Prediction of SARS-CoV-2 infection dynamics and impact of policy by modeling individual decisions of the general population

social sciences - The current SARS-CoV-2 pandemic leads to a worldwide threat of collapsing health care systems and large numbers of casualties. This necessitates a swift but appropriate response in terms of non-pharmacological interventions to prevent and control the infection. Currently, measures to restrict social contacts are the most important means of limiting the spread of the virus. These measures have economic, psychosocial and other detrimental consequences and need to be limited to the extent and duration that is absolutely necessary. Here, we propose a model that allows to estimate a projected effect of measures based on simulating the impact on individual persons and their behavior in a given environment, going beyond the current state of the art in epidemiologic models. A central improvement over conventional models is the inclusion of a fuzzy logic-based description of the decisions individuals take under the influence of restrictive measures, media reporting, and public communication of policy. By developing this model into a tool including a graphical user interface and parameterizing it based on local data, we provide an accessible way to simulate the impact of policy decisions and inform and guide political and administrative decision processes.