Estimation of basic reproduction numbers as an indicator of infectivity based on incomplete or indirect information

2022 Fiscal Year Final Research Report

• Project/Area Number: 18K17371
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 58030:Hygiene and public health-related: excluding laboratory approach
• Research Institution: National Center for Global Health and Medicine (2019-2022); Hokkaido University (2018)
• Principal Investigator: Asai Yusuke 国立研究開発法人国立国際医療研究センター, 国際感染症センター, 研究員 (70779991)
• Project Period (FY): 2018-04-01 – 2023-03-31
• Keywords: 理論疫学 / 数理モデル / 数値シミュレーション

Outline of Final Research Achievements

A mathematical model including time delay and diagnostic bias terms was constructed and COVID-19 data were analyzed. Infection-related parameters were estimated and the results published as an original article (doi: 10.1016/j.jiac.2020.12.011.). Using a mathematical model that describes infectious disease epidemics as differential equations and international human movement as a stochastic process, the arrival time of infected individuals in disease-free countries was estimated and the relationship with R0 was clarified (https://doi.org/10.1016/j.idm.2023.05.004). A model of infection transmission was developed that takes into account the distance between individuals. Simulations confirmed the geographical and temporal spread of infection.

Free Research Field

理論疫学

Academic Significance and Societal Importance of the Research Achievements

医療政策を考える上で，データに基づいた意思決定は不可欠である．基本再生産数R0はこの判断に欠かせない指標であるが，用いられるデータが限られており，その推定が困難なケースが多く報告されてきた．
そこで本研究では，今まで用いられてこなかった間接的なデータをもとにしたR0の推定モデル・手法を構築した．構築した手法を用いて感染にかかるパラメータの推定とシミュレーションを行い，チャーター便の効果や検疫の効果を推定した．今後も新たな感染症の流行が想定される．本研究で構築したR0の推定手法やその枠組みを活用しシナリオ分析を行うことで，政策決定に必要なデータの取得と定量的な議論が可能となった．