Health System and Disease Modelling Development

Biostatisticians at Division of Biostatistics (DBIO) develop mathematical models for describing behaviour of a system in different public health areas. Mathematical models can take many forms, including but not limited to statistical models and system dynamics models, etc.  They are particularly useful in health policy evaluation and modelling infectious diseases transmissions but also in assessment intervention effects in clinical trials.

Infectious Disease Modelling

Owing to ethical challenges in many experimental studies (e.g. travel restriction in influenza A/H1N1 pandemic), mathematical models provide an important tool to assess the intervention effectiveness by using computational simulations. Over the past years, the tradition susceptible-infectious-recovered (SIR) models and its extension have been applied to a number of infectious diseases such as influenza, measles, and tuberculosis.  DBIO biostatisticians collaborate with public health officials such as Zhejiang and Hubei Provincial Center for Disease Control and Prevention (CDC) on studies of evaluation of interventions on epidemic controls.

Modelling F1  

Health Policy Modelling

Mathematical models for health policy are well suited to address dynamic complexity problems in public health. Through employing the models, policy options with the potential of improving health can be built in and tested in various scenarios and combinations with stakeholders such as health practitioners, health economists, and decision makers. DBIO works with different local officials such as Hospital Authority, Food and Health Bureau on hospital resources allocations and elderly care managements.

Modelling F2

Clinical Trials Modelling

Statistical models are useful in planning clinical trials and decision making especially for those in initial phases with limited data. DBIO has a number of academic and industrial partners on clinical trial studies.  A recent investigation on the use of mathematical models in Continuous Glucose Monitoring Systems (CGMS) data can greatly enhance the study power of a proof-of-concept trial for patients with pre-diabetes.

 Modelling F3