To investigate how an infectious disease spreads, it is desirable to use the observed surveillance data to discover the underlying (often hidden) disease transmission networks. Previous studies have provided methods for inferring information diffusion networks in which each node corresponds to an individual person within the diffusion network. However, in the case of disease transmission, to effectively propose and implement intervention strategies, it is more realistic and reasonable for policy makers to study the diffusion patterns at a metapopulation level, that is, to consider disease transmission networks in which nodes represent subpopulations, and links indicate their interrelationships. Such networks can be useful in several ways: (i) to investigate hidden impact factors that in.uence epidemic dynamics, (ii) to reveal possible sources of epidemic outbreaks, and (iii) to practically develop and/or improve strategies for controlling the spread of infectious diseases. Therefore, this thesis addresses the problem of inferring disease transmission networks at a metapopulation level. A network inference method called NetEpi (Network Epidemic) is developed and evaluated using both synthetic and real-world datasets. The experimental results show that NetEpi can recover most of the ground-truth disease transmission networks using only surveillance data.
|Date of Award||19 Mar 2014|
|Supervisor||Jiming LIU (Supervisor)|
- Communicable diseases
- Computer simulation