Imperial College London
Alternate

Joanne P. Webster

Faculty of Medicine, School of Public Health

Visiting Professor
 
 
 
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Contact

 

joanne.webster Website

 
 
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Location

 

Medical SchoolSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Deol:2016:10.1186/s13071-016-1824-7,
author = {Deol, AK and Webster, JP and walker, M and Basáñez, MG and Hollingsworth, TD and Fleming, F and Montresor, A and French, M},
doi = {10.1186/s13071-016-1824-7},
journal = {Parasites & Vectors},
title = {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},
url = {http://dx.doi.org/10.1186/s13071-016-1824-7},
volume = {9},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Background: Understanding whether schistosomiasis control programmes are on course to control morbidityand potentially switch towards elimination interventions would benefit from user-friendly quantitative tools thatfacilitate analysis of progress and highlight areas not responding to treatment. This study aimed to develop andevaluate such a tool using large datasets collected during Schistosomiasis Control Initiative-supported controlprogrammes.Methods: A discrete-time Markov model was developed using transition probability matrices parameterized withcontrol 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 Aused data at baseline and year 1 of the control programme; B used year 1 and year 2; C used baseline and year 1from selected districts, and D used baseline and year 1 Mali data. Model predictions were tested against 3 subsetsof the Uganda dataset: dataset 1, the full 4-year longitudinal cohort; dataset 2, from districts not used toparameterize matrix C; dataset 3, cross-sectional data, and dataset 4, from Mali as an independent dataset.Results: The model parameterized using matrices A, B and D predicted similar infection dynamics (overall andwhen stratified by infection intensity). Matrices A-D successfully predicted prevalence in each follow-up year for lowand high intensity categories in dataset 1 followed by dataset 2. Matrices A, B and D yielded similar and closematches to dataset 1 with marginal discrepancies when comparing model outputs against datasets 2 and 3. MatrixC produced more variable results, correctly estimating fewer data points.Conclusion: Model outputs closely matched observed values and were a useful predictor of the infection dynamicsof S. mansoni when using longitudinal and cross-sectional data from Uganda. This also held when the model wastested with data from Mali. This was most
AU  - Deol,AK
AU  - Webster,JP
AU  - walker,M
AU  - Basáñez,MG
AU  - Hollingsworth,TD
AU  - Fleming,F
AU  - Montresor,A
AU  - French,M
DO  - 10.1186/s13071-016-1824-7
PY  - 2016///
SN  - 1756-3305
TI  - 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
T2  - Parasites & Vectors
UR  - http://dx.doi.org/10.1186/s13071-016-1824-7
UR  - http://hdl.handle.net/10044/1/41606
VL  - 9
ER  -
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