/tags/opinion-dynamics/ opinion dynamics Source Themes Academic (https://sourcethemes.com/academic/)en-us© 2022Fri, 01 Jul 2022 22:12:34 +0200 /images/icon_hu0b7a4cb9992c9ac0e91bd28ffd38dd00_9727_512x512_fill_lanczos_center_2.png /tags/opinion-dynamics/ /project/infoxpand/ Fri, 01 Jul 2022 22:12:34 +0200 /project/infoxpand/ <h2 id="information-opinion-mobility-behavior-and-bayesian-inference-in-infectious-disease-modeling---subproject-c">Information, opinion, mobility, behavior, and Bayesian inference in infectious disease modeling - Subproject C.</h2> <p>The COVID-19 pandemic was accompanied by an &ldquo;infodemic,&rdquo; i.e., an excess of information about the virus, protective measures, and government intervention. In particular, misinformation and conspiracy theories disseminated on the Internet were blamed for increasing polarization of opinion, radicalization, and declining trust in institutions. It was warned that this had fueled the pandemic and made it even more difficult to manage. However, models of disease spread, such as those used to predict pandemic dynamics, ignore the fact that information and the pandemic form a complex interaction. While these models take into account that, for example, low vaccination rates increase hospitalizations, they fail to consider that knowledge of increasing hospitalizations motivates people to get vaccinated. The main goal in infoXpand is to understand this feedback loop between pandemic and information dissemination and to derive suggestions for future decision makers. To this end, we have formed an interdisciplinary consortium with unique expertise in pandemic modeling, opinion dynamics, mobility, and human behavior. We closely develop and analyze agent-based models and compartmental models that capture both classical disease dynamics and opinion dynamics. We calibrate critical model assumptions with data from social science survey studies and behavioral experiments, as well as extensive mobility data.</p> <h2 id="project-partners">Project partners</h2> <ul> <li>Dr. Viola Priesemann - Max Planck Institute for Dynamics and Self-Organization</li> <li>Prof. Dr. Mirjam Kretzschmar - Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht</li> <li>Prof. Dr. Michael Mäs - Karlsruhe Institute of Technology</li> <li>Prof. Dr. Kai Nagel - Technische Universität Berlin</li> </ul> /project/optimagent/ Fri, 01 Jul 2022 22:12:34 +0200 /project/optimagent/ <h2 id="optimized-strategies-for-epidemic-control-in-highly-heterogeneous-populations---subproject-d">Optimized strategies for epidemic control in highly heterogeneous populations - Subproject D.</h2> <p>The SARS-CoV-2 pandemic poses an unprecedented challenge to society and policy-making. The goal of OptimAgent is to develop a standardized model-based framework to support public health decision-making processes that can evaluate a wide range of infection control interventions. The focus is on the design of an agent-based model (ABM) that goes substantially beyond simulation approaches used to date. Through a flexible modular structure and an extensive consultation process with national and international modeling experts, the model will be tailored in particular to inform health policy decision-making during future pandemics. In addition, it will also be adaptable for endemic pathogens. The model will realistically represent the socio-demographic and regional structures of Germany. Agents will have demographic, socio-economic, sociological as well as psychological characteristics that influence individual contact behavior, risk of infection and disease. Based on the results of comprehensive and sophisticated analyses of contact behavior, specific model modules will be developed on selective target-directed contact restraints in different settings, contact tracking and testing strategies. The flexible modular model design will also provide opportunities for easy integration of additional components. The focus of the project is to analyze the impact of different dimensions of heterogeneity in the population as well as their interaction on the incidence of infection. This will provide new insights into the importance of heterogeneity in the spread of severe respiratory infectious diseases in the population and the effectiveness of pandemic control measures.</p> <h2 id="projektpartner">Projektpartner</h2> <ul> <li>Prof. Dr. Rafael Mikolajczyk, Jun.-Prof. Dr. Alexander Kuhlmann, Dr. Johannes Horn - Martin-Luther-Universität Halle-Wittenberg</li> <li>Prof. Dr. André Karch, Dr. Veronika Jäger - Westfälische Wilhelms-Universität</li> <li>Prof. Dr. Vitaly Belik, Freie Universität Berlin</li> <li>Prof. Dr. Markus Scholz, Universität Leipzig</li> <li>Prof. Dr. Jan Pablo Burgard &amp; Prof. Dr. Ralf Münnich; Universität Trier</li> <li>Dr. Wolfgang Bock, Technische Universität Kaiserslautern</li> <li>Prof. Dr. Bernd Hellingrath, Westfälische Wilhelms-Universität Münster</li> <li>Prof. Dr. Tyll Krüger, Technische Universität Breslau</li> <li>Prof. Dr. Mirjam E. Kretzschmar, Universität Utrecht</li> <li>Dr. Berit Lange, Helmholtz-Zentrum für Infektionsforschung</li> <li>Prof. Dr. Wolfgang Greiner, Universität Bielefeld</li> <li>Prof. Dr. Uwe Siebert &amp; Prof. Dr. Beate Jahn; UMIT – University for Health Sci- ences, Medical Informatics and Technology</li> <li>Dr. Sten Rüdiger, NET CHECK GmbH</li> </ul>