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Paper details
Number 4 - December 2023
Volume 33 - 2023
A hybrid mathematical model for an optimal border closure policy during a pandemic
Teddy Lazebnik, Labib Shami, Svetlana Bunimovich-Mendrazitsky
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.
Keywords
health care, spatio-temporal SIR model, international bio-tourism policy, multi-agent reinforcement learning