Optimal evaluation of re-opening policies for COVID-19 through the use of metaheuristic schemes

A new contagious disease or unidentified COVID-19 variants could provoke a new col-lapse in the global economy. Under such conditions, companies, factories, and organiza-tions must adopt reopening policies that allow their operations to reduce economic effects. Effective reopening policies should be designed using mathematical models that emulate infection chains through individual interactions. In contrast to other modeling approaches, agent-based schemes represent a computational paradigm used to characterize the person-to-person interactions of individuals inside a system, providing accurate simulation results. To evaluate the optimal conditions for a reopening policy, authorities and decision-makers need to conduct an extensive number of simulations manually, with a high possibility of losing information and important details. For this reason, the integration of optimization and simulation of reopening policies could automatically find the realistic scenario under which the lowest risk of infection was attained. In this paper, the metaheuristic technique of the Whale Optimization Algorithm is used to find the solution with the minimal trans-mission risk produced by an agent-based model that emulates a hypothetical re-opening context. Our scheme finds the optimal results of different generical activation scenarios. The experimental results indicate that our approach delivers practical knowledge and es-sential estimations for identifying optimal re-opening strategies with the lowest transmis-sion risk.(c) 2023 Elsevier Inc. All rights reserved.

Automated summary

A new contagious disease or unidentified COVID-19 variants could lead to a new global economic collapse. To mitigate the economic effects, companies and organizations must adopt reopening policies based on mathematical models that simulate infection chains. Agent-based schemes provide accurate simulation results by modeling person-to-person interactions, and the integration of optimization and simulation can automatically find the realistic scenario with the lowest risk of infection.