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    Markov model to forecast the change in prevalence of soil-transmitted helminths during a control programme: a case study in Vietnam

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    Author
    Montresor, A; Gabrielli, AF; Yajima, A; Lethanh, N; Biggs, B-A; Casey, GJ; Ta, TT; Engels, D; Savioli, L
    Date
    2013-05-01
    Source Title
    TRANSACTIONS OF THE ROYAL SOCIETY OF TROPICAL MEDICINE AND HYGIENE
    Publisher
    OXFORD UNIV PRESS
    University of Melbourne Author/s
    Biggs, Beverley-Ann; CASEY, GERARD
    Affiliation
    Medicine - Royal Melbourne Hospital
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Montresor, A., Gabrielli, A. F., Yajima, A., Lethanh, N., Biggs, B. -A., Casey, G. J., Ta, T. T., Engels, D. & Savioli, L. (2013). Markov model to forecast the change in prevalence of soil-transmitted helminths during a control programme: a case study in Vietnam. TRANSACTIONS OF THE ROYAL SOCIETY OF TROPICAL MEDICINE AND HYGIENE, 107 (5), pp.313-318. https://doi.org/10.1093/trstmh/trt019.
    Access Status
    Access this item via the Open Access location
    URI
    http://hdl.handle.net/11343/33129
    DOI
    10.1093/trstmh/trt019
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5576531
    NHMRC Grant code
    NHMRC/628751
    Description

    C1 - Journal Articles Refereed

    Abstract
    BACKGROUND: A mathematical model based on the Markov methodology to predict the change in prevalence of soil-transmitted helminth (STH) infections during public health control activities is not available, but would be an extremely efficient planning tool. METHOD: We used the parasitological data collected during a deworming and iron supplementation programme for women of child-bearing age conducted in Vietnam between 2006 and 2011 to develop a Markov transition probability model. The transition probabilities were calculated from the observed changes in prevalence in the different classes of intensity for each STH species during the first year of intervention. The model was then developed and used to estimate the prevalence in year 2, 3, 4 and 5 for each STH species and for 'any STH infection'. The prevalence predicted by the model was then compared with the prevalence observed at different times during programme implementation. RESULTS: The comparison between the model-predicted prevalence and the observed prevalence proved a good fit of the model. CONCLUSIONS: We consider the Markov transition probability model to be a promising method of predicting changes in STH prevalence during control efforts. Further research to validate the model with observed data in different geographical and epidemiological settings is suggested to refine the prediction model.
    Keywords
    Infectious Diseases; Epidemiology; Infectious Diseases

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