Hospital reconversion in response to the COVID-19 pandemic using simulation and multi-objective genetic algorithms

With the outbreak of the novel coronavirus SARS-CoV2, many countries have faced problems because of their available hospital capacity. Health systems must be prepared to restructure their facilities and meet the requirements of the pandemic while keeping their services and specialties active. This process, known as hospital reconversion, contributes to minimizing the risk of contagion between hospital staff and patients and optimizing the efficient treatment and disposal of healthcare wastes that represent a risk of nosocomial infection contagion. A methodology based upon simulation and mathematical optimization with genetic algorithms is proposed to address the hospital reconversion problem. Firstly, a discrete event simulation model is developed to study the flow of patients within the hospital system. Subsequently, the hospital reconversion problem is formulated through a mathematical model seeking to maximize the proximity relationships between departments and minimize the costs due to the flow of agents within the system. Finally, the results obtained from the optimization process are evaluated through the simulation model. The proposed framework is validated by assessing the hospital reconversion process in a COVID-19 Hospital in Mexico. The results show the mathematical model's effectiveness by incorporating the medical personnel's expertise in decisions regarding the use of elevators, departments' location, structural dimensions, use of corridors, and the floors to which the departments are assigned when facing a pandemic. The contribution of this approach can be replicated during the hospital reconversion process in other hospitals with different characteristics.