Research
Our group focuses on developing predictive models to predict infectious disease incidence, or other related activities, such as different intervention policies. We use a combination of statistical (e.g., regression analysis, time series analysis or data assimilation techniques, etc.) and mathematical approaches to increase the prediction accuracy. Our main targeted diseases activities including, but not limited to, influenza and dengue.
One of our interests is to use mathematical modelling and evolutionary analysis to study the influenza viruses and host immunity dynamics at the population, including i) how influenza viruses escape immunity, ii) how such viruses boost individual immunity in a population, iii) how binding avidity adaptation limits influenza transmission and diversity. The study of the impact of influenza herd immunity and binding avidity is collaborated with Prof. Steven Riley at Imperial College London, UK. We are also interested in studying the effects of social interactions on disease transmission. This is a joint work with Prof. Kin On KWOK at CUHK.
Our group also focuses on modelling the impact of extreme weather conditions on Dengue viruses transmission. Nonlinear effects of both rainfall and temperatures on mosquito population size exist. Mathematical modelling of mosquito population size would improve the accuracy of forecasting Dengue incidence. To understand how climate variation drives Dengue outbreaks through mosquito population dynamics in East Asian subtropical areas (such as Hong Kong and Taiwan) would help to understand whether Dengue infection is currently moving from tropical toward temperate areas and is of great importance to health protection not just in East Asia but also subtropical areas in other parts of the world. The work on Dengue forecast and control is currently collaborated with National Health Research Institutes and National Taiwan University in Taiwan. We are also developing new models to identify f lagged effects of weather on mosquito activities in Hong Kong.
We are also interested in applying mathematical modelling and computational approaches to study biological systems and to help experimentalists to simulate the impacts under different conditions to get a better understanding of biological systems. For example, we are currently using Deep Learning technique to identify virulent pathways in Pseudomonas aeruginosa, which is an important cause of gram-negative infection, especially in immune compromised patients in hospitals. We are also interested in building software tools to study therapeutic response with next-generation sequencing techniques to achieve precision medicine.
One of our interests is to use mathematical modelling and evolutionary analysis to study the influenza viruses and host immunity dynamics at the population, including i) how influenza viruses escape immunity, ii) how such viruses boost individual immunity in a population, iii) how binding avidity adaptation limits influenza transmission and diversity. The study of the impact of influenza herd immunity and binding avidity is collaborated with Prof. Steven Riley at Imperial College London, UK. We are also interested in studying the effects of social interactions on disease transmission. This is a joint work with Prof. Kin On KWOK at CUHK.
Our group also focuses on modelling the impact of extreme weather conditions on Dengue viruses transmission. Nonlinear effects of both rainfall and temperatures on mosquito population size exist. Mathematical modelling of mosquito population size would improve the accuracy of forecasting Dengue incidence. To understand how climate variation drives Dengue outbreaks through mosquito population dynamics in East Asian subtropical areas (such as Hong Kong and Taiwan) would help to understand whether Dengue infection is currently moving from tropical toward temperate areas and is of great importance to health protection not just in East Asia but also subtropical areas in other parts of the world. The work on Dengue forecast and control is currently collaborated with National Health Research Institutes and National Taiwan University in Taiwan. We are also developing new models to identify f lagged effects of weather on mosquito activities in Hong Kong.
We are also interested in applying mathematical modelling and computational approaches to study biological systems and to help experimentalists to simulate the impacts under different conditions to get a better understanding of biological systems. For example, we are currently using Deep Learning technique to identify virulent pathways in Pseudomonas aeruginosa, which is an important cause of gram-negative infection, especially in immune compromised patients in hospitals. We are also interested in building software tools to study therapeutic response with next-generation sequencing techniques to achieve precision medicine.