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This paper provides a mathematical optimization strategy for optimal municipal solid waste management in the context of the COVID-19 epidemic. This strategy integrates two approaches: optimization and machine learning models. First, the optimization model determines the optimal supply chain for the municipal waste management system. Then, machine learning prediction models estimate the required parameters over time, which helps generate future projections for the proposed strategy. The optimization model was coded in the General Algebraic Modeling System, while the prediction model was coded in the Python programming environment. A case study of New York City was addressed to evaluate the proposed strategy, which includes extensive socioeconomic data sets to train the machine learning model. We found the predicted waste collection over time based on the socioeconomic data. The results show trade-offs between the economic (profit) and environmental (waste sent to landfill) objectives for future scenarios, which can be helpful for possible pandemic scenarios in the following years. Supplementary Information The online version contains supplementary material available at 10.1007/s10668-023-03354-2.

The COVID-19 pandemic has caused crises in all fields in which the human race develops. This manuscript reviews various strategies and models developed to deal with the effects of the COVID-19 pandemic generated in different areas, such as vaccine logistics, energy consumption, environmental impact, economic impact, and the management of medical resources. Through this review, it can be observed how the proposed strategies can be compatible with each other and can lead to a greater benefit if they are used together. Some of them are designed to obtain short-term benefits (such as vaccine logistics), while the others are considered long-term benefits (such as economic stabilization). The main objective of this work is to show the reader strategies already established to combat the effects produced by the pandemic that can serve as a guide for the development of new and more robust strategies to achieve greater benefits.

Se dan a conocer distintitas estrategias de optimización matemática que ayuden a combatir las problemáticas generadas por el COVID-19. Para tal fin, las estrategias presentadas emplean metodologías de optimización comúnmente aplicadas en otras áreas de la ciencia para atender la logística de vacunas, el consumo de energía, el impacto ambiental, el impacto económico y la gestión de los recursos sanitarios, entre otros. Impulsar el desarrollo de este tipo de estrategias permitirá estar mejor preparados ante la posibilidad de futuras pandemias.

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