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.
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.
authors.affiliation: Schistosomiasis Control Initiative, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's campus) Imperial College London, London, W2 1PG, UK. arminder.deol09@imperial.ac.uk.
authors.forename: Arminder
authors.initials: A
authors.lastname: Deol
authors.affiliation: Schistosomiasis Control Initiative, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's campus) Imperial College London, London, W2 1PG, UK.|Department of Pathology and Pathogen Biology, Centre for Emerging, Endemic and Exotic Diseases, Royal Veterinary College, University of London, Herts, London, AL9 7TA, UK.|London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's campus), Imperial College London, London, W2 1PG, UK.
authors.forename: Joanne P
authors.initials: JP
authors.lastname: Webster
authors.affiliation: London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's campus), Imperial College London, London, W2 1PG, UK.
authors.forename: Martin
authors.initials: M
authors.lastname: Walker
authors.affiliation: London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's campus), Imperial College London, London, W2 1PG, UK.
authors.forename: Maria-Gloria
authors.initials: MG
authors.lastname: Basáñez
authors.affiliation: Department of Mathematics, University of Warwick, Coventry, CV4 7AL, UK.
authors.forename: T Déirdre
authors.initials: TD
authors.lastname: Hollingsworth
authors.affiliation: Schistosomiasis Control Initiative, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's campus) Imperial College London, London, W2 1PG, UK.
authors.forename: Fiona M
authors.initials: FM
authors.lastname: Fleming
authors.affiliation: Neglected Tropical Disease Department, World Health Organization, Avenue Appia, 20, Geneva, Switzerland.
authors.forename: Antonio
authors.initials: A
authors.lastname: Montresor
authors.affiliation: Schistosomiasis Control Initiative, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St Mary's campus) Imperial College London, London, W2 1PG, UK.
authors.forename: Michael D
authors.initials: MD
authors.lastname: French
mesh_terms: D000818:Animals; D003430:Cross-Sectional Studies; D019468:Disease Management; D006801:Humans; D008302:Mali / Q000453:epidemiology; D008390:Markov Chains; D015233:Models, Statistical; D011223:Praziquantel / Q000627:therapeutic use*; D015995:Prevalence; D012550:Schistosoma mansoni / Q000187:drug effects*; D012553:Schistosomiasis haematobia / Q000188:drug therapy / Q000453:epidemiology / Q000635:transmission; D012555:Schistosomiasis mansoni / Q000188:drug therapy* / Q000453:epidemiology* / Q000469:parasitology / Q000635:transmission; D012556:Schistosomicides / Q000627:therapeutic use*; D012984:Software; D014454:Uganda / Q000453:epidemiology
chemical_list: D012556:Schistosomicides; D011223:Praziquantel
keywords: Intensity; Markov modelling; Praziquantel; Prevalence; Schistosomiasis; Transition probabilities; Transmission dynamics