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A new approach to modelling schistosomiasis transmission based on stratified worm burden

  • D. GURARIE (a1), C. H. KING (a2) and X. WANG (a1)
  • DOI: http://dx.doi.org/10.1017/S0031182010000867
  • Published online: 13 July 2010
Abstract
SUMMARY

Background/Objective. Multiple factors affect schistosomiasis transmission in distributed meta-population systems including age, behaviour, and environment. The traditional approach to modelling macroparasite transmission often exploits the ‘mean worm burden’ (MWB) formulation for human hosts. However, typical worm distribution in humans is overdispersed, and classic models either ignore this characteristic or make ad hoc assumptions about its pattern (e.g., by assuming a negative binomial distribution). Such oversimplifications can give wrong predictions for the impact of control interventions. Methods. We propose a new modelling approach to macro-parasite transmission by stratifying human populations according to worm burden, and replacing MWB dynamics with that of ‘population strata’. We developed proper calibration procedures for such multi-component systems, based on typical epidemiological and demographic field data, and implemented them using Wolfram Mathematica. Results. Model programming and calibration proved to be straightforward. Our calibrated system provided good agreement with the individual level field data from the Msambweni region of eastern Kenya. Conclusion. The Stratified Worm Burden (SWB) approach offers many advantages, in that it accounts naturally for overdispersion and accommodates other important factors and measures of human infection and demographics. Future work will apply this model and methodology to evaluate innovative control intervention strategies, including expanded drug treatment programmes proposed by the World Health Organization and its partners.

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*Corresponding author: Department of Mathematics, 220 Yost Hall, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106-7058, USA. Tel: 001 216 368 2857. Fax: 001 216 368 5163. E-mail: dxg5@cwru.edu
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