Abstract
The Covid-19 disease has caused a world-wide pandemic with more than 60 million positive cases and more than 1.4 million deaths by the end of November 2020. As long as effective medical treatment and vaccination are not available, non-pharmaceutical interventions such as social distancing, self-isolation and quarantine as well as far-reaching shutdowns of economic activity and public life are the only available strategies to prevent the virus from spreading. These interventions must meet conflicting requirements where some objectives, like the minimization of disease-related deaths or the impact on health systems, demand for stronger counter-measures, while others, such as social and economic costs, call for weaker counter-measures. Therefore, finding the optimal compromise of counter-measures requires the solution of a multi-objective optimization problem that is based on accurate prediction of future infection spreading for all combinations of counter-measures under consideration. We present a strategy for construction and solution of such a multi-objective optimization problem with real-world applicability. The strategy is based on a micro-model allowing for accurate prediction via a realistic combination of person-centric data-driven human mobility and behavior, stochastic infection models and disease progression models including micro-level inclusion of governmental intervention strategies. For this micro-model, a surrogate macro-model is constructed and validated that is much less computationally expensive and can therefore be used in the core of a numerical solver for the multi-objective optimization problem. The resulting set of optimal compromises between counter-measures (Pareto front) is discussed and its meaning for policy decisions is outlined.
Highlights
The Covid-19 disease, caused by infection with the virus SARS-CoV-2, was first detected end of 2019 in China before spreading all over the world within a few months
We report on an agent-based mobility model for Berlin, that has been developed based on recent publications on the mechanism of Covid-19 spreading, and allows to simulate the dynamics of the infection spreading in the city of Berlin on a micro-level
We have developed an accompanying macro-model based on ordinary differential equations (ODEs, called the “ODE model” subsequently) that uses the agent-based models (ABMs) results to approximate the change in the infection behavior for different counter-measures
Summary
The Covid-19 disease, caused by infection with the virus SARS-CoV-2, was first detected end of 2019 in China before spreading all over the world within a few months. We observe that only the latter group parameters do change if the ODE model is fitted to data simulating different counter-measure combinations. CT kQ 1⁄4 kqnCT N ; where kq is the rate of people going into quarantine after being called by the contact tracing agency (kq = 10.25 independent of counter-measures), nCT is the number of people going to self-isolation/quarantine per symptomatic person (a parameter dependent on the effectiveness of the contract tracing agency), and CT = CT(t) is the number of positively tested individuals presently processed by the contract tracing agency This number is changing with time according to dCT.
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