Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts

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-isol...

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Veröffentlicht in:PloS one 2021-04, Vol.16 (4), p.e0249676-e0249676
Hauptverfasser: Wulkow, Hanna, Conrad, Tim O F, Djurdjevac Conrad, Nataša, Müller, Sebastian A, Nagel, Kai, Schütte, Christof
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container_title PloS one
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Conrad, Tim O F
Djurdjevac Conrad, Nataša
Müller, Sebastian A
Nagel, Kai
Schütte, Christof
description 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.
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subjects Berlin - epidemiology
Biology and Life Sciences
Chronic obstructive pulmonary disease
Communicable Disease Control
Computer science
Computer Simulation
Coronaviruses
COVID-19
COVID-19 - epidemiology
COVID-19 - prevention & control
COVID-19 - transmission
Differential equations
Disease transmission
Distribution
Epidemics
Funding
Humans
Infections
Influenza
Macroscopic models
Medicine and Health Sciences
Mental health
Models, Statistical
Mortality
Multiple objective analysis
Objectives
Optimization
Ordinary differential equations
Parameter estimation
Pareto optimization
Physical Sciences
Prevention
Research and Analysis Methods
SARS-CoV-2 - isolation & purification
Severe acute respiratory syndrome coronavirus 2
Social Sciences
Stochastic Processes
Telematics
title Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts
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