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 considered the critical epidemic conditions which could possibly emerge in this phase. The factors we have considered 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 uncertainty, typical of this pandemic, requires a risk analysis approach to complement epidemiological studies.

The Unknown of the Pandemic: An Agent-Based Model of Final Phase Risks / M. Cremonini, S. Maghool. - In: JASSS. - ISSN 1460-7425. - 23:4(2020 Oct 31). [10.18564/jasss.4426]

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

M. Cremonini
Primo
;
S. Maghool
Secondo
2020

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 considered the critical epidemic conditions which could possibly emerge in this phase. The factors we have considered 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 uncertainty, typical of this pandemic, requires a risk analysis approach to complement epidemiological studies.
Stochastic Epidemic Model; Multi-Agent Simulation; Network Analysis; Agent-Based Model; Risk Analysis
Settore INF/01 - Informatica
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
Settore SPS/07 - Sociologia Generale
31-ott-2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/782339
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