Markov model to forecast the change in prevalence of soil-transmitted helminths during a control programme: a case study in Vietnam
Author
Montresor, A; Gabrielli, AF; Yajima, A; Lethanh, N; Biggs, B-A; Casey, GJ; Ta, TT; Engels, D; Savioli, LDate
2013-05-01Source Title
TRANSACTIONS OF THE ROYAL SOCIETY OF TROPICAL MEDICINE AND HYGIENEPublisher
OXFORD UNIV PRESSAffiliation
Medicine - Royal Melbourne HospitalMetadata
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Journal ArticleCitations
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 locationOpen Access at PMC
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5576531NHMRC Grant code
NHMRC/628751Description
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 DiseasesExport Reference in RIS Format
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