Project Team: Jiming Liu (In-charge), William K. Cheung, Yiuming Cheung, Xiaowen Chu
Background and Research Problems:
This initiative will be based on our earlier studies on the problems of:
- how to determine age-specific priorities for vaccine distribution in order to most effectively reduce morbidity and mortality that may be caused by the outbreak, and
- how to gain insights into the effectiveness of a vaccination programme with respect to vaccine coverage, releasing time, and/or deployment methods.
Please find http://www.ncbi.nlm.nih.gov/pubmed/21607707, which used Hong Kong as a scenario concerning specific epidemiological infection risks in different age groups.
In this initiative, we will be developing computational techniques and tools for collecting, mining, and visualizing source data as well as disease transmission/diffusion networks in order to timely detect, monitor and control emerging disease spread or epidemic outbreaks. Regarding the temporal-spatial disease diffusion network to be detected, (unlike the existing SaTScan clustering) here we define the network to consist of nodes and links, where the nodes may be the cases reported/observed over time, and the directional links connecting the nodes may correspond to the probability/likelihood of disease “diffusion” from one node to another over time. Unlike the case of influenza spreading where the detected link between the nodes would indicate the direct transmission patterns of the disease from one group of people or location to another, e.g., via air-bourne transmissions. In the case of certain infectious/parasitic disease surveillance, the discovered links between the nodes may provide us with insights into certain “transmission” patterns, which might have been due to certain HIDDEN ecological/househood environments, e.g., due to some HIDDEN mosquito ecology/evolution patterns over space and time, within the region. Therefore, the discovered links could reveal those HIDDEN “transmission” pathways.
Besides, we will be also interested in:
- dynamic behavioral models of individuals, e.g., models of decision-making behaviors in the use of health services/treatments, given varying geographical, demographical, social-economic, as well as migration profiles;
- their evolution and impacts on the dynamics of disease prevalence. By doing so, we are trying to tackle even more challenging real-world public healthcare problems than the existing disease surveillance systems/projects around the world.
Towards this, I have been and will continue learning more from our domain experts. It will be an interdisciplinary research initiative.