8 Sep 2021
Understanding the COVID Pandemic - Models in Mathem. Epidemiology
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.
Organised by the EAA – European Actuarial Academy GmbH.
This web session is open to all professions (actuaries, risk managers, accountants, etc.) with interest in mathematical models for the Covid-19 pandemic. It is accessible for everyone with basic knowledge of calculus and (ordinary) differential equations and should be most interesting for individuals concerned with the evaluation of risks.
Technical RequirementsPlease check with your IT department if your firewall and computer settings support web session participation (the programme Zoom is used for this online training). Please also make sure that you are joining the web session with a stable internet connection.
Purpose and Nature
After introducing and clarifying some epidemiological terminology, we will introduce compartment models of SIR-type and explore the meaning of the reproduction number and how it is affected by different aspects of both the disease itself, vaccinations, and human behaviour, including non-pharmaceutical interventions like contact restrictions.
After briefly looking into some challenges arising from the interpretation of available data, in particular due to undetected infections, we will focus in particular on the problem of less than perfect compliance with public policies aimed at slowing the spread of an epidemic. Finally, we will combine the lessons learned and explain how transient changes in reported incidence figures arise naturally from the dynamics of the epidemic without necessarily being caused by the very policy changes they are attributed to.