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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-016-1824-7) contains supplementary material, which is available to authorized users.
Highlights
Understanding 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
We focus on the ability of the models to capture the observed point prevalence values within the 95 % Prediction interval (PI)
Where the upper or lower bounds of the 95 % Confidence interval (CI) around the observed values overlapped with the model predictions only, the model was able to capture the uncertainty in the data but not the point prevalence
Summary
Understanding 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. Many schistosomiasis control programmes have been running for several years, and have achieved their primary target of controlling schistosomiasis-related morbidity (where the aim of “control” is reducing prevalence of heavy infection to < 5 % across sentinel sites at 75 % national coverage [9]), whether from intestinal schistosomiasis (caused predominantly by Schistosoma mansoni) or from urogenital schistosomiasis (caused predominantly by S. haematobium) [10]. With this in mind, the WHO, alongside its global partners, has set the agenda for the stage of control. Interruption of transmission is anticipated, thereby accelerating elimination of the disease [12]
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