COVID-19 Outbreak: A Data-driven Optimization Model for Allocation of Patients

Sarkar, Sobhan; Pramanik, Anima; Maiti, J.; Reniers, Genserik

COVID-19 is an unprecedented pandemic that puts the entire world at stake and the healthcare systems across the globe have faced pressing challenges. The number of COVID-19 patients increases rapidly every day. The hospitals across many countries are starving to provide adequate service to the patients due to the shortage of resources and as a consequence, patients do not get admitted to hospitals on time, which in turn creates panic and might contribute to the spread of the pandemic. Under this resource constraint situation, this study proposes a data-driven optimization model for patient allocation in hospitals. First, a compartmental model is developed for characterizing the spread of the COVID-19 virus. Then, Pareto analysis is carried out to identify the most COVID-affected cities. An optimization model is then developed for optimal patient allocation in hospitals in different cities. Finally, a sensitivity analysis is also conducted to investigate the robustness of our decision model. Using published data for Indian cities, obtained from different websites, the proposed methodology has been validated. Experimental results reveal that the proposed model offers some efficient strategies for optimal allocation of patients. A total of ten cities are identified as the most affected. Besides, four factors, namely cooperation, distances between cities, number of patients, and bed capacity per city emerge as important determinants.