Department of Mathematics; Department of Computer Science; Institute of Computational and Theoretical Studies
The transmission of infectious diseases can be affected by many or even hidden factors, making it difficult to accurately predict when and where outbreaks may emerge. One approach at the moment is to develop and deploy surveillance systems in an effort to detect outbreaks as timely as possible. This enables policy makers to modify and implement strategies for the control of the transmission. The accumulated surveillance data including temporal, spatial, clinical, and demographic information, can provide valuable information with which to infer the underlying epidemic networks. Such networks can be quite informative and insightful as they characterize how infectious diseases transmit from one location to another. The aim of this work is to develop a computational model that allows inferences to be made regarding epidemic network topology in heterogeneous populations. We apply our model on the surveillance data from the 2009 H1N1 pandemic in Hong Kong. The inferred epidemic network displays significant effect on the propagation of infectious diseases.
Source Publication Title
Public Library of Science
© 2014 Wan et al.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
This study was supported by grants (FRG1/13-14/021) from Hong Kong Baptist University, who also supported the publication costs. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Link to Publisher's Edition
Wan, X., Liu, J., Cheung, W., & Tong, T. (2014). Inferring epidemic network topology from surveillance data. PLoS ONE, 9 (6), e100661. https://doi.org/10.1371/journal.pone.0100661