The Unknown of the Pandemic: An Agent-Based Model of Final Phase Risks

Abstract

Lifting social restrictions is one of the most critical decisions that public health authorities have to face during a pandemic such as COVID-19 This work focuses on the risk associated with such a decision We have called the period from the re-opening decision to epidemic expiration the ’final epidemic phase’, and con-sidered the critical epidemic conditions which could possibly emerge in this phase The factors we have consid-ered include: the proportion of asymptomatic cases, a mitigation strategy based on testing and the average duration of infectious states By assuming hypothetical configurations at the time of the re-opening decision and the partial knowledge concerning epidemic dynamics available to public health authorities, we have analyzed the risk of the re-opening decision based on possibly unreliable estimates We have presented a discrete-time stochastic model with state-dependent transmission probabilities and multi-agent simulations Our results show the different outcomes produced by different proportions of undetected asymptomatic cases, different probabilities of asymptomatic cases detected and contained, and a multivariate analysis of risk based on the average duration of asymptomatic and contained states Finally, our analysis highlights that enduring uncer-tainty, typical of this pandemic, requires a risk analysis approach to complement epidemiological studies © 2020, University of Surrey All rights reserved