Abstract
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 risk-group of people older than 60 years.
Highlights
In 2020, governments tried to curb the spread of COVID-19 without having an appropriate medical response available
To derive the efficacy of non-pharmaceutical interventions (NPIs) in counterfactual scenarios, the first step is to ensure the predictive capability of the model by assessing its fit to the actual spread of COVID-19
We do that by removing each NPI that was at work in reality and examine the effect of its omission from the baseline scenario in the
Summary
In 2020, governments tried to curb the spread of COVID-19 without having an appropriate medical response available. Most governments implemented non-pharmaceutical interventions (NPIs) which affect societal life in an unseen manner. Survey-based simulations of non-pharmaceutical interventions (SOEP) data or parts of it due to a very restrictive data access policy. We cannot share data on the agent population based on the SOEP. Researchers affiliated with an institution can request access to SOEP at this website: https:// www.diw.de/en/diw_01.c.601584.en/data_access. All of the code used to run the simulation and the publicly available datasets used to calibrate the simulation model are available in this repository: https://github.com/mariuzka/covid19_sim
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