Scenario Analysis of Vaccine Supply for COVID-19 in Japan Using Mathematical Models of Infectious Diseases

Abstract

AbstractVaccines suppress the increased spread of infectious diseases or the number of severe cases. Considerable research and development yielded several vaccines against coronavirus disease 2019 (COVID-19). To contain the disease, governments have run vaccination campaigns and implemented restrictions depending on vaccination status. Ensuring the stability of vaccine supply with increased and/or seasonal demand has thus become critical. However, determination of the required vaccine supply is difficult due to variations in virus mutations, their impacts on the disease, and vaccine efficacy. In this work, we developed a mathematical model to consider the effect of vaccination on the infection numbers, taking COVID-19 as a case study. We have conducted a sensitivity analysis to identify the key variables affecting the required vaccine supply. We have conducted a scenario analysis to assess the impact of changes in vaccine supply parameters on the number of severe cases in Japan. Production of different products at dual-use facilities has been suggested to tackle the variability in vaccine demand. The analysis provided insights into the feasibility and adequate timing for product-switch at dual-use facilities. The proposed method can be applied to other infectious diseases and can be expanded to support various aspects for decision-making for policy building.

Keywords

• Pandemic
• SEIR model
• Machine learning
• Dual-use facility
• Policy making