A toy optimization model for understanding the decision-making against a pandemic: COVID-19 case study

Abstract

After COVID-19 pandemic has affected the health and economy of people around the world; social distance was the most used measure to prevent infections throughout the almost three years of the pandemic. In this work, we construct a decision-making framework to reactivate the economic and social activities of cities, in a customized manner, considering the virus distribution and the health of the population. The proposed framework is modeled as a linear programming optimization model, where a city is discretized into territories that can open or restart their activities at certain percentages. The optimization variables represent the opening percentages, weighted by the relative economic importance of each territory, while virus propagation and health indices define the constraints of the model. The simplicity of the model, helps to understand the basics of how decision-makers could define clear policies supported by parameters that balance economic and health priorities. Moreover, our case study suggests that the model is sensitive to detect different periods of the pandemic, giving high percentages of opening when the combination of low infection and death rates, and health system capacity leads to low risk, while suggesting stringent restrictions of mobility in critical scenarios. Although the study focuses on the COVID-19 pandemic, the proposed framework can be adapted to future public health crises, provided the appropriate recalibration of parameters and data.