Spatio-temporal and stochastic modelling of severe acute respiratory syndrome

  • Poh-Chin Lai | pclai@hku.hk Department of Geography, The University of Hong Kong, Hong Kong, China.
  • Kim-Hung Kwong Formerly at the Department of Geography, The University of Hong Kong, Hong Kong, China.
  • Ho-Ting Wong Formerly at the Department of Geography, The University of Hong Kong, Hong Kong, China.

Abstract

This study describes the development of a spatio-temporal disease model based on the episodes of severe acute respiratory syndrome (SARS) that took place in Hong Kong in 2003. In contrast to conventional, deterministic modelling approaches, the model described here is predominantly spatial. It incorporates stochastic processing of environmental and social variables that interact in space and time to affect the patterns of disease transmission in a community. The model was validated through a comparative assessment between actual and modelled distribution of diseased locations. Our study shows that the inclusion of location-specific characteristics satisfactorily replicates the spatial dynamics of an infectious disease. The Pearson’s correlation coefficients for five trials based on 3-day aggregation of disease counts for 1-3, 4-6 and 7-9 day forecasts were 0.57- 0.95, 0.54-0.86 and 0.57-0.82, respectively, while the correlation based on 5-day aggregation for the 1-5 day forecast was 0.55- 0.94 and 0.58-0.81 for the 6-10 day forecast. The significant and strong relationship between actual results and forecast is encouraging for the potential development of an early warning system for detecting this type of disease outbreaks.

Downloads

Download data is not yet available.
Published
2013-11-01
Section
Original Articles
Keywords:
infectious disease epidemiology, spatial modelling, estimating disease spread, SARS, geographical information system, early warning system, Hong Kong.
Statistics
Abstract views: 912

PDF: 607
Share it

PlumX Metrics

PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.

How to Cite
Lai, P.-C., Kwong, K.-H., & Wong, H.-T. (2013). Spatio-temporal and stochastic modelling of severe acute respiratory syndrome. Geospatial Health, 8(1), 183-192. https://doi.org/10.4081/gh.2013.65