Simulation of Non-Pharmaceutical Interventions on COVID-19 with an Agent-based Model of Zonal Restraint

Non-Pharmaceutical Interventions (NPI) are currently the only mechanism governments can use to mitigate the impact of the COVID-19 epidemic. Similarly to the actual spread of the disease, the dynamics of the contention patterns emerging from the application of NPIs are complex and depend on interactions between people within a specific region as well as other stochastic factors associated to demographic, geographic, political and economical conditions. Agent-based models simulate microscopic rules of simultaneous spatial interactions between multiple agents within a population, in an attempt to reproduce the complex dynamics of the effect of the contention measures. In this way, it is possible to design individual behaviours along with NPI scenarios, measuring how the simulation dynamics is affected and therefore, yielding rapid insights to perform a broad assessment of the potential of composite interventions at different stages of the epidemic. In this paper we describe a model and a tool to experiment with such kind of analysis applied to a conceptual city, considering a number of widely-applied NPIs such as social distancing, case isolation, home quarantine, total lockdown, sentinel testing, mask wearing and a distinctive "zonal" enforcement measure, requiring these interventions to be applied gradually to separated enclosed districts (zones). We find that the model is able to capture emerging dynamics associated to these NPIs; besides, the zonal contention strategy yields an improvement on the mitigation impact across all scenarios of combination with individual NPIs. The model and tool are open to extensions to account for omitted or newer factors affecting the planning and design of NPIs intended to counter the late stages or forthcoming waves of the COVID-19 crisis.