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

Abstract

Vaccines 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

Code Availability Statement

The codes used in the work are available at https://github.com/PharmaPSE-Covid/COVID19.

Acknowledgements

The authors acknowledge industrial experts from the International Society of Pharmaceutical Engineering (ISPE) Japan community of practice “PharmaPSE COP” for useful discussions.

Author Contributions

K.M., J.K., Y.I., K.O., S.B., and H.S. conceived the initial idea. K.M., J.K., Y.I., K.O., and H.S. designed the study. Y.I. performed the computations, and K.M., J.K., and K.O. verified the results. H.S. supervised the project. K.M. and J.K. took the lead in writing the manuscript with support from Y.I., K. O., S.B., and H.S.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data generated during the study are available in a public repository, https://github.com/PharmaPSE-Covid/COVID19.

Additional information

Funding

This research is supported by a Grant-in-Aid for Scientific Research (B) No. 21H01699 from the Japan Society for the Promotion of Science.