In the wake of the COVID-19 pandemic, mathematical epidemiology has been tasked with explaining and forecasting case and fatality numbers based on incomplete data. As both the disease itself as well as policies introduced to curtail its spread turned out to have considerable effects on health and economic outcomes, it is more imperative than ever for risk evaluation to understand how epidemics spread and how interventions affect them.
This web session focuses on differential equation models in epidemiology and illustrates at the working example COVID-19 what can be learned from public health data in light of these models.
Registration deadline: 6 September 2021
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