Mathematical modelling and control of Schistosomiasis in Hubei Province, China

Zimin Chen, Lan Zou, Dingwen Shen, Weinian Zhang, Shigui Ruan

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

Hubei Province, along with four other provinces in the central and eastern China where schistosomiasis is endemic (Anhui, Hunan, Jiangsu, and Jiangxi), is located in the lake and marshland regions along the Yangtze River. High population density, large numbers of farm cattle, and huge areas of snail habitat are the main characteristics that maintain the persistence of the disease and the transmission of the parasite Schistosoma japonicum in these regions. Based on the schistosomiasis infection data from Hubei province, we propose a mathematical model for the human-cattle-snail transmission of schistosomiasis. The model is a system consisting of six ordinary differential equations that describe susceptible and infected human, cattle and snail subpopulations. After analyzing the existence of the disease-free equilibrium of the model, we determine the basic reproduction number and use the model to simulate the schistosomiasis infection data from Hubei Province. By carrying out sensitivity analyses of the basic reproduction number on various parameters, we find that the transmission of S. japonicum between cattle and snails plays a more important role than that between humans and snails in the endemicity of schistosomiasis in these regions. This strongly suggests that, to control and eventually eradicate schistosomiasis in the lake and marshland regions in China, a more comprehensive approach needs to include environmental factors in order to break the cattle-snail transmission cycle.

Original languageEnglish (US)
Pages (from-to)119-125
Number of pages7
JournalActa Tropica
Volume115
Issue number1-2
DOIs
StatePublished - Jul 2010

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Keywords

  • Basic reproduction number
  • Disease-free equilibrium
  • Mathematical model
  • Schistosomiasis
  • Stability

ASJC Scopus subject areas

  • Parasitology
  • Infectious Diseases

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