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

The decision of lifting social restrictions is one of the most critical that public health authorities face to, during a pandemic like the COVID-19 one. This work focuses on the risk associated to such decision. We have called 'final epidemic phase' the period from the re-opening decision to epidemic expiration and considered critical epidemic conditions possibly emerging in that final phase. Factors we have considered are: 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 about epidemic dynamics available to public health authorities, we analyzed the risk of the re-opening decision to be based on unreliable estimates. We present a discrete-time stochastic model with state-dependent transmission probabilities and multi-agent simulations. The results of our experiments show the different outcomes produced by different proportions of undetected asymptomatic cases, different probabilities of asymptomatic cases to be detected and contained, and a multivariate analysis of risk based on the average duration of the asymptomatic state and the contained state. Ultimately, the analysis we present highlights that the enduring uncertainty, characterizing the current pandemic, calls for risk analyses approaches to complement epidemiological studies.