
4. Epidemiology
We use developed mathematical model to estimate epidemiological parameters, assess intervention on epidemiological dynamics.
We utilize our developed mathematical models to estimate key epidemiological parameters, such as transmission rates, reproduction numbers, and incubation periods, which are essential for understanding disease dynamics. Additionally, we assess the impact of various interventions—such as vaccination, quarantine, antiviral treatments, and social distancing measures—on the spread and control of infectious diseases. By simulating different scenarios, our models provide quantitative insights that help optimize public health strategies, improve outbreak preparedness, and guide evidence-based policymaking.
As an example, we developed a multi-scale model to better understand the connection between intervention and infectious disease epidemiology.
The spread of infectious diseases is regulated by two interconnected mechanisms with different scales. First, at the micro-scale (i.e., within a host), viral agents invade target cells, in which they replicate themselves. The host immune system is soon activated, and viruses are eventually cleared. Thus, the viral load and immune level in a host dramatically changes during and after an infection. Second, at the macro scale (i.e., population level), the infection spreads in the population through contacts between infectious and susceptible hosts. The transmission risk is also dependent on the viral load of infectious hosts. These two scales can be studied using mathematical modelling, and they have traditionally been studied separately. However, explicitly modelling both processes at the same time could shed new light on the epidemiology of infectious diseases and reveal how the biological mechanisms within each host shape the epidemiology of infectious diseases.
The goal of this project is the development of a novel multi-scale modelling framework connecting within-host viral and immune dynamics with the epidemiology of infectious diseases to address practical public health questions. The proposed framework is built upon the integration of infection-specific viral and immune dynamics models and detailed agent-based models of the transmission process between hosts and aims to bring non-incremental advances in the state of the art of mathematical and computational infectious disease modelling.
Related publications:
Yamamoto N†, Ejima K†*, Mestre LM, Owora AH, Inoue M, Tsugane S, Sawada N (2024). Body mass index trajectories and mortality risk in Japan using a population-based prospective cohort study: the Japan Public Health Center-based Prospective Study. Int J Epidemiol. 53(1):dyad145
Yamamoto N†, Koizumi Y†, Tsuzuki S†, Ejima K, Takano M, Iwami S‡*, Mizushima D‡*, Oka S‡* (2022). Evaluating the cost-effectiveness of a pre-exposure prophylaxis program for HIV prevention for men who have sex with men in Japan. Scientific Reports 12(1):3088
Ejima K†*, Kim KS†, Bento AI†, Iwanami S, Fujita Y, Ito Y, Ohashi H, Koizumi Y, Watashi K, Aihara K, Shibuya K, Iwami S* (2022). Estimation of timing of infection from longitudinal SARS-CoV-2 viral load data: mathematical modelling study. BMC Infectious Diseases 22(1):656
Ejima K†*, Kim KS†, Ludema C, Bento AI, Iwanami S, Fujita Y, Ohashi H, Koizumi Y, Watashi K, Aihara K, Nishiura H, Iwami S* (2021). Estimation of the incubation period of COVID-19 using viral load data. Epidemics 35:100454
Ejima K†*, Koizumi Y, Yamamoto N, Rosenberg M, Ludema C, Bento AI, Yoneoka D, Ichikawa S, Mizushima D, Iwami S†* (2021). HIV testing by public health centers and municipalities and new HIV cases during the COVID-19 pandemic in Japan. Journal of Acquired Immune Deficiency Syndromes 87(2):e182-e187
Ejima K†*, Kim KS†, Iwanami S†, Fujita Y, Li M, Zoh RS, Aihara K, Miyazaki T, Wakita T, Iwami S* (2021). Time variation in the probability of failing to detect a case of PCR testing for SARS-CoV-2 as estimated from a viral dynamics model. Journal of the Royal Society Interface 18:20200947