About Ejima Lab at LKCMedicine

Mathematical models serve as vital computational tools in exploring infectious diseases across various scales, from population dynamics to within-host phenomena. At the within-host level, these models meticulously delineate viral replication, clearance, and immune responses within individual hosts. However, they fall short in directly assessing the population-level impact of detailed individual response to infection. On the contrary, between-host models are extensively employed to delineate transmission dynamics between individuals, study disease epidemiology, and evaluate intervention strategies at a broader scale. Nonetheless, between-host models lack the granularity to account for individual-level biological processes.

In our laboratory, we not only delve into within-host responses for scientific exploration but also bridge the gap between individual and population scales, leveraging cutting-edge biological insights to inform bedside practice and societal strategies through integrative mathematical modeling. This multiscale approach to infectious disease modeling promises a holistic understanding of disease dynamics, offering avenues to simulate the impact of targeted interventions across different population strata.

Research project: Patient stratification for development of better isolation guideline

Isolation of COVID-19 has been implemented in most countries since the pandemic started. However, the isolation guideline varies between countries. Scientifically evidenced isolation guidelines are needed. In this study, we compare different isolation guidelines, using the simulator mimicking the time course change in viral load. This project is led by Keisuke Ejima, an Assistant Professor at Lee Kong Chian School of Medicine, Nanyang Technological University.

Requirements: Candidates should have a Bachelor’s degree in a quantitative field, such as data science, computational biology, mathematics, computer science, (bio)statistics, or related field. Research experience and/or educational background on public health and medicine is a plus but not essential. Computational experience on R is preferred. Good communication skill and respectful attitude for teamwork.

Key responsibilities:

• Collect and clean data

• Compute descriptive statistics of the data

• Review literature

• Involved in manuscript writing

• All activities are under supervision by PI