Agent-based simulation of COVID-19 containment measures: the case of lockdowns in cities

Abstract

The effectiveness and political feasibility of COVID-19 containment measures such as lockdowns, are contentious. This stems in part from an absence of tools for their rigorous evaluation. Common epidemiological models such as the SEIR model generally lack the spatial resolution required for micro-level containment actions, the visualization capabilities for communicating measures such as localized lockdowns and the scenario-testing capabilities for assessing different alternatives. We present an individual-level ABM that generates geo-social networks animated by agent-agent and agent-building interactions. The model simulates real-world contexts and is demonstrated for the city of Jerusalem. Simulation outputs yield much useful information for evaluating the effectiveness of lockdowns. These include network-generated socio-spatial contagion chains for individual agents, dynamic building level contagion processes and neighborhood-level patterns of COVID-19 imports and exports useful in identifying super-spreader neighborhoods. The policy implications afforded by these various outputs are discussed.