A HYBRID MATHEMATICAL MODEL FOR AN OPTIMAL BORDER CLOSURE POLICY DURING A PANDEMIC

Teddy Lazebnik, Labib Shami, Svetlana Bunimovich-Mendrazitsky

Research output: Contribution to journalArticlepeer-review

Abstract

During a global health crisis, a country’s borders are a weak point through which carriers from countries with high morbidity rates can enter, endangering the health of the local community and undermining the authorities’ efforts to prevent the spread of the pathogen. Therefore, most countries have adopted some level of border closure policies as one of the first steps in handling pandemics. However, this step involves a significant economic loss, especially for countries that rely on tourism as a source of income. We developed a pioneering model to help decision-makers determine the optimal border closure policies during a health crisis that minimize the magnitude of the outbreak and maximize the revenue of the tourism industry. This approach is based on a hybrid mathematical model that consists of an epidemiological sub-model with tourism and a pandemic-focused economic sub-model, which relies on elements from the field of artificial intelligence to provide policymakers with a data-driven model for a border closure strategy for tourism during a global pandemic.

Original languageEnglish
Pages (from-to)583-601
Number of pages19
JournalInternational Journal of Applied Mathematics and Computer Science
Volume33
Issue number4
DOIs
StatePublished - 1 Dec 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 T. Lazebnik et al.

Keywords

  • health care
  • international bio-tourism policy
  • multi-agent reinforcement learning
  • spatio-temporal SIR model

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