Markov model to forecast the change in prevalence of soil-transmitted helminths during a control programme: a case study in Vietnam
AuthorMontresor, A; Gabrielli, AF; Yajima, A; Lethanh, N; Biggs, B-A; Casey, GJ; Ta, TT; Engels, D; Savioli, L
Source TitleTRANSACTIONS OF THE ROYAL SOCIETY OF TROPICAL MEDICINE AND HYGIENE
PublisherOXFORD UNIV PRESS
AffiliationMedicine - Royal Melbourne Hospital
Document TypeJournal Article
CitationsMontresor, 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 StatusAccess this item via the Open Access location
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC5576531
NHMRC Grant codeNHMRC/628751
C1 - Journal Articles Refereed
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.
KeywordsInfectious Diseases; Epidemiology; Infectious Diseases
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