<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

A Hybrid Mathematical Model for an Optimal Border Closure Policy During a Pandemic

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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...

A Hybrid Mathematical Model for an Optimal Border Closure Policy During a Pandemic

Online veröffentlicht: 21. Dez. 2023

Seitenbereich: 583 - 601

Eingereicht: 21. Nov. 2022

Akzeptiert: 07. Juli 2023

DOI: https://doi.org/10.34768/amcs-2023-0042

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

© 2023 Teddy Lazebnik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Hybrid Mathematical Model for an Optimal Border Closure Policy During a Pandemic

Online veröffentlicht: 21. Dez. 2023

Seitenbereich: 583 - 601

Eingereicht: 21. Nov. 2022

Akzeptiert: 07. Juli 2023

DOI: https://doi.org/10.34768/amcs-2023-0042

Schlüsselwörterhealth care, spatio-temporal SIR model, international bio-tourism policy, multi-agent reinforcement learning

© 2023 Teddy Lazebnik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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