4.6 Article

Modeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations

Journal

PLOS ONE
Volume 16, Issue 10, Pages -

Publisher

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0259108

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Governments worldwide employ NPIs to control the spread of COVID-19, facing the challenge of estimating impacts before implementation. This study in Germany uses agent-based simulations and SEIR models to analyze COVID-19 spread, finding quarantining infected individuals and utilizing home offices to be most effective NPIs. Education-related NPIs have limited impact individually but combined openings could result in significant case increases. Results also show varying effects of NPIs on different age groups.
Governments around the globe use non-pharmaceutical interventions (NPIs) to curb the spread of coronavirus disease 2019 (COVID-19) cases. Making decisions under uncertainty, they all face the same temporal paradox: estimating the impact of NPIs before they have been implemented. Due to the limited variance of empirical cases, researchers could so far not disentangle effects of individual NPIs or their impact on different demographic groups. In this paper, we utilize large-scale agent-based simulations in combination with Susceptible-Exposed-Infectious-Recovered (SEIR) models to investigate the spread of COVID-19 for some of the most affected federal states in Germany. In contrast to other studies, we sample agents from a representative survey. Including more realistic demographic attributes that influence agents' behavior yields accurate predictions of COVID-19 transmissions and allows us to investigate counterfactual what-if scenarios. Results show that quarantining infected people and exploiting industry-specific home office capacities are the most effective NPIs. Disentangling education-related NPIs reveals that each considered institution (kindergarten, school, university) has rather small effects on its own, yet, that combined openings would result in large increases in COVID-19 cases. Representative survey-characteristics of agents also allow us to estimate NPIs' effects on different age groups. For instance, re-opening schools would cause comparatively few infections among the riskgroup of people older than 60 years.

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