Modelling and optimal control of multi strain epidemics, with application to COVID-19.

Edilson F Arruda, Shyam S Das, Claudia M Dias, Dayse H Pastore
Author Information
  1. Edilson F Arruda: Department of Decision Analytics and Risk, Southampton Business School, University of Southampton, Southampton, United Kingdom. ORCID
  2. Shyam S Das: Graduate Program in Mathematical and Computational Modeling, Multidisciplinary Institute, Federal Rural University of Rio de Janeiro, Nova Iguaçu RJ, Brazil.
  3. Claudia M Dias: Graduate Program in Mathematical and Computational Modeling, Multidisciplinary Institute, Federal Rural University of Rio de Janeiro, Nova Iguaçu RJ, Brazil.
  4. Dayse H Pastore: Department of Basic and General Disciplines, Federal Center for Technological Education Celso Suckow da Fonseca, Rio de Janeiro, Rio de Janeiro, Brazil.

Abstract

Reinfection and multiple viral strains are among the latest challenges in the current COVID-19 pandemic. In contrast, epidemic models often consider a single strain and perennial immunity. To bridge this gap, we present a new epidemic model that simultaneously considers multiple viral strains and reinfection due to waning immunity. The model is general, applies to any viral disease and includes an optimal control formulation to seek a trade-off between the societal and economic costs of mitigation. We validate the model, with and without mitigation, in the light of the COVID-19 epidemic in England and in the state of Amazonas, Brazil. The model can derive optimal mitigation strategies for any number of viral strains, whilst also evaluating the effect of distinct mitigation costs on the infection levels. The results show that relaxations in the mitigation measures cause a rapid increase in the number of cases, and therefore demand more restrictive measures in the future.

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MeSH Term

Algorithms
Brazil
COVID-19
Computer Simulation
England
Epidemics
Humans
Models, Theoretical
SARS-CoV-2
Virus Diseases

Word Cloud

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