ArticleParasites & vectors2016
Development and evaluation of a Markov model to predict changes in schistosomiasis prevalence in response to praziquantel treatment: a case study of Schistosoma mansoni in Uganda and Mali.
Article in Parasites & vectors, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Economic evaluation of primary health care integration and targeted mass drug administration in counties in western Kenya.Frontiers in health services · 2026Article
- Two-year longitudinal survey reveals high genetic diversity of Schistosoma mansoni with adult worms surviving praziquantel treatment at the start of mass drug administration in Uganda.Parasites & vectors · 2019Article
- The public health benefit and burden of mass drug administration programs in Vietnamese schoolchildren: Impact of mebendazole.PLoS neglected tropical diseases · 2018Article
- Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province.PLoS neglected tropical diseases · 2018Article
- A call to strengthen the global strategy against schistosomiasis and soil-transmitted helminthiasis: the time is now.The Lancet. Infectious diseases · 2017Review
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8 authors.
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Abstract
backgroundUnderstanding whether schistosomiasis control programmes are on course to control morbidity and potentially switch towards elimination interventions would benefit from user-friendly quantitative tools that facilitate analysis of progress and highlight areas not responding to treatment. This study aimed to develop and evaluate such a tool using large datasets collected during Schistosomiasis Control Initiative-supported control programmes.
methodsA discrete-time Markov model was developed using transition probability matrices parameterized with control programme longitudinal data on Schistosoma mansoni obtained from Uganda and Mali. Four matrix variants (A-D) were used to compare different data types for parameterization: A-C from Uganda and D from Mali. Matrix A used data at baseline and year 1 of the control programme; B used year 1 and year 2; C used baseline and year 1 from selected districts, and D used baseline and year 1 Mali data. Model predictions were tested against 3 subsets of the Uganda dataset: dataset 1, the full 4-year longitudinal cohort; dataset 2, from districts not used to parameterize matrix C; dataset 3, cross-sectional data, and dataset 4, from Mali as an independent dataset.
resultsThe model parameterized using matrices A, B and D predicted similar infection dynamics (overall and when stratified by infection intensity). Matrices A-D successfully predicted prevalence in each follow-up year for low and high intensity categories in dataset 1 followed by dataset 2. Matrices A, B and D yielded similar and close matches to dataset 1 with marginal discrepancies when comparing model outputs against datasets 2 and 3. Matrix C produced more variable results, correctly estimating fewer data points.
conclusionModel outputs closely matched observed values and were a useful predictor of the infection dynamics of S. mansoni when using longitudinal and cross-sectional data from Uganda. This also held when the model was tested with data from Mali. This was most apparent when modelling overall infection and in low and high infection intensity areas. Our results indicate the applicability of this Markov model approach as countries aim at reaching their control targets and potentially move towards the elimination of schistosomiasis.
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