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
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Published version
Author(s)
Type
Journal Article
Abstract
Background: 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. This study aimed to develop and
evaluate such a tool using large datasets collected during Schistosomiasis Control Initiative-supported control
programmes.
Methods: A 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.
Results: The 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.
Conclusion: Model 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.
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.
Methods: A 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.
Results: The 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.
Conclusion: Model 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.
Date Issued
2016-10-12
Date Acceptance
2016-10-01
Citation
Parasites & Vectors, 2016, 9
ISSN
1756-3305
Publisher
BioMed Central
Journal / Book Title
Parasites & Vectors
Volume
9
Copyright Statement
© 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
License URL
Sponsor
The Childrens Investment Fund Foundation (UK)
Grant Number
N/A
Subjects
Intensity
Markov modelling
Praziquantel
Prevalence
Schistosomiasis
Transition probabilities
Transmission dynamics
Mycology & Parasitology
1108 Medical Microbiology
1117 Public Health And Health Services
Publication Status
Published
Article Number
543