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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

Overview of attention for article published in Parasites & Vectors, October 2016
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2 tweeters

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5 Dimensions

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34 Mendeley
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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
Published in
Parasites & Vectors, October 2016
DOI 10.1186/s13071-016-1824-7
Pubmed ID
Authors

Arminder Deol, Joanne P. Webster, Martin Walker, Maria-Gloria Basáñez, T. Déirdre Hollingsworth, Fiona M. Fleming, Antonio Montresor, Michael D. French

Abstract

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. 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. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 21%
Student > Ph. D. Student 6 18%
Student > Master 4 12%
Professor 3 9%
Student > Doctoral Student 3 9%
Other 2 6%
Unknown 9 26%
Readers by discipline Count As %
Medicine and Dentistry 6 18%
Agricultural and Biological Sciences 3 9%
Environmental Science 3 9%
Nursing and Health Professions 3 9%
Mathematics 1 3%
Other 7 21%
Unknown 11 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 April 2017.
All research outputs
#4,562,241
of 9,339,536 outputs
Outputs from Parasites & Vectors
#1,228
of 2,697 outputs
Outputs of similar age
#121,136
of 257,014 outputs
Outputs of similar age from Parasites & Vectors
#43
of 80 outputs
Altmetric has tracked 9,339,536 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,697 research outputs from this source. They receive a mean Attention Score of 4.2. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 257,014 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 80 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.