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Advances and challenges in predicting the impact of lymphatic filariasis elimination programmes by mathematical modelling

Mathematical simulation models for transmission and control of lymphatic filariasis are useful tools for studying the prospects of lymphatic filariasis elimination. Two simulation models are currently being used. The first, EPIFIL, is a population-based, deterministic model that simulates average tr...

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Detalles Bibliográficos
Autores principales: Stolk, Wilma A, de Vlas, Sake J, Habbema, J Dik F
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1448203/
https://www.ncbi.nlm.nih.gov/pubmed/16569234
http://dx.doi.org/10.1186/1475-2883-5-5
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author Stolk, Wilma A
de Vlas, Sake J
Habbema, J Dik F
author_facet Stolk, Wilma A
de Vlas, Sake J
Habbema, J Dik F
author_sort Stolk, Wilma A
collection PubMed
description Mathematical simulation models for transmission and control of lymphatic filariasis are useful tools for studying the prospects of lymphatic filariasis elimination. Two simulation models are currently being used. The first, EPIFIL, is a population-based, deterministic model that simulates average trends in infection intensity over time. The second, LYMFASIM, is an individual-based, stochastic model that simulates acquisition and loss of infection for each individual in the simulated population, taking account of individual characteristics. For settings like Pondicherry (India), where Wuchereria bancrofti infection is transmitted by Culex quinquefasciatus, the models give similar predictions of the coverage and number of treatment rounds required to bring microfilaraemia prevalence below a level of 0.5%. Nevertheless, published estimates of the duration of mass treatment required for elimination differed, due to the use of different indicators for elimination (EPIFIL: microfilaraemia prevalence < 0.5% after the last treatment; LYMFASIM: reduction of microfilaraemia prevalence to zero, within 40 years after the start of mass treatment). The two main challenges for future modelling work are: 1) quantification and validation of the models for other regions, for investigation of elimination prospects in situations with other vector-parasite combinations and endemicity levels than in Pondicherry; 2) application of the models to address a range of programmatic issues related to the monitoring and evaluation of ongoing control programmes. The models' usefulness could be enhanced by several extensions; inclusion of different diagnostic tests and natural history of disease in the models is of particular relevance.
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spelling pubmed-14482032006-04-27 Advances and challenges in predicting the impact of lymphatic filariasis elimination programmes by mathematical modelling Stolk, Wilma A de Vlas, Sake J Habbema, J Dik F Filaria J Review Mathematical simulation models for transmission and control of lymphatic filariasis are useful tools for studying the prospects of lymphatic filariasis elimination. Two simulation models are currently being used. The first, EPIFIL, is a population-based, deterministic model that simulates average trends in infection intensity over time. The second, LYMFASIM, is an individual-based, stochastic model that simulates acquisition and loss of infection for each individual in the simulated population, taking account of individual characteristics. For settings like Pondicherry (India), where Wuchereria bancrofti infection is transmitted by Culex quinquefasciatus, the models give similar predictions of the coverage and number of treatment rounds required to bring microfilaraemia prevalence below a level of 0.5%. Nevertheless, published estimates of the duration of mass treatment required for elimination differed, due to the use of different indicators for elimination (EPIFIL: microfilaraemia prevalence < 0.5% after the last treatment; LYMFASIM: reduction of microfilaraemia prevalence to zero, within 40 years after the start of mass treatment). The two main challenges for future modelling work are: 1) quantification and validation of the models for other regions, for investigation of elimination prospects in situations with other vector-parasite combinations and endemicity levels than in Pondicherry; 2) application of the models to address a range of programmatic issues related to the monitoring and evaluation of ongoing control programmes. The models' usefulness could be enhanced by several extensions; inclusion of different diagnostic tests and natural history of disease in the models is of particular relevance. BioMed Central 2006-03-28 /pmc/articles/PMC1448203/ /pubmed/16569234 http://dx.doi.org/10.1186/1475-2883-5-5 Text en Copyright © 2006 Stolk et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review
Stolk, Wilma A
de Vlas, Sake J
Habbema, J Dik F
Advances and challenges in predicting the impact of lymphatic filariasis elimination programmes by mathematical modelling
title Advances and challenges in predicting the impact of lymphatic filariasis elimination programmes by mathematical modelling
title_full Advances and challenges in predicting the impact of lymphatic filariasis elimination programmes by mathematical modelling
title_fullStr Advances and challenges in predicting the impact of lymphatic filariasis elimination programmes by mathematical modelling
title_full_unstemmed Advances and challenges in predicting the impact of lymphatic filariasis elimination programmes by mathematical modelling
title_short Advances and challenges in predicting the impact of lymphatic filariasis elimination programmes by mathematical modelling
title_sort advances and challenges in predicting the impact of lymphatic filariasis elimination programmes by mathematical modelling
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1448203/
https://www.ncbi.nlm.nih.gov/pubmed/16569234
http://dx.doi.org/10.1186/1475-2883-5-5
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