
1. Data analysis/simulation
We develop mathematical and statistical models to analyze various types of data.
We develop mathematical and statistical models to analyze various types of data. Recently, our work has focused on developing mechanistic models to describe biological responses—such as viral load and immunological markers—following infection or vaccination. Additionally, we employ standard statistical methods, such as survival analysis, and leverage machine learning/AI techniques to analyze unstructured data, particularly when the data generation process is highly complex or unknown.
As an example, temporal change in viral load (i.e., viral dynamics) of viruses causing acute respiratory infection including SARS-CoV-2 has been described by a simple mathematical model, commonly referred to as the target cell limited model (Panel A) with only three variables T(t), I(t), and V(t), which are the numbers of uninfected target cells, infected target cells, and the amount of virus at time, respectively. The model accounts for the fundamental biological process of the infection within a host, such as viral replication and elimination due to immune response and explains the longitudinal viral load data (Panel B). However, the mechanism is not that simple and different between viruses.
We aim to build on top of this simple model by including compelling new features as driven by new empirical evidence on SARS-CoV-2 viral dynamics. For example, recent literature suggests that the difference in response of adaptive immunity, such as cytotoxic T lymphocytes and antibody, may contribute to determine the mortality risk. Therefore, we plan to extend the model to explicitly include adaptive immunity reaction to SARS-CoV-2 infection (Panel A). Further, the model will be extended to incorporate the effect of the treatment. We will primarily consider three mechanisms of action: (①) blocking virus production; (②) blocking de novo infection; and (③) promoting cytotoxicity (Panel A).
Relevant publication:
Chua HK†, Singh Ananya†, Wang Y, Goh YS, Chan CEZ, Chavatte JM, Lin RVTP, Su YCF, Ajelli M, Chia PY, Ong SWX, Lye DC, Young BE, Ejima K* (2025) Defining the critical requisites for accurate simulation of SARS-CoV-2 viral dynamics: patient characteristics and data collection protocol, Journal of Medical Virology
Kim KS†, Ejima K†, Iwanami S, Fujita Y, Ohashi H, Koizumi Y, Asai Y, Nakaoka S, Watashi K, Aihara K, Thompson RN, Ke R, Perelson AS‡, S. Iwami S‡ (2021). A quantitative model used to compare within-host SARS-CoV-2, MERS-CoV and SARS-CoV dynamics provides insights into the pathogenesis and treatment of SARS-CoV-2. PLoS Biology 19:e3001128