Modelling local and global effects on the risk of contracting Tuberculosis using stochastic Markov-chain models
Hoad, K., van't Hoog, A. H., Rosen, D., Marston, B., Nyabiage, L., Williams, B. G., Dye, C. and Cheng, R. C. H.. (2009) Modelling local and global effects on the risk of contracting Tuberculosis using stochastic Markov-chain models. Mathematical Biosciences, Vol.218 (No.2). pp. 98-104. ISSN 0025-5564Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.mbs.2009.01.002
For some diseases, the transmission of infection can cause spatial clustering of disease cases. This clustering has an impact on how one estimates the rate of the spread of the disease and on the design of control strategies. It is, however, difficult to assess such clustering, (local effects on transmission), using traditional statistical methods. A stochastic Markov-chain model that takes into account possible local or more dispersed global effects on the risk of contracting disease is introduced in the context of the transmission dynamics of tuberculosis. The model is used to analyse TB notifications collected in the Asembo and Gem Divisions of Nyanza Province in western Kenya by the Kenya Ministry of Health/National Leprosy and Tuberculosis Program and the Centers for Disease Control and Prevention. The model shows evidence of a pronounced local effect that is significantly greater than the global effect. We discuss a number of variations of the model which identify how this local effect depends on factors such as age and gender. Zoning/clustering of villages is used to identify the influence that zone size has on the model's ability to distinguish local and global effects. An important possible use of the model is in the design of a community randomised trial where geographical clusters of people are divided into two groups and the effectiveness of an intervention policy is assessed by applying it to one group but not the other. Here the model can be used to take the effect of case clustering into consideration in calculating the minimum difference in an outcome variable (e.g. disease prevalence) that can be detected with statistical significance. It thereby gauges the potential effectiveness of such a trial. Such a possible application is illustrated with the given time/spatial TB data set. (C) 2009 Elsevier Inc. All rights reserved.
|Item Type:||Journal Article|
|Subjects:||Q Science > QH Natural history > QH301 Biology|
|Divisions:||Faculty of Social Sciences > Warwick Business School|
|Journal or Publication Title:||Mathematical Biosciences|
|Publisher:||Elsevier Science Inc.|
|Official Date:||April 2009|
|Number of Pages:||7|
|Page Range:||pp. 98-104|
|Access rights to Published version:||Restricted or Subscription Access|
|Funder:||Engineering and Physical Sciences Research Council (EPSRC)|
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