Preparing for Shortages of Future COVID-19 Drugs: A Data-Based Model for Optimal Allocation

• Main Authors: Sy, Charlie L., Aviso, Kathleen, Cayamanda, Christina D., Chiu, Anthony F., Lucas, Rochelle Irene, Promentilla, Michael Angelo, Razon, Luis F., Tan, Raymond R., Tapia, John Frederick, Torneo, Ador, Ubando, Aristotle T., Yu, Derrick Ethelbhert C.
• Format: text
• Published: Animo Repository 2020
• Subjects: Medicine; Covid-19; pandemic; drugs; Philippines; Chemicals and Drugs; Community Health and Preventive Medicine; Public Health Education and Promotion
• Online Access: https://animorepository.dlsu.edu.ph/res_aki/102; https://animorepository.dlsu.edu.ph/context/res_aki/article/1107/viewcontent/Preparing_for_Shortages_of_Covid_19_drugs.pdf
• Institution: De La Salle University

Drugs for the treatment of Covid-19 are currently beign tested, and those that are apporved for use are likely to be in short supply due to the global scale of the pandemic. This policy brief proposes a model for optimally allocating future Covid-19 drugs to patients to minimize deaths under conditions of resource scarcity. A linear programming model is developed that estimates the potential number of deaths that may result from Covid-19 under two scenarios: with antivirals and without antivirals. It takes into account patient risk level, the severity of their symptoms, resource availability in hospitals (i.e. hospital beds, critical care units, ventilators), observed mortality rates, and share of the Philippine population. Based on simulations, the model can make actionable recommendations on how to prioritize the allocation of the drugs.