting-edge technology and research.

The **SynoSys.PC project** focuses on leveraging high-resolution wearable data, network science, data analytics, and AI to uncover the mechanisms behind Long-COVID and Post-COVID-Condition (PCC). With one of the largest datasets of its kind, including time series data from over 500,000 participants, our goal is to identify wearable-based indicators of persistent symptoms and establish an open competence center for integrating digital technologies into health research and care. --&gt;
&lt;!-- ### Key Highlights:

- **Collaboration with University Hospital Jena**
- **High-resolution time series data from 500,000+ participants**
- **Focus on wearable-based indicators for Long-COVID symptoms**
- **Development of an open competence center for health research** --&gt;
&lt;!--
### Vacancies:

- [**Research Associate/PhD Student**](https://tu-dresden.de/stellenausschreibung/11889)
- [**Research Associate/PostDoc**](https://tu-dresden.de/stellenausschreibung/11888)
- [**Research Associate for Database Management and Data Curation**](https://tu-dresden.de/stellenausschreibung/11890)

**Application Deadline:** January 31

Be part of a team that’s shaping the future of healthcare! Explore our open positions today and join us in driving innovation and improving public health with digital tools and smart technologies. Together, we can make a difference!

If you have any questions about the position, please contact us at [synosys@tu-dresden.de](mailto:synosys@tu-dresden.de). --&gt;
&lt;!-- ![SynoSys.PC Project](https://pixabay.com/illustrations/clock-wrist-watch-smartwatch-7708859/) --&gt;
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      <title>Artificial Intelligence is spreading fake news during the U.S. election campaign</title>
      <link>https://synosys.github.io/news/ai_fake_news_us_election/</link>
      <pubDate>Tue, 29 Oct 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/news/ai_fake_news_us_election/</guid>
      <description></description>
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      <title>Escalating Foreign Influence: How during the U.S. election campaign Russia, China, and Iran, create a surge of misleading narratives seeks to distort perceptions of key political figures</title>
      <link>https://synosys.github.io/news/kamala_harris_us_election/</link>
      <pubDate>Tue, 29 Oct 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/news/kamala_harris_us_election/</guid>
      <description></description>
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    <item>
      <title>Spatiotemporal Organization of Sleep</title>
      <link>https://synosys.github.io/project/spatiotemporal-sleep-analysis/</link>
      <pubDate>Fri, 20 Sep 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/project/spatiotemporal-sleep-analysis/</guid>
      <description>&lt;p&gt;We perform comprehensive spatiotemporal analyses of sleep patterns in a sample of over 100,000 individuals across Germany spanning almost 3 years. As part of the Robert Koch Institute&amp;rsquo;s Data Donation Project for the early detection of COVID-19, sleep data, in conjunction with heart rate and activity data, was collected in a privacy-preserving fashion by passive sensors in consumer wearable devices from April 2020 until December 2022, yielding more than 45 million nights of sleep observations overall. Sleep tracking studies for the first time enable a means of large-scale, real-time, non-invasive, continuous, longitudinal objective sleep measurements at a relatively low-cost with high accessibility. This combination of long observation period, and high temporal and spatial resolution is ideally suited to track sleep behavior over time, enabling the identification of robust seasonal trends as well as fine-grained analyses of short-term changes. The large number of regular participants from all over Germany allows for spatial analyses in a highly-resolved manner to discern complex patterns along geographical position or population density.&lt;/p&gt;
&lt;figure style=&#34;margin-top: 0;&#34;&gt;
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Spatiotemporal analyses of sleep behavior have great informative value for public health. Mapping sleep patterns is essential to understanding which external conditions pose risks for sleep, which ones may be protective, and how these conditions interact. The two main conditions that we focus on are in constant conflict with each other in our modern lives: - Sun time (or solar time) refers to the natural time structure on Earth based on the position of the sun in the sky, which gives rise to the timing and duration of day and night as well as the seasons. - Social time (or societal time) refers to the human-constructed timing framework, shaped by policy and social forces, that determines weekly structure, work and school schedules, time zone allocation and adherence to daylight saving time.
&lt;p&gt;Individual aspects of this interaction of sun and social time on sleep and circadian rhythms have been investigated in numerous studies to date. These studies range from small diary studies and detailed physiological studies in small but high-quality samples to large questionnaire-based studies, with studies based on wearable sensor data in larger samples a most recent addition. Their seemingly conflicting results on the relative contribution of social and sun time and the seasonality of human sleep highlight the complexity of the matter at hand. Moreover, it indicates the biological and epidemiological insights that may be gleaned from assessing systematic patterns in sleep with season, weekly structure and geographical position to pinpoint vulnerable populations and effective countermeasures where sun and social time conflicts may lead to unfavorable sleep outcomes and risks for acute and long-term health and safety may arise.&lt;/p&gt;
&lt;p&gt;With the Corona Data Donation project, we were able to assess many of the social and solar attributes of sleep for the first time together in one large, longitudinal data set of objective sleep phenotypes. Our results emphasize the effectiveness of passive sensing for monitoring and understanding trends in public health.&lt;/p&gt;
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</description>
    </item>
    
    <item>
      <title>Our Recent Paper Unveils Breakthrough Insights into Fatigue Symptoms in Post-COVID Syndrome and ME/CFS</title>
      <link>https://synosys.github.io/news/post_covid-syndrome/</link>
      <pubDate>Tue, 10 Sep 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/news/post_covid-syndrome/</guid>
      <description>&lt;p&gt;We are excited to share the results of our recent study, in which we report new insights into Post-exertional Malaise (PEM), a severe condition where physical activity amplifies symptoms in people with Post-COVID Syndrome (PCC) and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS).&lt;/p&gt;
&lt;p&gt;Our study highlights that even everyday activities, such as walking or light exertion, can significantly increase symptoms in affected patients. The root cause is an impaired ability of the body to utilize oxygen efficiently. Normally, cells utilize oxygen for energy production. However, in patients with PCC and ME/CFS, this process is disrupted, causing rapid exhaustion and muscle fatigue.&lt;/p&gt;
&lt;p&gt;We also found that many patients exhibit ongoing immune activation, as if their bodies are still fighting an infection long after recovery from their COVID-19. This immune response contributes to the impaired oxygen utilization and amplifies fatigue and other symptoms.&lt;/p&gt;
&lt;p&gt;We are excited because we believe that our findings may open up new pathways for developing effective treatments. Our research suggests that careful management of physical activity, including avoiding overexertion, could be key to preventing symptom flare-ups and to improving daily life for affected patients.&lt;/p&gt;
&lt;p&gt;We are excited about the potential impact of this study and are confident that it offers hope for those struggling with the long-lasting effects of PCC and ME/CFS. You can read the full paper &lt;a href=&#34;https://link.springer.com/article/10.1007/s15010-024-02386-8&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;here&lt;/a&gt;.&lt;/p&gt;
</description>
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    <item>
      <title>Towards an understanding of physical activity-induced post-exertional malaise: Insights into microvascular alterations and immunometabolic interactions in post-COVID condition and myalgic encephalomyelitis/chronic fatigue syndrome</title>
      <link>https://synosys.github.io/publication/haunhorst-2024-towards/</link>
      <pubDate>Mon, 09 Sep 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/haunhorst-2024-towards/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Clonal Interference in Heterogeneous Networks</title>
      <link>https://synosys.github.io/project/clonal_interference/</link>
      <pubDate>Sun, 01 Sep 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/project/clonal_interference/</guid>
      <description>&lt;p&gt;Our research group explores the intricate relationship between complex environmental structures and evolutionary processes, focusing on clonal interference in asexual populations. We aim to understand how organisms adapt in heterogeneous, network-like environments that closely resemble real-world scenarios.&lt;/p&gt;
&lt;p&gt;Clonal interference (CI) occurs in asexual populations when multiple beneficial mutations arise and compete, unable to combine through recombination. While CI has been studied in well-mixed or simple spatial structures, its behavior in complex, heterogeneous environments remains largely unexplored.&lt;/p&gt;
&lt;p&gt;&lt;img src=&#34;optional_cool.gif&#34; alt=&#34;cool gif&#34;&gt;&lt;/p&gt;
&lt;p&gt;Key Research Aims:&lt;/p&gt;
&lt;ol&gt;
&lt;li&gt;Understanding CI in Highly Heterogeneous Environments: We investigate how diverse network topologies influence clonal interference.&lt;/li&gt;
&lt;li&gt;Identifying Key Structural Components: We aim to pinpoint specific elements of network structures that significantly impact adaptive processes, understanding which features promote faster adaptation and which areas are likely sources of adaptive mutations.&lt;/li&gt;
&lt;li&gt;Shaping and Controlling Adaptive Evolution: By understanding how network structures influence evolution, we seek to develop strategies for potentially guiding adaptive processes in complex environments.&lt;/li&gt;
&lt;li&gt;Developing Analytical Tools: We&amp;rsquo;re working to create and refine mathematical and computational tools that effectively model and predict evolutionary dynamics in intricate systems, bridging network theory, evolutionary biology, and complex systems science.&lt;/li&gt;
&lt;/ol&gt;
&lt;p&gt;The insights gained from our research have potential applications across a wide range of fields. In epidemiology, our understanding of how pathogens evolve in structured host populations could form more effective disease management strategies. Conservation biologists might use our findings to guide habitat management practices, supporting species adaptation and resilience in fragmented environments. Our work could also lead to improved bioprocess designs by accounting for how environmental structure affects microbial evolution.&lt;/p&gt;
&lt;p&gt;Beyond these direct applications, the principles we uncover might have broader, more speculative implications. For instance, our approaches to mutation spread in biological networks could provide insights into how content goes viral on social media platforms with diverse user structures. The competition between genetic strains in our research might elucidate how ideas, innovations and cultural practices propagate through social and professional networks.&lt;/p&gt;
&lt;p&gt;As we continue to explore the intricate interplay between network complexity and evolutionary dynamics, we&amp;rsquo;re not merely advancing theoretical knowledge. We&amp;rsquo;re developing a deeper, more nuanced understanding of how adaptation occurs in complex systems. This research has the potential to open new avenues for managing and harnessing evolutionary processes in various fields, spanning from biology to the social sciences. By bridging the gap between network theory and evolutionary biology, we&amp;rsquo;re paving the way for a more comprehensive understanding of how information – be it genetic, cultural, or technological – spreads and competes in our interconnected world.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>COVID-19 Detection from Wearables</title>
      <link>https://synosys.github.io/project/covid_detection/</link>
      <pubDate>Sun, 01 Sep 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/project/covid_detection/</guid>
      <description>&lt;p&gt;Controlling the spread of infectious diseases early in a pandemic is crucial. The recent COVID-19 outbreak and its rapid global spread highlight the urgent need for tools that can limit the spreading of infectious diseases at the earliest stage possible to prevent global pandemics.
Previous research has suggested the potential to detect COVID-19 infections through the observation of physiological alterations occurring after the onset of the disease, including elevated resting heart rates, extended sleep durations, and decreased physical activity. Wearable sensors, equipped to monitor these changes, in conjunction with machine learning algorithms trained on data from these sensors, could identify these variations, thus providing a method to quickly classify individuals as infected.
Utilizing wearable sensor data from the Corona Data Donation Project, which encompasses resting heart rate, step count, and sleep duration time series from over 120,000 voluntary participants, a Logistic Regression model was developed for the classification of submitted COVID-19 test results. This model achieved classification success, as evidenced by a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 0.58, precision of 0.26, and recall of 0.54. The implementation of the catch22 feature set for feature extraction from the submitted wearable sensor data enhanced the interpretability of the classification algorithm. It also enables a more detailed analysis of the specific physiological changes used to differentiate individuals testing positive or negative for COVID-19.
This study emphasizes the capability of predicting COVID-19 across large cohorts and underscores the value of employing infectious disease prediction models based on wearable sensor data as an additional public health tool.
&lt;strong&gt;The LR model presented in Fig. 1 demonstrated&lt;/strong&gt; prediction capabilities for COVID-19 test results surpassing random chance, thereby facilitating the early detection of this infectious disease. Additionally, the incorporation of catch22 features into the feature engineering process enhanced the interpretability of the classification decisions made by the model.
The predictive LR model shown in Fig. 1 demonstrated satisfactory performance, notable given the voluntary nature of data submission, the impossibility of verifying correct and constant wear of devices, reliance on daily averages, absence of precise test and symptom onset dates, and the utilization of multiple devices across a broad cohort. The present study introduced a predictive model that has been trained on a considerable cohort size, addressing concerns from existing research about models only trained on significantly smaller cohorts. Additionally, this study tackled the issue of geographic underrepresentation by developing a predictive model for COVID-19 classification with data donated outside the United States, addressing other concerns in existing literature.
Additionally confirmatory models have been developed which showed performances consistent with existing literature, validating its efficacy in confirming COVID-19 infections within this study’s framework. Enhancements in confirmatory capability were noted with the increasing days included after a COVID-19 test submission, indicating that a longer observational period bolsters the model’s confirmatory power. Employing interpretable catch22 feature engineering provided clarity in the classification process and verified that the detected signals coincide with physiological responses to COVID-19 infection, as shown in Fig. 2 and 3. This consistency in signal detection across the models and their alignment with other research accentuates the catch22 features’ ability to accurately capture vital data signals from wearable sensor data. Conclusively, this study proposes a combined approach of catch22 feature engineering and machine learning classification algorithms as an effective strategy in combating the rapid spread of infectious diseases. Highlighting that this methodology could be particulary valuable in the initial stages of a pandemic, where traditional methods might be unavailable. Thus, it offers an essential addition to public health resources. This study stresses the potential of utilizing wearable sensor data, combined with automated feature engineering and machine learning classification algorithms, for the detection and confirmation of diseases like COVID-19. With additional advancements machine learning approaches to detect illnesses like COVID-19 using wearable sensor data could significantly improve the readiness and response to future public health crise&lt;/p&gt;
 &lt;figure&gt;
  &lt;img src=&#34;./figures/paul_w2.png&#34; alt=&#34;drawing&#34; width=&#34;Full&#34;/&gt;
  &lt;figcaption&gt;
  Fig. 2 - Permutation Feature Importance for the test dataset for the Confirmatory LR: Focusing on features that led to a mean increase in model performance (increase in Geometric Mean (GM)). This represents a selection from the complete set of features. Each box displayed on the dashed line in the plot illustrates the range from the first to the third quartile of the GM reduction observed over 100 iterations for each catch22 feature. The median reduction in GM is denoted by the orange line within each box. The whiskers extend to the most extreme GM reduction value that falls within 1.5 times the interquartile range from the edges of the box. Any outliers are represented by dots.
&lt;/figcaption&gt;
&lt;/figure&gt;
 &lt;figure&gt;
  &lt;img src=&#34;./figures/paul_w3.png&#34; alt=&#34;drawing&#34; width=&#34;Full&#34;/&gt;
  &lt;figcaption&gt;
  Fig. 3 - Permutation Feature Importance for the test dataset for the Confirmatory XGBoost Model: Focusing on features that led to a mean increase in model performance (increase in Geometric Mean (GM)). This represents a selection from the complete set of features. Each box displayed on the dashed line in the plot illustrates the range from the first to the third quartile of the GM reduction observed over 100 iterations for each catch22 feature. The median reduction in GM is denoted by the orange line within each box. The whiskers extend to the most extreme GM reduction value that falls within 1.5 times the interquartile range from the edges of the box. Any outliers are represented by dots.
&lt;/figcaption&gt;
&lt;/figure&gt;
</description>
    </item>
    
    <item>
      <title>FEDORA Project Secures Funding to Explore Post-COVID Ecological Dynamics</title>
      <link>https://synosys.github.io/news/fedora/</link>
      <pubDate>Sun, 01 Sep 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/news/fedora/</guid>
      <description>&lt;p&gt;We are thrilled to announce that our project, “FEDORA - Federated Network Modeling of Ecological Complex Dynamical Patterns in Post-COVID,” has successfully secured funding. This pioneering initiative aims to advance the understanding of how ecological systems are affected by complex dynamical patterns emerging in the aftermath of the COVID-19 pandemic.&lt;/p&gt;
&lt;p&gt;The FEDORA project will focus on federated network modeling to examine these complex patterns, particularly those related to wearable technology, sleep, as well as persistent symptoms observed post-COVID. Through a collaborative approach, the project will harness cutting-edge data and modeling techniques to reveal new insights into the long-term impacts of COVID-19 on ecological and human health.&lt;/p&gt;
&lt;p&gt;Our interdisciplinary team will work together to develop groundbreaking models that integrate data from various sources, creating a comprehensive understanding of the interplay between ecological and health dynamics in the post-COVID world. The insights gained from this project will not only contribute to scientific knowledge but also guide future public health strategies and ecological conservation efforts.&lt;/p&gt;
&lt;p&gt;We look forward to sharing more updates as the project progresses and invite the community to stay tuned for further developments.&lt;/p&gt;
&lt;p&gt;&lt;img src=&#34;logo-federal.jpg&#34; alt=&#34;FEDORA Project Logo&#34;&gt;&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>FEDORA Project Secures Funding to Explore Post-COVID Ecological Dynamics</title>
      <link>https://synosys.github.io/project/fedora/</link>
      <pubDate>Sun, 01 Sep 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/project/fedora/</guid>
      <description>&lt;p&gt;We are thrilled to announce that our project, “FEDORA - Federated Network Modeling of Ecological Complex Dynamical Patterns in Post-COVID,” has successfully secured funding. This pioneering initiative aims to advance the understanding of how ecological systems are affected by complex dynamical patterns emerging in the aftermath of the COVID-19 pandemic.&lt;/p&gt;
&lt;p&gt;The FEDORA project will focus on federated network modeling to examine these complex patterns, particularly those related to wearable technology, sleep, as well as persistent symptoms observed post-COVID. Through a collaborative approach, the project will harness cutting-edge data and modeling techniques to reveal new insights into the long-term impacts of COVID-19 on ecological and human health.&lt;/p&gt;
&lt;p&gt;Our interdisciplinary team will work together to develop groundbreaking models that integrate data from various sources, creating a comprehensive understanding of the interplay between ecological and health dynamics in the post-COVID world. The insights gained from this project will not only contribute to scientific knowledge but also guide future public health strategies and ecological conservation efforts.&lt;/p&gt;
&lt;p&gt;We look forward to sharing more updates as the project progresses and invite the community to stay tuned for further developments.&lt;/p&gt;
&lt;p&gt;&lt;img src=&#34;logo-federal.jpg&#34; alt=&#34;FEDORA Project Logo&#34;&gt;&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Fungal Growth</title>
      <link>https://synosys.github.io/project/fungal_growth/</link>
      <pubDate>Sun, 01 Sep 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/project/fungal_growth/</guid>
      <description>&lt;p&gt;Fungi interact in a mycorrhizal symbiosis with plants or are simple decomposers of organic matter.
In this project, we investigate the growth of filamentous fungi by creating and investigating mathematical growth models.
We cooperate with experimental biologists from the &lt;a href=&#34;https://www.aguilar-ecology.com/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;aguilar ecology lab&lt;/a&gt; who have a profound knowledge of the growth patterns of different fungal species (see Fig.1).
In a first &lt;a href=&#34;https://academic.oup.com/ismecommun/article/2/1/2/7460968&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;network analysis of our collaborators&lt;/a&gt;, they identified that some network traits vary most in between species, as for example the meshedness (how different the network is from a fully-connected one without crossing edges).&lt;/p&gt;
&lt;figure&gt;
  &lt;img src=&#34;./figures/2022Aguilar_network_paper_fig3adapted.png&#34; alt=&#34;drawing&#34; width=&#34;800&#34;/&gt;
  &lt;figcaption&gt;Fig.1 - Different species of hyphal growing fungi and the reconstruction of the network they are forming.
  Figure is adapted from &lt;a href=&#34;https://academic.oup.com/ismecommun/article/2/1/2/7460968&#34;&gt;Aguilar-Trigueros et al. (2022)&lt;/a&gt;.
  &lt;/figcaption&gt;
&lt;/figure&gt;
&lt;p&gt;We address the problem from a modeling perspective, that means we build an agent-based model, which is motivated from real fungi.
In our model each agent represents the tip of a hyphen and the tip as well as the whole hyphen release an enzyme.
This enzyme diffuses though the environment and other tips/agents can sense the local concentration and gradient.
This is all the information the tip has about its environment, i.e. only local information is allowed.
New Tips/Agents are created either by an apical branching event, at which one tip splits in two, or by a lateral branching event, where from the lower half of a tip compartment multiple branches grow perpendicular to the local growth direction.
By varying some fundamental parameters of the model as the diffusion coefficient, the enzyme release rate or the number of lateral branches, different shapes can emerge as shown if Fig. 2.&lt;/p&gt;
&lt;figure&gt;
  &lt;img src=&#34;./figures/3_mycel_compilation.png&#34; alt=&#34;drawing&#34; width=&#34;1200&#34;/&gt;
  &lt;figcaption&gt;Fig.2 -
  Depending on the parameters, the mycelium grows densely (A) or less densely (B, C) if for example the enzyme release rate is increased (B)  or the septation rule is adapted (C). The enzyme in the environment (orange and red background color) inhibits the growth of the mycelium, thus the mycelium is more responsive to its own form (compare A with B). In (A, B) the septation and lateral branching form always two septa and branches at each septation event. In (C) 6 septa and branches are formed during a septation event. While the quadrants 2-4 show the concentration on a variable linear scale (bottom color bar), the 1st quadrant uses a fixed logarithmic scale (top color bar) to enable a fair comparison between simulations.
  &lt;/figcaption&gt;
&lt;/figure&gt;
&lt;p&gt;Our goals are now to reproduce key structural properties of real fungal species with a model as simple as possible, study the statistical properties of the different phases of the model and what task the fungi can perform beast at every phase.
This is exciting, especially because we are able to switch between theory, modelling and experiments.
The big picture is of course the role of the fungi in the ecosystems in connection with their growth strategies.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Import Risk</title>
      <link>https://synosys.github.io/project/import_risk/</link>
      <pubDate>Sun, 01 Sep 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/project/import_risk/</guid>
      <description>&lt;p&gt;Our globalized world is strongly connected, on the small scale by buses, trains, cars, and on the largest, global scale by the Worldwide Air transportation Network (WAN).
It is likely that our connectivity has not reached its peak yet, since for example the number of globally scheduled air passengers increased yearly by about 6% between 2004 and 2019.
The connectivity has immense consequences on the propagation of disease since alongside passengers everything that sticks to them, such as bacteria, viruses, earth clumps beneath their shoes etc. comes with.&lt;/p&gt;
&lt;p&gt;This project crosses the fields of network science, transportation, mobility and disease dynamics and it&amp;rsquo;s an offspring of the idea to not use the conventional distance (in meter, miles, feet &amp;hellip; pick your unit) as a metric but an &lt;strong&gt;effective distance&lt;/strong&gt; that is based on the mobility between regions.
When - $P(B | A)$ is the probability to travel to B from A (which is computed from mobility fluxes) than the effective distance is $ d _{\text{eff}} (A | B) = d_0 - log (P(i| n_0)) $ -.
In the seminal paper by &lt;a href=&#34;https://www.science.org/doi/full/10.1126/science.1245200&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Brockmann and Helbing (2013)&lt;/a&gt; _effective distance $d _{\text{eff}}$ - was shown to linearly correlate with the arrival time of diseases as shown in Fig. 2.&lt;/p&gt;
 &lt;figure&gt;
  &lt;img src=&#34;./figures/eff_dist_paper_adapted_1.png&#34; alt=&#34;drawing&#34; width=&#34;Full&#34;/&gt;
  &lt;figcaption&gt;
  Fig. 2 - The shortest path tree based on the &lt;b&gt;effective distance&lt;/b&gt; with the airport of Hong Kong as origin (&lt;b&gt;A&lt;/b&gt;) and how a pandemic spreads around the globe (&lt;b&gt;B&lt;/b&gt;).
  While the spread on the world map seems rather random, you see a nice circular wave spreading in the effective distance tree, illustrating the linear correlation to disease arrival times.
  Adapted from &lt;a href=&#34;https://www.science.org/doi/full/10.1126/science.1245200&#34;&gt;Brockmann and Helbing&lt;/a&gt;.
&lt;/figcaption&gt;
&lt;/figure&gt;
&lt;p&gt;In our recent paper &lt;a href=&#34;https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011775&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Klamser et al. (2024),&lt;/a&gt; we deepen the connection between the effective distance shortest path tree, spreading dynamics and mobility.
We present a way to estimate the passenger flow between any two airports (including those without direct connections) given that we only know the number and maximal capacity of planes (see illustrative example in Fig. 3).
In the end, the method allows us to estimate the number of infected passenger leaving B when we know the number of infected that entered A.
Thus, we know the risk of importation and why it is named &lt;b&gt;import risk method&lt;/b&gt;.&lt;/p&gt;
 &lt;figure&gt;
  &lt;img src=&#34;./figures/relrisk.png&#34; alt=&#34;drawing&#34; width=&#34;400&#34;/&gt;
  &lt;figcaption&gt;
  Fig. 3 - Import Risk Example: 100 infected passengers board planes at airport X en route to destination airports elsewhere in the network, going through transit airports in the process. The relative import risk at a given destination is the fraction of the 100 individuals that entered at airport X and exited at that destination airport (red).
  &lt;/figcaption&gt;
&lt;/figure&gt;
&lt;p&gt;The mechanistic idea behind the import risk method is to let a random walker start at node B of the WAN and exit at each node with a probability derived from the effective distance shortest path tree.
The final fraction of times it leaves at node A is the import risk probability.
Now, this method is parameter free and motivated by disease dynamics, but at the same time it provides a method to estimate mobility on a global scale.
The mobility estimation is a classic task in transportation science with well-established models as the gravity model (proposed in &lt;a href=&#34;https://www.jstor.org/stable/2087063?casa_token=5goDmVg4aOkAAAAA%3A6Y9zup7UcSjT8DQ1izhRza_-N568nGLP_43ikQys0Vzoux-4qaYKp1u0Cruz2YwZ2hggPU2PIKW4oqbB6xnFtaJIgj-oX_zCbMwLneoPv8OzCdsFXA&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;1946 by Zipf&lt;/a&gt;).
A core aspect of our import risk paper &lt;a href=&#34;https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011775&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Klamser et al. (2024)&lt;/a&gt; is to compare our method to established mobility models.
We found that our approach outperforms the established models, and the crazy thing is, that our method is parameter free, while the gravity model always needs parameter estimation (one needs to know the real mobility flow beforehand).
This holds true if we check the prediction quality for the 10 countries with the highest import probability, which is especially important for disease propagation since these countries are the one that should increase their passenger control (shown in Fig. 4).&lt;/p&gt;
 &lt;figure&gt;
  &lt;img src=&#34;./figures/ir_paper_fig_10cntrs.png&#34; alt=&#34;drawing&#34; width=&#34;800&#34;/&gt;
  &lt;figcaption&gt;
  Fig. 4 - Mobility model comparisons for top 10 import countries: The well-established gravity model with exponential (&lt;b&gt;A&lt;/b&gt;) and power-law (&lt;b&gt;B&lt;/b&gt;) distance decay, the parameter free radiation model (&lt;b&gt;C&lt;/b&gt;) and our import risk model (&lt;b&gt;D&lt;/b&gt;).
  Each dot is a country that is among the 10 countries with the highest import probability from another country (10 target countries x 183 source countries).
  A true or false positive (T.Pos. or F. Pos.) means that the country is or is not among the 10 countries with the highest reference import probability.
  A false negative (F. Neg.) means that it belongs according to reference it is among the top 10 but was not detected by the respective model.
  Figure adapted from &lt;a href=&#34;https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011775&#34;&gt;Klamser et al. (2024)&lt;/a&gt;.
  &lt;/figcaption&gt;
&lt;/figure&gt;
&lt;p&gt;We applied the measure during COVID-19 to estimate the countries that have been reached by Omicron without it being detected &lt;a href=&#34;https://www.covid-19-mobility.org/reports/importrisk_omicron_update/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;here&lt;/a&gt;.
And in a big cooperation with great colleagues that have been put together by &lt;a href=&#34;https://manliodedomenico.com/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Manlio De Domenico,&lt;/a&gt; we have developed and validated a framework (&lt;a href=&#34;https://academic.oup.com/pnasnexus/article/2/6/pgad192/7191545?login=false&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Klamser and d&amp;rsquo;Andrea et al. (2023)&lt;/a&gt;)that can estimate from sparse and noisy samples via phylogenetic analysis the time when the variant first occurred in the country of origin, how it spreads across the globe with our import risk model, and how the countries are affected by a disease dynamic model.
We were even able to smash everything into one number, which we call the &lt;strong&gt;pandemic delay&lt;/strong&gt;, that estimates how long it takes for a new emerging variant to make out 10% of all current cases in a respective country.
It revealed that only by combining mobility data with a phylogenetic estimate of the reproduction number, the pandemic potentials of the lineages are correctly
assessed relative to each other.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Just published: Using smartphones to study vaccination decisions in the wild</title>
      <link>https://synosys.github.io/news/smartphones_vaccination_paper/</link>
      <pubDate>Thu, 08 Aug 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/news/smartphones_vaccination_paper/</guid>
      <description>&lt;p&gt;We are pleased to share our latest research findings with the community. Recently, we have developed an innovative method in cooperation with the DTU to study how people make vaccination decisions in everyday life. For this purpose, we used a specially designed smartphone app that captures real interactions and allows us to analyze the impact of these interactions on vaccination decisions.
Our findings show that people in real decision-making situations rely more heavily on their personal experiences or personal risk assessments than on what they hear or see from others. This method allows us to better understand decision-making behavior in a real-world context.
We are confident that these new insights will provide valuable input for shaping future vaccination campaigns.&lt;/p&gt;
&lt;p&gt;You can find the detailed paper here:&lt;/p&gt;







  
    


&lt;div class=&#34;pub-list-item&#34; style=&#34;margin-bottom: 1rem&#34;&gt;
  &lt;i class=&#34;far fa-file-alt pub-icon&#34; aria-hidden=&#34;true&#34;&gt;&lt;/i&gt;

   
  &lt;a href=&#34;https://synosys.github.io/publication/girardini-2024-understanding/&#34;&gt;Using smartphones to study vaccination decisions in the wild&lt;/a&gt;.&lt;br /&gt;
  &lt;span class=&#34;article-metadata li-cite-author&#34;&gt;
    
  &lt;span &gt;
  N A Girardini&lt;/span&gt;, &lt;span &gt;
  A Stopczynski&lt;/span&gt;, &lt;span &gt;
  O Baranov&lt;/span&gt;, &lt;span &gt;
  C Betsch&lt;/span&gt;, &lt;span &gt;
  &lt;a href=&#34;https://synosys.github.io/authors/dirk/&#34;&gt;D Brockmann&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
  S Lehmann&lt;/span&gt;, &lt;span &gt;
  R Böhm&lt;/span&gt; &lt;/span
  &gt;&lt;br /&gt;
   PLOS Digital Health 3(8): e0000550
   (2024).
  &lt;p&gt;








  

















&lt;a class=&#34;btn btn-outline-primary btn-page-header btn-sm&#34; href=&#34;https://doi.org/https://doi.org/10.1371/journal.pdig.0000550&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;
  DOI
&lt;/a&gt;



  
  
  
    
  
  
  
  
  
    
  
  &lt;a class=&#34;btn btn-outline-primary btn-page-header btn-sm&#34; href=&#34;https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000550&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;
    View&lt;/a&gt;
  
&lt;/p&gt;
  
&lt;/div&gt;

  


</description>
    </item>
    
    <item>
      <title>Using smartphones to study vaccination decisions in the wild</title>
      <link>https://synosys.github.io/publication/girardini-2024-understanding/</link>
      <pubDate>Thu, 08 Aug 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/girardini-2024-understanding/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Complexity Explorables @ Cosmo</title>
      <link>https://synosys.github.io/project/explorables_at_cosmo/</link>
      <pubDate>Sat, 20 Jul 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/project/explorables_at_cosmo/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Real-time assessment of motives for sharing and creating content among highly active Twitter users</title>
      <link>https://synosys.github.io/publication/phillip-2024-twitter/</link>
      <pubDate>Tue, 02 Jul 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/phillip-2024-twitter/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Toolbox of individual-level interventions against online misinformation</title>
      <link>https://synosys.github.io/publication/phillipp-2024-toolbox/</link>
      <pubDate>Mon, 13 May 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/phillipp-2024-toolbox/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Susceptibility to Online Misinformation: A Systematic Meta-Analysis of Demographic and Psychological Factors</title>
      <link>https://synosys.github.io/publication/phillipp-2024-susceptibility/</link>
      <pubDate>Fri, 03 May 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/phillipp-2024-susceptibility/</guid>
      <description></description>
    </item>
    
    <item>
      <title>The Evolution of Online News Headlines</title>
      <link>https://synosys.github.io/publication/phillipp-2024-evolution/</link>
      <pubDate>Fri, 03 May 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/phillipp-2024-evolution/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Human Mobility</title>
      <link>https://synosys.github.io/topics/human_mobility/</link>
      <pubDate>Wed, 06 Mar 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/topics/human_mobility/</guid>
      <description>&lt;div align=&#34;justify&#34;&gt;
From daily commutes and travel behaviors to the migration of populations on a global scale, human mobility encompasses a vast array of phenomena with profound societal implications. By leveraging innovative data sources such as GPS traces, mobile phone records, and social media check-ins, we gain unprecedented insights into the factors driving human movement patterns. Understanding human mobility is not only essential for urban planning, transportation management, and public health but also sheds light on broader societal trends, including the spread of infectious diseases, urbanization, and social inequalities. Through interdisciplinary collaboration and advanced analytical techniques, we strive to uncover the underlying mechanisms shaping human mobility and its impact on our interconnected world. Join us on a journey to unravel the complexities of human mobility and harness its potential to address pressing challenges and shape the future of our societies.
&lt;/div&gt;</description>
    </item>
    
    <item>
      <title>The story of SynoSys, a department of CIDS</title>
      <link>https://synosys.github.io/old_news/welcome_synosys/</link>
      <pubDate>Tue, 13 Feb 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/welcome_synosys/</guid>
      <description>&lt;p&gt;In 2023 (or earlier?), i didnt want to be at the RKI anymore, the bureaucracy was getting out of hand. Thus I decided it was time for something new. An exciting endevour. I wrapped up my beloved crew and we set up shop in Dresden. Born was Synergy of Systems (SynoSys) department.&lt;/p&gt;
&lt;p&gt;Aforementioned and hand picked beloved inaugural crew is constitued of&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Dirk Brockmann: Me duh, the founding director&lt;/li&gt;
&lt;li&gt;Pascal Klamser: PostDoc&lt;/li&gt;
&lt;li&gt;Angelique Burdinski: PhD student&lt;/li&gt;
&lt;li&gt;Adrian Pelcaru: the almost PhD student, codename Garfunkel&lt;/li&gt;
&lt;li&gt;Adrian Zacharieae: PhD student&lt;/li&gt;
&lt;li&gt;Barbara&lt;/li&gt;
&lt;li&gt;Paul  Buttkuss: another almost PhD student, codename Buttkiss aka Simon
*&lt;/li&gt;
&lt;/ul&gt;
</description>
    </item>
    
    <item>
      <title>Citizen data sovereignty is key to wearables and wellness data reuse for the common good</title>
      <link>https://synosys.github.io/publication/gilbert-2024-citizen/</link>
      <pubDate>Mon, 12 Feb 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/gilbert-2024-citizen/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Creed for Complexity</title>
      <link>https://synosys.github.io/unser_credo/</link>
      <pubDate>Sun, 28 Jan 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/unser_credo/</guid>
      <description></description>
    </item>
    
    <item>
      <title>News</title>
      <link>https://synosys.github.io/news_collection/</link>
      <pubDate>Sun, 28 Jan 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/news_collection/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Projects</title>
      <link>https://synosys.github.io/projects/</link>
      <pubDate>Sun, 28 Jan 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/projects/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Publications</title>
      <link>https://synosys.github.io/publications/</link>
      <pubDate>Sun, 28 Jan 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publications/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Research</title>
      <link>https://synosys.github.io/research/</link>
      <pubDate>Sun, 28 Jan 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/research/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Science</title>
      <link>https://synosys.github.io/science/</link>
      <pubDate>Sun, 28 Jan 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/science/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Complexity Explorables</title>
      <link>https://synosys.github.io/project/complexity_explorables/</link>
      <pubDate>Sun, 14 Jan 2024 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/project/complexity_explorables/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Rhythm of relationships in a social fish over the course of a full year in the wild</title>
      <link>https://synosys.github.io/publication/monk-2023-rhythm/</link>
      <pubDate>Sun, 24 Sep 2023 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/monk-2023-rhythm/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Evidence for positive long- and short-term effects of vaccinations against COVID-19 in wearable sensor metrics</title>
      <link>https://synosys.github.io/publication/wiedermann-2023-evidence/</link>
      <pubDate>Tue, 25 Jul 2023 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/wiedermann-2023-evidence/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Enhancing global preparedness during an ongoing pandemic from partial and noisy data</title>
      <link>https://synosys.github.io/publication/klamser-2023-enhancing/</link>
      <pubDate>Wed, 07 Jun 2023 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/klamser-2023-enhancing/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Modeling the impact of the Omicron infection wave in Germany</title>
      <link>https://synosys.github.io/publication/maier-2023-modeling/</link>
      <pubDate>Tue, 21 Mar 2023 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/maier-2023-modeling/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Estimating the share of SARS-CoV-2- immunologically naïve individuals in Germany up to June 2022</title>
      <link>https://synosys.github.io/publication/maier-2023-estimating/</link>
      <pubDate>Sun, 22 Jan 2023 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/maier-2023-estimating/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Reconstructing and predicting the spatial evolution of Carbapenemase-producing Enterobacteriaceae outbreaks</title>
      <link>https://synosys.github.io/publication/myall-2023-reconstructing/</link>
      <pubDate>Sun, 22 Jan 2023 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/myall-2023-reconstructing/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Directing smartphone use through the self-nudge app one sec</title>
      <link>https://synosys.github.io/publication/phillipp-2023-smartphone/</link>
      <pubDate>Thu, 05 Jan 2023 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/phillipp-2023-smartphone/</guid>
      <description></description>
    </item>
    
    <item>
      <title>From delta to omicron: The role of individual factors and social context in self-reported compliance with pandemic regulations and recommendations</title>
      <link>https://synosys.github.io/publication/sprengholz-2023-fromdelta/</link>
      <pubDate>Fri, 23 Dec 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/sprengholz-2023-fromdelta/</guid>
      <description></description>
    </item>
    
    <item>
      <title>New paper out: Understanding the impact of digital contact tracing during the COVID-19 pandemic</title>
      <link>https://synosys.github.io/old_news/dct-paper/</link>
      <pubDate>Tue, 06 Dec 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/dct-paper/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Understanding the impact of digital contact tracing during the COVID-19 pandemic</title>
      <link>https://synosys.github.io/publication/burdinski-2021-tracing/</link>
      <pubDate>Tue, 06 Dec 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/burdinski-2021-tracing/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Synchronization</title>
      <link>https://synosys.github.io/slides/ws-2022-complex-systems-lecture-3/</link>
      <pubDate>Mon, 14 Nov 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/slides/ws-2022-complex-systems-lecture-3/</guid>
      <description>&lt;h1 id=&#34;synchronization-phenomena&#34;&gt;Synchronization Phenomena&lt;/h1&gt;

&lt;figure class=&#34;r-stretch&#34;&gt;
    
        &lt;img src=&#34;huygens.png&#34; /&gt;
    
    
&lt;/figure&gt;

&lt;hr&gt;
</description>
    </item>
    
    <item>
      <title>A systematic review of worldwide causal and correlational evidence on digital media and democracy</title>
      <link>https://synosys.github.io/publication/phillipp-2022-systematic/</link>
      <pubDate>Mon, 07 Nov 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/phillipp-2022-systematic/</guid>
      <description></description>
    </item>
    
    <item>
      <title>New paper out: Germany’s fourth COVID-19 wave was mainly driven by the unvaccinated</title>
      <link>https://synosys.github.io/old_news/role_of_vaccinated/</link>
      <pubDate>Mon, 19 Sep 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/role_of_vaccinated/</guid>
      <description>&lt;p&gt;&lt;a href=&#34;https://www.nature.com/articles/s43856-022-00176-7&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;&lt;strong&gt;In this paper&lt;/strong&gt;&lt;/a&gt; we use a recently developed approach (the contribution matrix method) in combination with epidemiological data to estimate the contribution of vaccinated and unvaccinated populations to the current COVID-19 effective reproduction number &lt;em&gt;R&lt;/em&gt; in Germany. We find that a substantial fraction of reproduction is caused by unvaccinated individuals.&lt;/p&gt;
&lt;p&gt;A pdf of the paper is available &lt;a href=&#34;https://www.nature.com/articles/s43856-022-00176-7.pdf&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;&lt;strong&gt;here&lt;/strong&gt;&lt;/a&gt;.&lt;/p&gt;
&lt;h3 id=&#34;background&#34;&gt;Background&lt;/h3&gt;
&lt;p&gt;While the majority of the German population was fully vaccinated at the time (about 65%), COVID-19 incidence started growing exponentially in October 2021 with about 41% of recorded new cases aged twelve or above being symptomatic breakthrough infections, presumably also contributing to the dynamics. So far, it remained elusive how significant this contribution was and whether targeted non-pharmaceutical interventions (NPIs) may have stopped the amplification of the crisis.&lt;/p&gt;
&lt;h3 id=&#34;methods&#34;&gt;Methods&lt;/h3&gt;
&lt;p&gt;We develop and introduce a contribution matrix approach based on the next-generation matrix of a population-structured compartmental infectious disease model to derive contributions of respective inter- and intragroup infection pathways of unvaccinated and vaccinated subpopulations to the effective reproduction number and new infections, considering empirical data of vaccine efficacies against infection and transmission.&lt;/p&gt;
&lt;h3 id=&#34;results&#34;&gt;Results&lt;/h3&gt;
&lt;p&gt;Here we show that about 61%–76% of all new infections were caused by unvaccinated individuals and only 24%–39% were caused by the vaccinated. Furthermore, 32%–51% of new infections were likely caused by unvaccinated infecting other unvaccinated. Decreasing the transmissibility of the unvaccinated by, e. g. targeted NPIs, causes a steeper decrease in the effective reproduction number R than decreasing the transmissibility of vaccinated individuals, potentially leading to temporary epidemic control. Reducing contacts between vaccinated and unvaccinated individuals serves to decrease R in a similar manner as increasing vaccine uptake.&lt;/p&gt;
&lt;h3 id=&#34;conclusions&#34;&gt;Conclusions&lt;/h3&gt;
&lt;p&gt;A minority of the German population—the unvaccinated—is assumed to have caused the majority of new infections in the fall of 2021 in Germany. Our results highlight the importance of combined measures, such as vaccination campaigns and targeted contact reductions to achieve temporary epidemic control.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Podcast: Das neue Berlin</title>
      <link>https://synosys.github.io/old_news/das_neue_berlin/</link>
      <pubDate>Mon, 19 Sep 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/das_neue_berlin/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Germany’s fourth COVID-19 wave was mainly driven by the unvaccinated</title>
      <link>https://synosys.github.io/publication/maier-2022-germany/</link>
      <pubDate>Sun, 18 Sep 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/maier-2022-germany/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Dirk  @ Viertausendhertz Podcast</title>
      <link>https://synosys.github.io/old_news/dirk_4000hz/</link>
      <pubDate>Sun, 04 Sep 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/dirk_4000hz/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Enhancing global preparedness during an ongoing pandemic from partial and noisy data</title>
      <link>https://synosys.github.io/publication/klamser-2022-enhancing/</link>
      <pubDate>Fri, 19 Aug 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/klamser-2022-enhancing/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Preprint out: Enhancing global preparedness during an ongoing pandemic from partial and noisy data</title>
      <link>https://synosys.github.io/old_news/global_preparedness/</link>
      <pubDate>Fri, 19 Aug 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/global_preparedness/</guid>
      <description>&lt;p&gt;&lt;strong&gt;The preprint is available here:&lt;/strong&gt; &lt;a href=&#34;https://www.medrxiv.org/content/10.1101/2022.08.19.22278981v1&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;https://www.medrxiv.org/content/10.1101/2022.08.19.22278981v1&lt;/a&gt;.&lt;/p&gt;
&lt;hr&gt;
</description>
    </item>
    
    <item>
      <title>Is testing at home sufficient? Self tests and official statistics</title>
      <link>https://synosys.github.io/old_news/datenspende_blog_incidence/</link>
      <pubDate>Wed, 10 Aug 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/datenspende_blog_incidence/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Modeling the impact of the Omicron infection wave in Germany</title>
      <link>https://synosys.github.io/publication/maier-2022-modeling-the-impact/</link>
      <pubDate>Sun, 10 Jul 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/maier-2022-modeling-the-impact/</guid>
      <description></description>
    </item>
    
    <item>
      <title>New Preprint Out: Modeling the impact of the Omicron infection wave in Germany</title>
      <link>https://synosys.github.io/old_news/omicron/</link>
      <pubDate>Sun, 10 Jul 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/omicron/</guid>
      <description>&lt;h2 id=&#34;abstract&#34;&gt;Abstract&lt;/h2&gt;
&lt;p&gt;In November 2021, the first case of SARS-CoV-2 “variant of concern” (VOC) B.1.1.529 (“Omicron”) was reported in Germany, alongside global reports of reduced vaccine efficacy against infections with this variant. The potential threat posed by the rapid spread of this variant in Germany remained, at the time, elusive.
We developed a variant-dependent population-averaged susceptible-exposed-infected-recovered (SEIR) infectious disease model. The model was calibrated on the observed fixation dynamics of the Omicron variant in December 2021, and allowed us to estimate potential courses of upcoming infection waves in Germany, focusing on the corresponding burden on intensive care units (ICUs) and the efficacy of contact reduction strategies. A maximum median incidence of approximately 300 000 (50% PI in 1000: [181,454], 95% PI in 1000: [55,804]) reported cases per day was expected with the median peak occurring in the mid of February 2022, reaching a cumulative Omicron case count of 16.5 million (50% PI in mio: [11.4, 21.3], 95% PI in mio: [4.1, 27.9]) until Apr 1, 2022. These figures were in line with the actual Omicron waves that were subsequently observed in Germany with respective peaks occurring in mid February (peak: 191k daily new cases) and mid March (peak: 230k daily new cases), cumulatively infecting 14.8 million individuals during the study period. The model peak incidence was observed to be highly sensitive to variations in the assumed generation time and decreased with shorter generation time. Low contact reductions were expected to lead to containment. Early, strict, and short contact reductions could have led to a strong “rebound” effect with high incidences after the end of the respective non-pharmaceutical interventions. Higher vaccine uptake would have led to a lower outbreak size. To ensure that ICU occupancy remained below maximum capacity, a relative risk of requiring ICU care of 10%–20% was necessary (after infection with Omicron vs. infection with Delta).
We expected a large cumulative number of infections with the VOC Omicron in Germany with ICU occupancy likely remaining below capacity nevertheless, even without additional non-pharmaceutical interventions. Our estimates were in line with the retrospectively observed waves. The results presented here informed legislation in Germany. The methodology developed in this study might be used to estimate the impact of future waves of COVID-19 or other infectious diseases.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Dirk  @ re:publica 22</title>
      <link>https://synosys.github.io/old_news/dirk_re_publica/</link>
      <pubDate>Fri, 10 Jun 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/dirk_re_publica/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Podcast: Let&#39;s talk change</title>
      <link>https://synosys.github.io/old_news/lets_talk_change/</link>
      <pubDate>Fri, 22 Apr 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/lets_talk_change/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Estimating the distribution of COVID-19-susceptible, -recovered, and -vaccinated individuals in Germany up to April 2022</title>
      <link>https://synosys.github.io/publication/maier-2022-sero/</link>
      <pubDate>Tue, 19 Apr 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/maier-2022-sero/</guid>
      <description></description>
    </item>
    
    <item>
      <title>New preprint: Estimating the distribution of COVID-19-susceptible, -recovered, and -vaccinated individuals in Germany up to April 2022</title>
      <link>https://synosys.github.io/old_news/sero_preprint/</link>
      <pubDate>Tue, 19 Apr 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/sero_preprint/</guid>
      <description>&lt;p&gt;&lt;strong&gt;The preprint is available here:&lt;/strong&gt; &lt;a href=&#34;https://www.medrxiv.org/content/10.1101/2022.04.19.22274030v1&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;https://www.medrxiv.org/content/10.1101/2022.04.19.22274030v1&lt;/a&gt;.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Evidence for positive long- and short-term effects of vaccinations against COVID-19 in wearable sensor metrics -- Insights from the German Corona Data Donation Project</title>
      <link>https://synosys.github.io/publication/wiedermann-2022-long_covid/</link>
      <pubDate>Wed, 06 Apr 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/wiedermann-2022-long_covid/</guid>
      <description></description>
    </item>
    
    <item>
      <title>New preprint: Evidence for positive long- and short-term effects of vaccinations against COVID-19 in wearable sensor metrics -- Insights from the German Corona Data Donation Project</title>
      <link>https://synosys.github.io/old_news/long_covid_preprint/</link>
      <pubDate>Wed, 06 Apr 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/long_covid_preprint/</guid>
      <description>&lt;p&gt;In this paper we use physiological data collected in the &lt;a href=&#34;https://corona-datenspende.de/science/en/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;German Data Donation Project (Datenspende)&lt;/a&gt; to investigate the differential impact of vaccination on the effect that a COVID-19 infection has on &lt;strong&gt;resting heart rate&lt;/strong&gt;, &lt;strong&gt;physical activity&lt;/strong&gt; and &lt;strong&gt;sleep&lt;/strong&gt;.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;The preprint is available here:&lt;/strong&gt; &lt;a href=&#34;https://arxiv.org/abs/2204.02846&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;https://arxiv.org/abs/2204.02846&lt;/a&gt;.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>New preprint: Rhythm of relationships in a social fish over the course of a full year in the wild</title>
      <link>https://synosys.github.io/old_news/aslak-rhythm-preprint/</link>
      <pubDate>Mon, 14 Mar 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/aslak-rhythm-preprint/</guid>
      <description>&lt;p&gt;&lt;strong&gt;The preprint is available here:&lt;/strong&gt; &lt;a href=&#34;https://www.biorxiv.org/content/10.1101/2022.03.11.483964v1&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;https://www.biorxiv.org/content/10.1101/2022.03.11.483964v1&lt;/a&gt;.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Rhythm of relationships in a social fish over the course of a full year in the wild</title>
      <link>https://synosys.github.io/publication/aslak-2022-preprint/</link>
      <pubDate>Mon, 14 Mar 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/aslak-2022-preprint/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Abschätzung der Infektionswelle durch die SARS-CoV-2 VOC Omikron</title>
      <link>https://synosys.github.io/publication/maier-2022-omicron/</link>
      <pubDate>Thu, 03 Feb 2022 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/maier-2022-omicron/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Biases in human mobility data impact epidemic modeling</title>
      <link>https://synosys.github.io/publication/schlosser-2021-bias/</link>
      <pubDate>Thu, 23 Dec 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/schlosser-2021-bias/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Die Pandemie wird durch fehlenden Impfschutz getrieben: Was ist zu tun?</title>
      <link>https://synosys.github.io/publication/maier-2021-pandemie/</link>
      <pubDate>Thu, 25 Nov 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/maier-2021-pandemie/</guid>
      <description>&lt;h2 id=&#34;wichtig&#34;&gt;&lt;strong&gt;Wichtig:&lt;/strong&gt;&lt;/h2&gt;
&lt;blockquote&gt;
&lt;p&gt;Die hier beschriebenen wissenschaftlichen Ergebnisse beziehen sich auf die &lt;strong&gt;B.1.617.2&lt;/strong&gt; SARS-CoV-2 Variante (&lt;em&gt;&amp;ldquo;Delta&amp;rdquo;&lt;/em&gt;) und das Infektionsgeschehen Mitte November in Deutschland. Die Ergebnisse spiegeln nicht die Situation der Omicron-Welle (SARS-CoV-2-Variante &lt;strong&gt;B.1.1.529&lt;/strong&gt;) Anfang 2022 wieder.&lt;/p&gt;
&lt;/blockquote&gt;
&lt;hr&gt;
&lt;hr&gt;
&lt;p&gt;Impfungen gehören du den wirksamsten Mitteln der Pandemiebekämpfung. Trotzdem sind die Impfraten im &lt;a href=&#34;https://ourworldindata.org/covid-vaccinations?country=OWID_WRL&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;deutschsprachigen Europa&lt;/a&gt; aktuell viel zu niedrig für eine effektive Eindämmung des Virus. In Deutschland waren am 22.11.2021 68% doppelt geimpft, obwohl das &lt;a href=&#34;https://www.google.com/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=&amp;amp;cad=rja&amp;amp;uact=8&amp;amp;ved=2ahUKEwjH84v28rD0AhWJzKQKHfsXCJUQFnoECAUQAQ&amp;amp;url=https://www.rki.de/DE/Content/Infekt/EpidBull/Archiv/2021/27/Art_01.html&amp;amp;usg=AOvVaw0G9DqJ7ijemGlZYRwXrZbW&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;RKI&lt;/a&gt; bereits im Juli 2021 empfahl, 85% der 12-59-Jährigen und 90% der Über-60-Jährigen zu impfen. Ähnlich schlecht sieht es in Österreich (65% vollständig geimpft) und der Schweiz (66%) aus. Auffrischungsimpfungen (Booster) zur Erhöhung der Impfeffektivität sind seit November in Deutschland &lt;a href=&#34;https://www.rki.de/DE/Content/Kommissionen/STIKO/Empfehlungen/PM_2021-11-18.html&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;empfohlen&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;Neue Berechnungen auf der Basis eines mathematischen Ansteckungsmodells &lt;sup id=&#34;fnref:1&#34;&gt;&lt;a href=&#34;#fn:1&#34; class=&#34;footnote-ref&#34; role=&#34;doc-noteref&#34;&gt;1&lt;/a&gt;&lt;/sup&gt; zeigen, dass eine effektive Erhöhung der Impfquote jedoch dringend notwendig ist: bei 8 bis 9 von 10 Ansteckungen mit COVID-19 ist mindestens eine ungeimpfte Person beteiligt. Das folgende Beispiel illustriert, dass aktuell der Großteil der Neuinfektionen durch fehlenden Impfschutz verursacht wird, obwohl ungeimpfte Personen nur ca. 32% der Bevölkerung ausmachen.&lt;/p&gt;














&lt;figure  id=&#34;figure-abb1&#34;&gt;
  &lt;div class=&#34;d-flex justify-content-center&#34;&gt;
    &lt;div class=&#34;w-100&#34; &gt;&lt;img alt=&#34;&#34; srcset=&#34;
               /publication/maier-2021-pandemie/abb1_hue3d634736fd43df10aac553ccb3f4083_157779_ccdfe103bc690c00c7c2fc9987d18c85.jpeg 400w,
               /publication/maier-2021-pandemie/abb1_hue3d634736fd43df10aac553ccb3f4083_157779_ad3a9845a07c9004e239652e5336d0a3.jpeg 760w,
               /publication/maier-2021-pandemie/abb1_hue3d634736fd43df10aac553ccb3f4083_157779_1200x1200_fit_q75_lanczos.jpeg 1200w&#34;
               src=&#34;https://synosys.github.io/publication/maier-2021-pandemie/abb1_hue3d634736fd43df10aac553ccb3f4083_157779_ccdfe103bc690c00c7c2fc9987d18c85.jpeg&#34;
               width=&#34;760&#34;
               height=&#34;570&#34;
               loading=&#34;lazy&#34; data-zoomable /&gt;&lt;/div&gt;
  &lt;/div&gt;&lt;/figure&gt;
&lt;p&gt;Die Abbildung zeigt, wie ungeimpfte und geimpfte Personen anteilig an der Verbreitung von COVID-19 beteiligt sind (laut Modellschätzungen). Man stelle sich eine Gruppe von Infizierten vor. Angenommen, diese Gruppe hat 100 Ansteckungen verursacht. In der oberen Grafik (A) wird eine Impfeffektivität von 72% bei Erwachsenen und 92% bei Kindern und
Jugendlichen angenommen. Dann sehen die 100 Ansteckungen folgendermaßen aus: 51
von den 100 Angesteckten sind Ungeimpfte, die von anderen Ungeimpften infiziert wurden. 25 sind Geimpfte, die von Ungeimpften infiziert wurden. Es wurden also 76 Personen von Ungeimpften angesteckt. Der Anteil der Leute, der durch Geimpfte angesteckt wird, ist erheblich kleiner: 15 Angesteckte sind Ungeimpfte, die von Geimpften angesteckt wurden und 9 sind Geimpfte, die von Geimpften angesteckt wurden. Es wurden also lediglich 24 Personen von Geimpften angesteckt. 91 von 100 Ansteckungen entstehen mit Beteiligung mindestens einer Person ohne Impfschutz.&lt;/p&gt;














&lt;figure  id=&#34;figure-abb2&#34;&gt;
  &lt;div class=&#34;d-flex justify-content-center&#34;&gt;
    &lt;div class=&#34;w-100&#34; &gt;&lt;img alt=&#34;&#34; srcset=&#34;
               /publication/maier-2021-pandemie/abb2_hucbf4ebb6e9f66eebdac69406ef95838f_156632_808930b7bcafa70c4f5a142c5d509e80.jpeg 400w,
               /publication/maier-2021-pandemie/abb2_hucbf4ebb6e9f66eebdac69406ef95838f_156632_76d6c27b8cacea6619e2388996f3b37f.jpeg 760w,
               /publication/maier-2021-pandemie/abb2_hucbf4ebb6e9f66eebdac69406ef95838f_156632_1200x1200_fit_q75_lanczos.jpeg 1200w&#34;
               src=&#34;https://synosys.github.io/publication/maier-2021-pandemie/abb2_hucbf4ebb6e9f66eebdac69406ef95838f_156632_808930b7bcafa70c4f5a142c5d509e80.jpeg&#34;
               width=&#34;760&#34;
               height=&#34;570&#34;
               loading=&#34;lazy&#34; data-zoomable /&gt;&lt;/div&gt;
  &lt;/div&gt;&lt;/figure&gt;
&lt;p&gt;Um zu verstehen, ob neue Ansteckungen auch durch fehlenden Impfschutz getrieben werden, wenn die Impfung nur eine geringe Effektivität hat, wurde in einem zweiten Beispiel eine geringere Impfeffektivität angenommen &lt;sup id=&#34;fnref:2&#34;&gt;&lt;a href=&#34;#fn:2&#34; class=&#34;footnote-ref&#34; role=&#34;doc-noteref&#34;&gt;2&lt;/a&gt;&lt;/sup&gt;. In der unteren Grafik wird eine Impfeffektivität von 50% bei über 60ig-Jährigen, 60% bei Erwachsenen bis 59 Jahren und 60% bei Kindern und Jugendlichen bis 17 Jahren angenommen. Dann sehen die 100 Infektionen folgendermaßen aus: 38 von den 100 Angesteckten sind Ungeimpfte, die von anderen Ungeimpften infiziert wurden. 29 sind Geimpfte, die von Ungeimpften infiziert wurden. Es wurden also 67 Personen von Ungeimpften angesteckt. Der Anteil der Leute, der durch Geimpfte angesteckt wird, ist erheblich kleiner: 17 Angesteckte sind Ungeimpfte, die von Geimpften angesteckt wurden und 16 sind Geimpfte, die von Geimpften angesteckt wurden. Es wurden also lediglich 33 Neuinfizierte von Geimpften angesteckt. 84 von 100 Ansteckungen entstehen mit Beteiligung mindestens einer Person ohne Impfschutz.&lt;/p&gt;
&lt;h2 id=&#34;daraus-ergibt-sich&#34;&gt;Daraus ergibt sich:&lt;/h2&gt;
&lt;ol&gt;
&lt;li&gt;An 8 bis 9 von 10 Ansteckungen sind Personen ohne Impfschutz beteiligt.&lt;/li&gt;
&lt;li&gt;Je effektiver der Impfschutz, desto weniger Ansteckungen werden durch geimpfte Personen verursacht.&lt;/li&gt;
&lt;/ol&gt;
&lt;blockquote&gt;
&lt;p&gt;&lt;strong&gt;Die Dynamik der Pandemie wird aktuell also stark durch fehlenden oder nachlassenden, weniger effektiven Impfschutz getrieben.&lt;/strong&gt;&lt;/p&gt;
&lt;/blockquote&gt;
&lt;blockquote&gt;
&lt;h2 id=&#34;daher-empfiehlt-sich&#34;&gt;Daher empfiehlt sich:&lt;/h2&gt;
&lt;ol&gt;
&lt;li&gt;Impfquote effektiv erhöhen.&lt;/li&gt;
&lt;li&gt;Effektivität des Impfschutzes erhöhen, z.B. durch Boostern.&lt;/li&gt;
&lt;li&gt;Kontakte reduzieren, v.a. zwischen ungeimpften Personen.&lt;/li&gt;
&lt;/ol&gt;
&lt;/blockquote&gt;
&lt;p&gt;Ohne zusätzliche Maßnahmen ist keine baldige Besserung der Impfquote zu erwarten, da die Zuwachsrate der Impfkampagne seit Juni 2021 &lt;a href=&#34;http://impfdashboard.de/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;stagniert&lt;/a&gt;. Für zusätzliche Maßnahmen zur Steigerung der Impfquote besteht die Herausforderung, dass ein Großteil der ungeimpften Personen schwer erreichbar ist - z.B. laut der &lt;a href=&#34;https://projekte.uni-erfurt.de/cosmo2020/web/summary/56/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;COSMO-Studie&lt;/a&gt; sind 30% der Ungeimpften zögerlich, 54% sagen, sie wollen sich auf keinen Fall impfen lassen (Stand November 2021). Vor- und Nachteile eine Impfpflicht sollten &lt;a href=&#34;https://psyarxiv.com/pmx3w&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;diskutiert werden&lt;/a&gt;. Maßnahmen, die schnell die Kontakte reduzieren, sollten zum Brechen der 4. Welle implementiert werden, da selbst ein schneller Anstieg der Impfrate erst mittelfristig weitere Wellen verhindern, nicht aber die aktuelle Welle brechen kann.&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;
&lt;p&gt;&lt;strong&gt;Kontakt:&lt;/strong&gt; &lt;a href=&#34;mailto:bfmaier@physik.hu-berlin.de&#34;&gt;bfmaier@physik.hu-berlin.de&lt;/a&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li&gt;
&lt;p&gt;&lt;strong&gt;Preprint:&lt;/strong&gt; &lt;a href=&#34;http://localhost:1313/publication/maier-2021-germany/maier-2021-germany.pdf&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Germany’s current COVID-19 crisis is mainly driven by the unvaccinated&lt;/a&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;div class=&#34;footnotes&#34; role=&#34;doc-endnotes&#34;&gt;
&lt;hr&gt;
&lt;ol&gt;
&lt;li id=&#34;fn:1&#34;&gt;
&lt;p&gt;Das theoretische Framework hinter diesen Berechnungen heisst Contribution Matrix. Die Berechnungen sind detailliert hier beschrieben: &lt;a href=&#34;https://rocs.hu-berlin.de/publication/maier-2021-germany/maier-2021-germany.pdf&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;https://rocs.hu-berlin.de/publication/maier-2021- germany/maier-2021-germany.pdf&lt;/a&gt;&amp;#160;&lt;a href=&#34;#fnref:1&#34; class=&#34;footnote-backref&#34; role=&#34;doc-backlink&#34;&gt;&amp;#x21a9;&amp;#xfe0e;&lt;/a&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li id=&#34;fn:2&#34;&gt;
&lt;p&gt;Beide Szenarien gehen von folgenden aktuellen Impfraten pro Altersgruppe aus: 0-11 = 0%, 12-17 = 40%, 18-59 = 72%, 60+ = 85%. Über die tatsächliche Impfeffektivität besteht wissenschaftliche Unsicherheit. Daher werden zwei verschiedene Szenarien angenommen. In Szenario A wird eine hohe Impfeffektivität von 92% bei Kindern und 72% bei Erwachsenen angenommen. Dies entspricht den Effektivitäten der Periode Oktober 2021 für symptomatisches COVID-19 (&lt;a href=&#34;https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Situationsberichte/Gesamt.html&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;RKI Situationsbericht vom 11.11.2021&lt;/a&gt;). Im Modell werden jedoch Impfeffektivitäten gegen Infektion angenommen, die üblicherweise geringer sind als gegen symptomatisches COVID-19. Diese Effektivitätsschätzung ist daher eine optimistische Schätzung und dient dies als mögliche Obergrenze. Die niedrigeren Impfeffktivitäten gegen Infektion (Szenario B) sind in Übereinstimmung mit Daten aus Großbritannien gewählt (50% bei über 60ig-Jährigen, 60% bei Erwachsenen bis 59 Jahren und 60% bei Kindern und Jugendlichen bis 17 Jahren). In &lt;a href=&#34;https://www.science.org/doi/10.1126/science.abm0620&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Großbritannien&lt;/a&gt; wurde vorwiegend mit AstraZeneca geimpft. Dieser Impfstoff gilt in der Literatur als weniger effektiv. Daher dienen diese Werte im Modell als Schätzung für niedrige Impfeffektivität gegen Infektion. Die Ansteckungs-Zahlen beschreiben die geschätzten Anteile der Infektionswege an der totalen effektiven Reproduktionszahl (Reff). Zum Zeitpunkt dieser Berechnungen lag R etwa bei &lt;a href=&#34;https://github.com/robert-koch-institut/SARS-CoV-2-Nowcasting_und_-R-Schaetzung/blob/main/Nowcast_R_aktuell.csv&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;1,2&lt;/a&gt;.&amp;#160;&lt;a href=&#34;#fnref:2&#34; class=&#34;footnote-backref&#34; role=&#34;doc-backlink&#34;&gt;&amp;#x21a9;&amp;#xfe0e;&lt;/a&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;/ol&gt;
&lt;/div&gt;
</description>
    </item>
    
    <item>
      <title>Finding disease outbreak locations from human mobility data</title>
      <link>https://synosys.github.io/publication/schlosser-2021-finding/</link>
      <pubDate>Tue, 19 Oct 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/schlosser-2021-finding/</guid>
      <description></description>
    </item>
    
    <item>
      <title>New paper out: Finding disease outbreak locations from human mobility data</title>
      <link>https://synosys.github.io/old_news/outbreak_detection/</link>
      <pubDate>Tue, 19 Oct 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/outbreak_detection/</guid>
      <description>&lt;p&gt;In this paper we investigated how much information on individual mobility patterns is required
to reconstruct the origin of an outbreak from spatially distributed cases.&lt;/p&gt;
&lt;p&gt;It turns out: not so much.&lt;/p&gt;
&lt;p&gt;The paper is available &lt;a href=&#34;https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-021-00306-6#citeas&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;&lt;strong&gt;here&lt;/strong&gt;&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Here&amp;rsquo;s the abstract:&lt;/strong&gt;&lt;/p&gt;
&lt;hr&gt;
&lt;p&gt;Finding the origin location of an infectious disease outbreak quickly is crucial in
mitigating its further dissemination. Current methods to identify outbreak locations
early on rely on interviewing affected individuals and correlating their movements,
which is a manual, time-consuming, and error-prone process. Other methods such as
contact tracing, genomic sequencing or theoretical models of epidemic spread offer
help, but they are not applicable at the onset of an outbreak as they require highly
processed information or established transmission chains. Digital data sources such as
mobile phones offer new ways to find outbreak sources in an automated way. Here,
we propose a novel method to determine outbreak origins from geolocated
movement data of individuals affected by the outbreak. Our algorithm scans
movement trajectories for shared locations and identifies the outbreak origin as the
most dominant among them. We test the method using various empirical and
synthetic datasets, and demonstrate that it is able to single out the true outbreak
location with high accuracy, requiring only data of N = 4 individuals. The method can
be applied to scenarios with multiple outbreak locations, and is even able to estimate
the number of outbreak sources if unknown, while being robust to noise. Our
method is the first to offer a reliable, accurate out-of-the-box approach to identify
outbreak locations in the initial phase of an outbreak. It can be easily and quickly
applied in a crisis situation, improving on previous manual approaches. The method is
not only applicable in the context of disease outbreaks, but can be used to find
shared locations in movement data in other contexts as well.&lt;/p&gt;
&lt;hr&gt;
</description>
    </item>
    
    <item>
      <title>New Monitor: Contact patterns during the Pandemic</title>
      <link>https://synosys.github.io/old_news/contact_monitor/</link>
      <pubDate>Tue, 05 Oct 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/contact_monitor/</guid>
      <description>&lt;p&gt;In addition to mobility, we now show the average contacts in the population over time—with equally important insights into the course of the pandemic.&lt;/p&gt;
&lt;p&gt;The mobility of people gives us an important overview of the pandemic, but it is actually the contacts between people that are instrumental in the infection dynamics: Transmissions occur when people meet.&lt;/p&gt;
&lt;p&gt;With the new &lt;a href=&#34;https://www.covid-19-mobility.org/contact-index/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;&lt;strong&gt;contact monitor&lt;/strong&gt;&lt;/a&gt; we make these contacts visible. The monitor shows the average number of contacts that a person in Germany had on a given day.&lt;/p&gt;
&lt;p&gt;You can clearly see how strongly contacts have decreased in March 2020, and how they are still at a low level more than a year after the start of the pandemic. Also visible are the different phases of the pandemic and reactions to it with lockdowns, relaxations, holidays and vacations.&lt;/p&gt;
&lt;p&gt;The number of contacts is calculated from GPS mobile phone data and provided to us as an anonymous, aggregated statistic - &lt;a href=&#34;https://www.covid-19-mobility.org/data-info/contacts/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;more on the methodology here&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;We will continue to analyze contact dynamics and investigate what conclusions can be drawn about infection dynamics. For example, in the study &lt;a href=&#34;https://www.pnas.org/content/118/31/e2026731118&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Ruediger et al.&lt;/a&gt;, contacts were shown to be related to the reproductive number R.&lt;/p&gt;
&lt;hr&gt;
</description>
    </item>
    
    <item>
      <title>Dirk&#39;s book is out</title>
      <link>https://synosys.github.io/old_news/dirks_book_out/</link>
      <pubDate>Fri, 17 Sep 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/dirks_book_out/</guid>
      <description>&lt;p&gt;Finally, &lt;strong&gt;&amp;ldquo;Im Wald vor lauter Bäumen - unsere komplexe Welt besser verstehen&amp;rdquo;&lt;/strong&gt; is now out and available.&lt;/p&gt;
&lt;p&gt;This popular scientific book explains the science of Complexity, how it works, what anti-disciplinary science looks like and what benefits are gained by connecting different phenomena observed in traditionally distant scientific disciplines.&lt;/p&gt;
&lt;p&gt;The book captures topics such as:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;complex networks&lt;/li&gt;
&lt;li&gt;synchronization&lt;/li&gt;
&lt;li&gt;critical phenomena&lt;/li&gt;
&lt;li&gt;tipping points&lt;/li&gt;
&lt;li&gt;the emergence of cooperation&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;and illustrates the connections between these phenomena using various examples from biology, ecology and social science.&lt;/p&gt;
&lt;p&gt;The book is in German.&lt;/p&gt;
&lt;p&gt;More information, also in German is available &lt;a href=&#34;https://www.dtv.de/buch/dirk-brockmann-im-wald-vor-lauter-baeumen-28299/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;&lt;strong&gt;here&lt;/strong&gt;&lt;/a&gt;.&lt;/p&gt;
&lt;hr&gt;
</description>
    </item>
    
    <item>
      <title>Global Mobility Visualization</title>
      <link>https://synosys.github.io/project/viz-gat/</link>
      <pubDate>Tue, 10 Aug 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/project/viz-gat/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Boosting people’s ability to detect microtargeted advertising</title>
      <link>https://synosys.github.io/publication/phillipp-2021-boosting/</link>
      <pubDate>Fri, 30 Jul 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/phillipp-2021-boosting/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Preprint out: Digital contact tracing contributes little to COVID-19 outbreak containment</title>
      <link>https://synosys.github.io/old_news/tracing_paper/</link>
      <pubDate>Tue, 29 Jun 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/tracing_paper/</guid>
      <description>&lt;p&gt;Our preprint &amp;ldquo;Digital contact tracing contributes little to COVID-19 outbreak containment&amp;rdquo; is out and available.&lt;/p&gt;
&lt;p&gt;As digital contact tracing apps raised the hope that they may be efficient enough to contain outbreaks
without falling back to harsher NPIs, we analyzed its success. Relying on empirical results, we find
an expected outbreak size reduction on the order of a few percent at best. While the intervention
may aid the manual tracing process, we argue that countries should concentrate on other means to contain
COVID-19 outbreaks.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Here&amp;rsquo;s the abstract:&lt;/strong&gt;&lt;/p&gt;
&lt;hr&gt;
&lt;p&gt;Digital contact tracing applications have been introduced in many countries to aid in the containment of
COVID-19 outbreaks. Initially, enthusiasm was high regarding their implementation as a non-pharmaceutical
intervention (NPI). Yet, no country was able to prevent larger outbreaks without falling back to harsher NPIs,
and the total effect of digital contact tracing remains elusive. Based on the results of empirical studies and
modeling efforts, we show that digital contact tracing apps might have prevented cases on the order of single-digit
percentages up until now, at best. We show that this poor impact can be attributed to a combination of low
participation rates, a non-flexible reliance on symptom-based testing, low engagement of participants, and
delays between testing and test result upload. We find that contact tracing does not change the epidemic
threshold and exclusively prevents more cases during the supercritical phase of an epidemic, making it unfit
as a tool to prevent outbreaks. Locally clustered contact structures may increase the intervention’s efficacy,
but only if the number of contacts per individual is homogeneously distributed, a condition usually not found in
contact networks.  Our results suggest that policy makers cannot rely on digital contact tracing to contain
outbreaks of COVID-19 or similar diseases.&lt;/p&gt;
&lt;hr&gt;
</description>
    </item>
    
    <item>
      <title>The ROCS-Team welcomes new members!</title>
      <link>https://synosys.github.io/old_news/welcome_new_members/</link>
      <pubDate>Tue, 29 Jun 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/welcome_new_members/</guid>
      <description>&lt;p&gt;Our team keeps growing!&lt;/p&gt;
&lt;p&gt;We warmly welcome:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Adrian Pelcaru&lt;/li&gt;
&lt;li&gt;Paul Buttkus&lt;/li&gt;
&lt;li&gt;Tashin Talbot&lt;/li&gt;
&lt;li&gt;Marc Wiedemann&lt;/li&gt;
&lt;li&gt;Jakob Kolb&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;We are very happy that they are on board. Welcome!&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Digital contact tracing contributes little to COVID-19 outbreak containment</title>
      <link>https://synosys.github.io/publication/burdinski-2021-tracing-preprint/</link>
      <pubDate>Fri, 25 Jun 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/burdinski-2021-tracing-preprint/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Dirk  @ Jung &amp; Naiv - Folge 503</title>
      <link>https://synosys.github.io/old_news/dirk_jung_und_naiv/</link>
      <pubDate>Thu, 25 Mar 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/dirk_jung_und_naiv/</guid>
      <description></description>
    </item>
    
    <item>
      <title>A new COVID-19 mobility monitor report ist online</title>
      <link>https://synosys.github.io/old_news/mobility_between_counties/</link>
      <pubDate>Wed, 03 Mar 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/mobility_between_counties/</guid>
      <description>&lt;p&gt;You can find the whole report here: &lt;a href=&#34;https://www.covid-19-mobility.org/reports/traffic-grid/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;** -&amp;gt; Traffic Between Adjacent Counties &amp;lt;- **&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;The report can be &lt;a href=&#34;https://www.covid-19-mobility.org/reports/adjacent-travel/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;found on the covid-19-mobility monitor site&lt;/a&gt; but is also available as an &lt;a href=&#34;https://observablehq.com/@adrianzachariae/traffic-lattice-germany&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Observable notebook&lt;/a&gt;.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Potential benefits of delaying the second mRNA COVID-19 vaccine dose</title>
      <link>https://synosys.github.io/publication/maier-2021-potential/</link>
      <pubDate>Mon, 01 Mar 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/maier-2021-potential/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Preprint out: Potential benefits of delaying the second mRNA COVID-19 vaccine dose</title>
      <link>https://synosys.github.io/old_news/vaccination_paper/</link>
      <pubDate>Mon, 01 Mar 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/vaccination_paper/</guid>
      <description>&lt;p&gt;We are happy that our preprint &lt;a href=&#34;https://arxiv.org/pdf/2102.13600.pdf&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;&lt;strong&gt;Potential benefits of delaying the second mRNA COVID-19 vaccine dose&lt;/strong&gt;&lt;/a&gt; is out and available on &lt;em&gt;arxiv.org&lt;/em&gt;.&lt;/p&gt;
&lt;p&gt;This has been a very constructive and effective collaborative effort, lead by our &lt;a href=&#34;https://synosys.github.io/author/dr.-benjamin-f.-maier/&#34;&gt;Ben Maier&lt;/a&gt;, who did all the heavy lifting, and valuable contributions by the teams of Cornelia Betsch, Michael Meyer-Hermann and by Karl Lauterbach.&lt;/p&gt;
&lt;p&gt;In a nutshell we evaluate and compare different vaccination strageties of COVID-19 mRNA-vaccines in high-risk groups in Germany and show that a flexible scheduling of the second vaccine dose can potentially be the way to go. If you are interested in the details, have a look at the manuscript.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Here&amp;rsquo;s the abstract:&lt;/strong&gt;&lt;/p&gt;
&lt;hr&gt;
&lt;p&gt;Vaccination against COVID-19 with the recently approved mRNA vaccines BNT162b2 (BioNTech/Pfizer) and mRNA-1273 (Moderna) is currently underway in a large number of countries. However, high incidence rates and rapidly spreading SARS-CoV-2 variants are concerning. In combination with acute supply deficits in Europe in early 2021, the question arises of whether stretching the vaccine, for instance by delaying the second dose, can make a significant contribution to preventing deaths, despite associated risks such as lower vaccine efficacy, the potential emergence of escape mutants, enhancement, waning immunity, reduced social acceptance of off-label vaccination, and liability shifts. A quantitative epidemiological assessment of risks and benefits of non-standard vaccination protocols remains elusive. To clarify the situation and to provide a quantitative epidemiological foundation we develop a stochastic epidemiological model that integrates specific vaccine rollout protocols into a risk-group structured infectious disease dynamical model. Using the situation and conditions in Germany as a reference system, we show that delaying the second vaccine dose is expected to prevent deaths in the four to five digit range, should the incidence resurge. We show that this considerable public health benefit relies on the fact that both mRNA vaccines provide substantial protection against severe COVID-19 and death beginning 12 to 14 days after the first dose. The benefits of protocol change are attenuated should vaccine compliance decrease substantially. To quantify the impact of protocol change on vaccination adherence we performed a large-scale online survey. We find that, in Germany, changing vaccination protocols may lead to small reductions in vaccination intention. In sum, we therefore expect the benefits of a strategy change to remain substantial and stable.&lt;/p&gt;
&lt;hr&gt;
</description>
    </item>
    
    <item>
      <title>Just published: Social networks predict the life and death of honey bees</title>
      <link>https://synosys.github.io/old_news/bee_paper/</link>
      <pubDate>Wed, 17 Feb 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/bee_paper/</guid>
      <description>






  
    


&lt;div class=&#34;pub-list-item&#34; style=&#34;margin-bottom: 1rem&#34;&gt;
  &lt;i class=&#34;far fa-file-alt pub-icon&#34; aria-hidden=&#34;true&#34;&gt;&lt;/i&gt;

   
  &lt;a href=&#34;https://synosys.github.io/publication/wild-2020-social/&#34;&gt;Social networks predict the life and death of honey bees&lt;/a&gt;.&lt;br /&gt;
  &lt;span class=&#34;article-metadata li-cite-author&#34;&gt;
    
  &lt;span &gt;
  B Wild&lt;/span&gt;, &lt;span &gt;
  D M Dormagen&lt;/span&gt;, &lt;span &gt;
  &lt;a href=&#34;https://synosys.github.io/authors/adrian/&#34;&gt;A Zachariae&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
  M L Smith&lt;/span&gt;, &lt;span &gt;
  K S Traynor&lt;/span&gt;, &lt;span &gt;
  &lt;a href=&#34;https://synosys.github.io/authors/dirk/&#34;&gt;D Brockmann&lt;/a&gt;&lt;/span&gt;, &lt;span &gt;
  I D Couzin&lt;/span&gt;, &lt;span &gt;
  T Landgraf&lt;/span&gt; &lt;/span
  &gt;&lt;br /&gt;
   Nature Communications 12, 1110
   (2021).
  &lt;p&gt;








  
    
  



&lt;a class=&#34;btn btn-outline-primary btn-page-header btn-sm&#34; href=&#34;https://www.nature.com/articles/s41467-021-21212-5.pdf&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;
  PDF
&lt;/a&gt;



&lt;a href=&#34;#&#34; class=&#34;btn btn-outline-primary btn-page-header btn-sm js-cite-modal&#34;
        data-filename=&#34;/publication/wild-2020-social/cite.bib&#34;&gt;
  Cite
&lt;/a&gt;













&lt;a class=&#34;btn btn-outline-primary btn-page-header btn-sm&#34; href=&#34;https://doi.org/https://doi.org/10.1038/s41467-021-21212-5&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;
  DOI
&lt;/a&gt;



  
  
  
    
  
  
  
  
  
    
  
  &lt;a class=&#34;btn btn-outline-primary btn-page-header btn-sm&#34; href=&#34;https://www.nature.com/articles/s41467-021-21212-5#citeas&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;
    View&lt;/a&gt;
  
&lt;/p&gt;
  
&lt;/div&gt;

  


</description>
    </item>
    
    <item>
      <title>Social networks predict the life and death of honey bees</title>
      <link>https://synosys.github.io/publication/wild-2020-social/</link>
      <pubDate>Wed, 17 Feb 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/wild-2020-social/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Pascal Klamser joins the ROCS-Team - Welcome Pascal</title>
      <link>https://synosys.github.io/old_news/welcom_pascal/</link>
      <pubDate>Sat, 13 Feb 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/welcom_pascal/</guid>
      <description>&lt;div align=&#34;justify&#34;&gt;
Pascal is a physicist by training with a PhD (almost) in biophysics and an interdisciplinary interest with a focus on multi-agent systems. How simple interaction rules shape the large scale dynamics of a system is the unifying question behind diverse projects as predator avoidance in collective behavior and opinion dynamics on adaptive networks. He joined our team during the Corona pandemic in March 2021.
&lt;p&gt;In our team Pascal will expand our COVID-19 modeling efforts. He will also dive into the data analytics projects.&lt;/p&gt;
&lt;p&gt;We are very happy that Pascal will be joining us soon.&lt;/p&gt;
&lt;/div&gt;
</description>
    </item>
    
    <item>
      <title>A new COVID-19 mobility monitor report ist online</title>
      <link>https://synosys.github.io/old_news/new_mobility_report/</link>
      <pubDate>Wed, 10 Feb 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/new_mobility_report/</guid>
      <description>&lt;p&gt;You can find the whole report here: &lt;a href=&#34;https://www.covid-19-mobility.org/reports/adjacent-travel/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;** -&amp;gt; Traffic in the Pandemic by Distance &amp;lt;- **&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;In past reports we have shown repeatedly that the change in mobility during the pandemic affects local and long-distance traffic differently. Recently, there has been increased interest in traffic-by-distance, as actual and proposed policies like the “15-km rule” have targeted long-distance travel to limit the spread from COVID-19 hotspots to neighboring areas and beyond. This report explores &lt;strong&gt;how much traffic remains local&lt;/strong&gt;, &lt;strong&gt;how much crosses borders&lt;/strong&gt;, and &lt;strong&gt;how drastic regional differences are&lt;/strong&gt;.&lt;/p&gt;
&lt;p&gt;The report can be &lt;a href=&#34;https://www.covid-19-mobility.org/reports/adjacent-travel/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;found on the covid-19-mobility monitor site&lt;/a&gt; but is also available as an &lt;a href=&#34;https://observablehq.com/@adrianzachariae/traffic-in-the-pandemic-by-distance&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Observable notebook&lt;/a&gt;.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>COVID-19</title>
      <link>https://synosys.github.io/covid-19/</link>
      <pubDate>Thu, 28 Jan 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/covid-19/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Dirk Brockmann macht Schule</title>
      <link>https://synosys.github.io/old_news/dirk_9th_grade/</link>
      <pubDate>Thu, 28 Jan 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_news/dirk_9th_grade/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Alles, was das Unterwegssein bremst, ist jetzt sinnvoll</title>
      <link>https://synosys.github.io/press/zeitonline_2021_01_21/</link>
      <pubDate>Thu, 21 Jan 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/press/zeitonline_2021_01_21/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Project</title>
      <link>https://synosys.github.io/old_projects/</link>
      <pubDate>Wed, 20 Jan 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/old_projects/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Visualizations</title>
      <link>https://synosys.github.io/visualizations/</link>
      <pubDate>Wed, 20 Jan 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/visualizations/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Visualizations</title>
      <link>https://synosys.github.io/viz/</link>
      <pubDate>Wed, 20 Jan 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/viz/</guid>
      <description>&lt;!-- &lt;a href=&#34;../viz/wan/wan.html&#34; target =&#34;_blank&#34;&gt;World Air Aviation Network&lt;/a&gt;. --&gt;
</description>
    </item>
    
    <item>
      <title>Wir müssen an allen Ecken ganz massiv Kontakte reduzieren</title>
      <link>https://synosys.github.io/press/rbb24_2021_01_15/</link>
      <pubDate>Fri, 15 Jan 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/press/rbb24_2021_01_15/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Corona Datenspende App</title>
      <link>https://synosys.github.io/project/datenspende/</link>
      <pubDate>Thu, 14 Jan 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/project/datenspende/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Pressekonferenz des RKI</title>
      <link>https://synosys.github.io/press/youtube_2021_01_14/</link>
      <pubDate>Thu, 14 Jan 2021 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/press/youtube_2021_01_14/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Epipack</title>
      <link>https://synosys.github.io/project/epipack/</link>
      <pubDate>Thu, 22 Oct 2020 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/project/epipack/</guid>
      <description></description>
    </item>
    
    <item>
      <title>COVID-19 lockdown induces disease-mitigating structural changes in mobility networks</title>
      <link>https://synosys.github.io/publication/schlosser-2020-covid/</link>
      <pubDate>Fri, 03 Jul 2020 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/schlosser-2020-covid/</guid>
      <description></description>
    </item>
    
    <item>
      <title>How behavioural sciences can promote truth, autonomy and democratic discourse online</title>
      <link>https://synosys.github.io/publication/phillipp-2020-behavioural/</link>
      <pubDate>Mon, 15 Jun 2020 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/phillipp-2020-behavioural/</guid>
      <description></description>
    </item>
    
    <item>
      <title>German COVID-19 Snapshot Monitoring (COSMO)-Welle 11 (12.05.2020)</title>
      <link>https://synosys.github.io/publication/betsch-2020-kosmo-welle11/</link>
      <pubDate>Fri, 01 May 2020 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/betsch-2020-kosmo-welle11/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Coronavirus-Pandemie--Die Krise nachhaltig überwinden</title>
      <link>https://synosys.github.io/publication/brockmann-2020-coronavirus/</link>
      <pubDate>Mon, 13 Apr 2020 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/brockmann-2020-coronavirus/</guid>
      <description></description>
    </item>
    
    <item>
      <title>German COVID-19 Snapshot Monitoring (COSMO)-Welle 9 (28.04.2020)</title>
      <link>https://synosys.github.io/publication/betsch-2020-kosmo-welle9/</link>
      <pubDate>Wed, 01 Apr 2020 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/betsch-2020-kosmo-welle9/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Modeling echo chambers and polarization dynamics in social networks</title>
      <link>https://synosys.github.io/publication/phillipp-2020-echo/</link>
      <pubDate>Thu, 27 Feb 2020 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/phillipp-2020-echo/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China</title>
      <link>https://synosys.github.io/publication/maier-2020-effective/</link>
      <pubDate>Tue, 25 Feb 2020 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/maier-2020-effective/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Netwulf</title>
      <link>https://synosys.github.io/project/netwulf/</link>
      <pubDate>Wed, 29 Jan 2020 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/project/netwulf/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Comprehensive integrated NGS-based surveillance and contact-network modeling unravels transmission dynamics of vancomycin-resistant enterococci in a high-risk population within a tertiary care hospital</title>
      <link>https://synosys.github.io/publication/neumann-2020-comprehensive/</link>
      <pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/neumann-2020-comprehensive/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Digitale Epidemiologie</title>
      <link>https://synosys.github.io/publication/brockmann-2020-digitale/</link>
      <pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/brockmann-2020-digitale/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Experiencing the risk of overutilising opioids among patients with chronic non-cancer pain in ambulatory care (ERONA): the protocol of an exploratory, randomised controlled trial</title>
      <link>https://synosys.github.io/publication/wegwarth-2020-experiencing/</link>
      <pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/wegwarth-2020-experiencing/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Massive parallelization boosts big Bayesian multidimensional scaling</title>
      <link>https://synosys.github.io/publication/holbrook-2020-massive/</link>
      <pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/holbrook-2020-massive/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Accelerating dynamics of collective attention</title>
      <link>https://synosys.github.io/publication/phillipp-2019-accelerating/</link>
      <pubDate>Fri, 15 Mar 2019 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/phillipp-2019-accelerating/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Example Slides</title>
      <link>https://synosys.github.io/talk/ws-2022-complex-systems/</link>
      <pubDate>Tue, 05 Feb 2019 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/talk/ws-2022-complex-systems/</guid>
      <description>&lt;h1 id=&#34;create-slides-in-markdown-with-wowchemy&#34;&gt;Create slides in Markdown with Wowchemy&lt;/h1&gt;
&lt;p&gt;&lt;a href=&#34;https://wowchemy.com/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Wowchemy&lt;/a&gt; | &lt;a href=&#34;https://wowchemy.com/docs/content/slides/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Documentation&lt;/a&gt;&lt;/p&gt;
&lt;hr&gt;
&lt;h2 id=&#34;features&#34;&gt;Features&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;Efficiently write slides in Markdown&lt;/li&gt;
&lt;li&gt;3-in-1: Create, Present, and Publish your slides&lt;/li&gt;
&lt;li&gt;Supports speaker notes&lt;/li&gt;
&lt;li&gt;Mobile friendly slides&lt;/li&gt;
&lt;/ul&gt;
&lt;hr&gt;
&lt;h2 id=&#34;controls&#34;&gt;Controls&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;Next: &lt;code&gt;Right Arrow&lt;/code&gt; or &lt;code&gt;Space&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Previous: &lt;code&gt;Left Arrow&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Start: &lt;code&gt;Home&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Finish: &lt;code&gt;End&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Overview: &lt;code&gt;Esc&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Speaker notes: &lt;code&gt;S&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Fullscreen: &lt;code&gt;F&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Zoom: &lt;code&gt;Alt + Click&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://revealjs.com/pdf-export/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;PDF Export&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
&lt;hr&gt;
&lt;h2 id=&#34;code-highlighting&#34;&gt;Code Highlighting&lt;/h2&gt;
&lt;p&gt;Inline code: &lt;code&gt;variable&lt;/code&gt;&lt;/p&gt;
&lt;p&gt;Code block:&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-python&#34;&gt;porridge = &amp;quot;blueberry&amp;quot;
if porridge == &amp;quot;blueberry&amp;quot;:
    print(&amp;quot;Eating...&amp;quot;)
&lt;/code&gt;&lt;/pre&gt;
&lt;hr&gt;
&lt;h2 id=&#34;math&#34;&gt;Math&lt;/h2&gt;
&lt;p&gt;In-line math: $x + y = z$&lt;/p&gt;
&lt;p&gt;Block math:&lt;/p&gt;
&lt;p&gt;$$
f\left( x \right) = ;\frac{{2\left( {x + 4} \right)\left( {x - 4} \right)}}{{\left( {x + 4} \right)\left( {x + 1} \right)}}
$$&lt;/p&gt;
&lt;hr&gt;
&lt;h2 id=&#34;fragments&#34;&gt;Fragments&lt;/h2&gt;
&lt;p&gt;Make content appear incrementally&lt;/p&gt;
&lt;pre&gt;&lt;code&gt;{{&amp;lt; fragment &amp;gt;}} $\mathbf{y} =  $ {{&amp;lt; /fragment &amp;gt;}}
{{&amp;lt; fragment &amp;gt;}} $X\boldsymbol\beta$ {{&amp;lt; /fragment &amp;gt;}}
{{&amp;lt; fragment &amp;gt;}} $+ \boldsymbol\varepsilon$ {{&amp;lt; /fragment &amp;gt;}}
&lt;/code&gt;&lt;/pre&gt;
&lt;p&gt;Press &lt;code&gt;Space&lt;/code&gt; to play!&lt;/p&gt;
&lt;p&gt;&lt;span class=&#34;fragment &#34; &gt;
   $\mathbf{y} =  $ 
&lt;/span&gt;
&lt;span class=&#34;fragment &#34; &gt;
   $X\boldsymbol\beta$ 
&lt;/span&gt;
&lt;span class=&#34;fragment &#34; &gt;
   $+ \boldsymbol\varepsilon$ 
&lt;/span&gt;&lt;/p&gt;
&lt;hr&gt;
&lt;p&gt;A fragment can accept two optional parameters:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;code&gt;class&lt;/code&gt;: use a custom style (requires definition in custom CSS)&lt;/li&gt;
&lt;li&gt;&lt;code&gt;weight&lt;/code&gt;: sets the order in which a fragment appears&lt;/li&gt;
&lt;/ul&gt;
&lt;hr&gt;
&lt;h2 id=&#34;speaker-notes&#34;&gt;Speaker Notes&lt;/h2&gt;
&lt;p&gt;Add speaker notes to your presentation&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-markdown&#34;&gt;{{% speaker_note %}}

- Only the speaker can read these notes
- Press `S` key to view

{{% /speaker_note %}}
&lt;/code&gt;&lt;/pre&gt;
&lt;p&gt;Press the &lt;code&gt;S&lt;/code&gt; key to view the speaker notes!&lt;/p&gt;
&lt;aside class=&#34;notes&#34;&gt;
  &lt;ul&gt;
&lt;li&gt;Only the speaker can read these notes&lt;/li&gt;
&lt;li&gt;Press &lt;code&gt;S&lt;/code&gt; key to view&lt;/li&gt;
&lt;/ul&gt;

&lt;/aside&gt;
&lt;hr&gt;
&lt;h2 id=&#34;themes&#34;&gt;Themes&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;black: Black background, white text, blue links (default)&lt;/li&gt;
&lt;li&gt;white: White background, black text, blue links&lt;/li&gt;
&lt;li&gt;league: Gray background, white text, blue links&lt;/li&gt;
&lt;li&gt;beige: Beige background, dark text, brown links&lt;/li&gt;
&lt;li&gt;sky: Blue background, thin dark text, blue links&lt;/li&gt;
&lt;/ul&gt;
&lt;hr&gt;
&lt;ul&gt;
&lt;li&gt;night: Black background, thick white text, orange links&lt;/li&gt;
&lt;li&gt;serif: Cappuccino background, gray text, brown links&lt;/li&gt;
&lt;li&gt;simple: White background, black text, blue links&lt;/li&gt;
&lt;li&gt;solarized: Cream-colored background, dark green text, blue links&lt;/li&gt;
&lt;/ul&gt;
&lt;hr&gt;

&lt;section data-noprocess data-shortcode-slide
  
      
      data-background-image=&#34;boards.webp&#34;
  &gt;

&lt;h2 id=&#34;custom-slide&#34;&gt;Custom Slide&lt;/h2&gt;
&lt;p&gt;Customize the slide style and background&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-markdown&#34;&gt;{{&amp;lt; slide background-image=&amp;quot;boards.webp&amp;quot; &amp;gt;}}
{{&amp;lt; slide background-color=&amp;quot;#0000FF&amp;quot; &amp;gt;}}
{{&amp;lt; slide class=&amp;quot;my-style&amp;quot; &amp;gt;}}
&lt;/code&gt;&lt;/pre&gt;
&lt;hr&gt;
&lt;h2 id=&#34;custom-css-example&#34;&gt;Custom CSS Example&lt;/h2&gt;
&lt;p&gt;Let&amp;rsquo;s make headers navy colored.&lt;/p&gt;
&lt;p&gt;Create &lt;code&gt;assets/css/reveal_custom.css&lt;/code&gt; with:&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-css&#34;&gt;.reveal section h1,
.reveal section h2,
.reveal section h3 {
  color: navy;
}
&lt;/code&gt;&lt;/pre&gt;
&lt;hr&gt;
&lt;h1 id=&#34;questions&#34;&gt;Questions?&lt;/h1&gt;
&lt;p&gt;&lt;a href=&#34;https://discord.gg/z8wNYzb&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Ask&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;&lt;a href=&#34;https://wowchemy.com/docs/content/slides/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Documentation&lt;/a&gt;
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</description>
    </item>
    
    <item>
      <title>Modular hierarchical and power-law small-world networks bear structural optima for minimal first passage times and cover time</title>
      <link>https://synosys.github.io/publication/maier-2019-modular/</link>
      <pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/maier-2019-modular/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Courses &amp; Teaching</title>
      <link>https://synosys.github.io/courses/</link>
      <pubDate>Thu, 28 Jun 2018 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/courses/</guid>
      <description>&lt;h2 id=&#34;complex-systems-in-biology-winter-2022httpsrocshu-berlindecoursescomplex-systems&#34;&gt;&lt;a href=&#34;https://rocs.hu-berlin.de/courses/complex-systems/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Complex Systems in Biology, Winter 2022&lt;/a&gt;&lt;/h2&gt;
&lt;h2 id=&#34;complex-systems-in-biology-winter-2021httpsrocshu-berlindecoursescomplex-systems-2021&#34;&gt;&lt;a href=&#34;https://rocs.hu-berlin.de/courses/complex-systems-2021&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Complex Systems in Biology, Winter 2021&lt;/a&gt;&lt;/h2&gt;
&lt;hr&gt;
</description>
    </item>
    
    <item>
      <title>Privacy Policy</title>
      <link>https://synosys.github.io/privacy/</link>
      <pubDate>Thu, 28 Jun 2018 00:00:00 +0100</pubDate>
      <guid>https://synosys.github.io/privacy/</guid>
      <description>&lt;!-- Add your privacy policy here and set `draft: false` to publish it. Otherwise, delete this file if you don&#39;t need it. --&gt;
&lt;p&gt;The &lt;strong&gt;Legal Notice of TU Dresden&lt;/strong&gt; applies with the following amendments:&lt;/p&gt;
&lt;h2 id=&#34;responsibilities&#34;&gt;RESPONSIBILITIES&lt;/h2&gt;
&lt;p&gt;If you have any questions regarding content, please contact:
Dirk Brockmann
Technische Universität Dresden
DE – 01062 Dresden
Email: &lt;a href=&#34;mailto:dirk.brockmann@tu-dresden.de&#34;&gt;dirk.brockmann@tu-dresden.de&lt;/a&gt;
Telephone: +49 351 463-33199&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Technical implementation:&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;Technische Universität Dresden
Institution / Department
Center for Synergy of Systems
Georg-Schumann-Straße 7A
01069 Dresden
Head: Dirk Brockmann
Email: &lt;a href=&#34;mailto:dirk.brockmann@tu-dresden.de&#34;&gt;dirk.brockmann@tu-dresden.de&lt;/a&gt;&lt;/p&gt;
&lt;h2 id=&#34;data-protection-declaration&#34;&gt;DATA PROTECTION DECLARATION&lt;/h2&gt;
&lt;p&gt;TU Dresden processes personal data for the use of the public website &lt;a href=&#34;https://synosys.github.io&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;https://synosys.github.io&lt;/a&gt;). This personal data pertains to cookies only, which are used exclusively for providing this service. In particular, this means that this website uses no tracking cookies to record or analyze user movement and behavior on our website.&lt;/p&gt;
&lt;h3 id=&#34;legal-basis&#34;&gt;Legal basis&lt;/h3&gt;
&lt;p&gt;The legal basis for this is Art. 6 para. 1 letter f GDPR.&lt;/p&gt;
&lt;h2 id=&#34;rights-of-data-subjects&#34;&gt;Rights of data subjects&lt;/h2&gt;
&lt;p&gt;— You have the right to obtain information from TU Dresden on the data processed concerning you and/or to request the&lt;br&gt;
correction of inaccurate data.
— You have the right to erasure and restriction of processing as well as the right to object to the processing.
— You can contact TU Dresden&amp;rsquo;s Data Protection Officer at any time:&lt;/p&gt;
&lt;p&gt;Technische Universität Dresden
Data Protection Officer
01062 Dresden
Tel.: +49 351 463 32839
Fax : +49 351 463 39718
Email: &lt;a href=&#34;mailto:informationssicherheit@tu-dresden.de&#34;&gt;informationssicherheit@tu-dresden.de&lt;/a&gt;
&lt;a href=&#34;https://tu-dresden.de/informationssicherheit&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;https://tu-dresden.de/informationssicherheit&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;— You also have the right to appeal to the supervisory authority if you believe that the processing of data concerning your person does not comply with the law. The supervisory authority for data protection is:&lt;/p&gt;
&lt;p&gt;Saxon Data Protection Officer
Dr. Juliane Hundert
Devrientstraße 5
01067 Dresden
Email: &lt;a href=&#34;mailto:post@sdtb.sachsen.de&#34;&gt;post@sdtb.sachsen.de&lt;/a&gt;
Phone: + 49 (0) 35185471 101
&lt;a href=&#34;https://www.datenschutz.sachsen.de&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;www.datenschutz.sachsen.de&lt;/a&gt;
&amp;quot;&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Terms</title>
      <link>https://synosys.github.io/terms/</link>
      <pubDate>Thu, 28 Jun 2018 00:00:00 +0100</pubDate>
      <guid>https://synosys.github.io/terms/</guid>
      <description>&lt;!-- Add your terms here and set `draft: false` to publish it. Otherwise, delete this file if you don&#39;t need it. --&gt;
&lt;!-- Template for the formulation of the accessibility statement: the statement must be accessible from every subpage of a website, e.g. as a link &#34;Accessibility&#34; in the header or footer of the page.
The statement must be in an accessible format, usually as a separate HTML page. --&gt;
&lt;h2 id=&#34;accessibility-statement&#34;&gt;Accessibility Statement&lt;/h2&gt;
&lt;p&gt;This accessibility statement applies to &lt;a href=&#34;https://synosys.github.io&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;https://synosys.github.io&lt;/a&gt;. This website is run by Center Synergy of Systems (SynoSys).
Center Synergy of Systems (SynoSys) is committed to making its websites and mobile applications accessible, in accordance with “Website Accessibility Act” BfWebG (Barrierefreie-Websites-Gesetz) and the “Accessible IT Ordinance” BITV 2.0 (Barrierefreie-Informationstechnik-Verordnung), implementing EU Directive 2016/2102.&lt;/p&gt;
&lt;h3 id=&#34;preparation-of-this-accessibility-statement&#34;&gt;Preparation of this accessibility statement&lt;/h3&gt;
&lt;p&gt;This statement was prepared on October 30, 2024 and last reviewed on November 1,2024.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Compliance status&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;No BITV test has been conducted yet for the website. This is planned in the form of self-evaluation on March 31, 2025.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Feedback and contact information&lt;/strong&gt;
If you notice any shortcomings concerning accessible design, please contact Dirk Brockmann (&lt;a href=&#34;mailto:dirk.brockmann@tu-dresden.de&#34;&gt;dirk.brockmann@tu-dresden.de&lt;/a&gt;, 0351 463 33199).&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Enforcement procedure&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;If we do not process your feedback satisfactorily from your point of view, you can contact the Enforcement Body of Saxony:&lt;/p&gt;
&lt;p&gt;Office of the Commissioner of the Saxon State Government for the Affairs of Persons with Disabilities
Albertstraße 10, 01097 Dresden&lt;/p&gt;
&lt;p&gt;Postal address: Archivstraße 1, 01097 Dresden
Email: &lt;a href=&#34;mailto:info.behindertenbeauftragter@sk.sachsen.de&#34;&gt;info.behindertenbeauftragter@sk.sachsen.de&lt;/a&gt;
Telephone: +49 351 564-12161
Fax: +49 351 564-12169
Website: &lt;a href=&#34;https://www.inklusion.sachsen.de&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;https://www.inklusion.sachsen.de&lt;/a&gt;&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Hosts mobility and spatial spread of Rickettsia rickettsii</title>
      <link>https://synosys.github.io/publication/polo-2018-host/</link>
      <pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/polo-2018-host/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Human mobility, networks and disease dynamics on a global scale</title>
      <link>https://synosys.github.io/publication/brockmann-2018-human/</link>
      <pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/brockmann-2018-human/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Cover time for random walks on arbitrary complex networks</title>
      <link>https://synosys.github.io/publication/maier-2017-cover/</link>
      <pubDate>Wed, 18 Oct 2017 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/maier-2017-cover/</guid>
      <description></description>
    </item>
    
    <item>
      <title>A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin--destination demand networks</title>
      <link>https://synosys.github.io/publication/saberi-2017-complex/</link>
      <pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/saberi-2017-complex/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Effective distances for epidemics spreading on complex networks</title>
      <link>https://synosys.github.io/publication/iannelli-2017-effective/</link>
      <pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/iannelli-2017-effective/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Fundamental properties of cooperative contagion processes</title>
      <link>https://synosys.github.io/publication/chen-2017-fundamental/</link>
      <pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/chen-2017-fundamental/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Molecular surveillance of measles and rubella in the WHO European Region: new challenges in the elimination phase</title>
      <link>https://synosys.github.io/publication/santibanez-2017-molecular/</link>
      <pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/santibanez-2017-molecular/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Public health: This message must be herd</title>
      <link>https://synosys.github.io/publication/brockmann-2017-public/</link>
      <pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/brockmann-2017-public/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Temporal dynamics of online petitions</title>
      <link>https://synosys.github.io/publication/bottcher-2017-temporal/</link>
      <pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/bottcher-2017-temporal/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Connectivity of diagnostic technologies: improving surveillance and accelerating tuberculosis elimination</title>
      <link>https://synosys.github.io/publication/andre-2016-connectivity/</link>
      <pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/andre-2016-connectivity/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Saving human lives: What complexity science and information systems can contribute</title>
      <link>https://synosys.github.io/publication/helbing-2015-saving/</link>
      <pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/helbing-2015-saving/</guid>
      <description></description>
    </item>
    
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      <title>Spatial and functional heterogeneities shape collective behavior of tumor-immune networks</title>
      <link>https://synosys.github.io/publication/wells-2015-spatial/</link>
      <pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/wells-2015-spatial/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Origin detection during food-borne disease outbreaks-a case study of the 2011 ehec/hus outbreak in germany</title>
      <link>https://synosys.github.io/publication/manitz-2014-origin/</link>
      <pubDate>Wed, 01 Jan 2014 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/manitz-2014-origin/</guid>
      <description></description>
    </item>
    
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      <title>Robustness of skeletons and salient features in networks</title>
      <link>https://synosys.github.io/publication/shekhtman-2014-robustness/</link>
      <pubDate>Wed, 01 Jan 2014 00:00:00 +0000</pubDate>
      <guid>https://synosys.github.io/publication/shekhtman-2014-robustness/</guid>
      <description></description>
    </item>
    
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      <title>Understanding and predicting the global spread of emergent infectious diseases</title>
      <link>https://synosys.github.io/publication/brockmann-2014-understanding/</link>
      <pubDate>Wed, 01 Jan 2014 00:00:00 +0000</pubDate>
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