Research project: Connecting within-host viral dynamics with the epidemiology of COVID-19: a multiscale computational infrastructure

In this project, we aim to develop a multiscale modeling framework and computational infrastructure to connect within-host viral dynamics and between-host transmission dynamics. The multiscale model will help address key epidemiological and public health questions which could not be answered by classic (single scale) approaches. Specifically, we plan to develop and calibrate the multiscale model for SARS-CoV-2 and assess effect of intervention (e.g., antiviral treatment) on epidemiological dynamics. This project is led by Keisuke Ejima, an Assistant Professor at Lee Kong Chian School of Medicine, Nanyang Technological University.

Key responsibilities:

• Collect and clean data

• Analyze the data with mathematical/statistical models

• Review literature

• Involved in manuscript writing

• Additional task and research activities assigned by the PI

Key requirements and competencies:

• Master’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.