Investigating the trade-off between self-quarantine and forced quarantine provisions to control an epidemic: An evolutionary approach

Md Mamun Ur Rashid Khan, Md Rajib Arefin, Jun Tanimoto

Research output: Contribution to journalArticlepeer-review

Abstract

During a pandemic event like the present COVID-19, self-quarantine, mask-wearing, hygiene maintenance, isolation, forced quarantine, and social distancing are the most effective nonpharmaceutical measures to control the epidemic when the vaccination and proper treatments are absent. In this study, we proposed an epidemiological model based on the SEIR dynamics along with the two interventions defined as self-quarantine and forced quarantine by human behavior dynamics. We consider a disease spreading through a population where some people can choose the self-quarantine option of paying some costs and be safer than the remaining ones. The remaining ones act normally and send to forced quarantine by the government if they get infected and symptomatic. The government pays the forced quarantine costs for individuals, and the government has a budget limit to treat the infected ones. Each intervention derived from the so-called behavior model has a dynamical equation that accounts for a proper balance between the costs for each case, the total budget, and the risk of infection. We show that the infection peak cannot be reduced if the authority does not enforce a proactive (quantified by a higher sensitivity parameter) intervention. While comparing the impact of both self- and forced quarantine provisions, our results demonstrate that the latter is more influential to reduce the disease prevalence and the social efficiency deficit (a gap between social optimum payoff and equilibrium payoff).

Original languageEnglish
Article number127365
JournalApplied Mathematics and Computation
Volume432
DOIs
Publication statusPublished - Nov 1 2022

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Applied Mathematics

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