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Modelling Co-Infection with Malaria and Lymphatic Filariasis
Malaria and lymphatic filariasis (LF) continue to cause a considerable public health burden globally and are co-endemic in many regions of sub-Saharan Africa. These infections are transmitted by the same mosquito species which raises important questions about optimal vector control strategies in co-...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3681634/ https://www.ncbi.nlm.nih.gov/pubmed/23785271 http://dx.doi.org/10.1371/journal.pcbi.1003096 |
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author | Slater, Hannah C. Gambhir, Manoj Parham, Paul E. Michael, Edwin |
author_facet | Slater, Hannah C. Gambhir, Manoj Parham, Paul E. Michael, Edwin |
author_sort | Slater, Hannah C. |
collection | PubMed |
description | Malaria and lymphatic filariasis (LF) continue to cause a considerable public health burden globally and are co-endemic in many regions of sub-Saharan Africa. These infections are transmitted by the same mosquito species which raises important questions about optimal vector control strategies in co-endemic regions, as well as the effect of the presence of each infection on endemicity of the other; there is currently little consensus on the latter. The need for comprehensive modelling studies to address such questions is therefore significant, yet very few have been undertaken to date despite the recognised explanatory power of reliable dynamic mathematical models. Here, we develop a malaria-LF co-infection modelling framework that accounts for two key interactions between these infections, namely the increase in vector mortality as LF mosquito prevalence increases and the antagonistic Th1/Th2 immune response that occurs in co-infected hosts. We consider the crucial interplay between these interactions on the resulting endemic prevalence when introducing each infection in regions where the other is already endemic (e.g. due to regional environmental change), and the associated timescale for such changes, as well as effects on the basic reproduction number R(0) of each disease. We also highlight potential perverse effects of vector controls on human infection prevalence in co-endemic regions, noting that understanding such effects is critical in designing optimal integrated control programmes. Hence, as well as highlighting where better data are required to more reliably address such questions, we provide an important framework that will form the basis of future scenario analysis tools used to plan and inform policy decisions on intervention measures in different transmission settings. |
format | Online Article Text |
id | pubmed-3681634 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-36816342013-06-19 Modelling Co-Infection with Malaria and Lymphatic Filariasis Slater, Hannah C. Gambhir, Manoj Parham, Paul E. Michael, Edwin PLoS Comput Biol Research Article Malaria and lymphatic filariasis (LF) continue to cause a considerable public health burden globally and are co-endemic in many regions of sub-Saharan Africa. These infections are transmitted by the same mosquito species which raises important questions about optimal vector control strategies in co-endemic regions, as well as the effect of the presence of each infection on endemicity of the other; there is currently little consensus on the latter. The need for comprehensive modelling studies to address such questions is therefore significant, yet very few have been undertaken to date despite the recognised explanatory power of reliable dynamic mathematical models. Here, we develop a malaria-LF co-infection modelling framework that accounts for two key interactions between these infections, namely the increase in vector mortality as LF mosquito prevalence increases and the antagonistic Th1/Th2 immune response that occurs in co-infected hosts. We consider the crucial interplay between these interactions on the resulting endemic prevalence when introducing each infection in regions where the other is already endemic (e.g. due to regional environmental change), and the associated timescale for such changes, as well as effects on the basic reproduction number R(0) of each disease. We also highlight potential perverse effects of vector controls on human infection prevalence in co-endemic regions, noting that understanding such effects is critical in designing optimal integrated control programmes. Hence, as well as highlighting where better data are required to more reliably address such questions, we provide an important framework that will form the basis of future scenario analysis tools used to plan and inform policy decisions on intervention measures in different transmission settings. Public Library of Science 2013-06-13 /pmc/articles/PMC3681634/ /pubmed/23785271 http://dx.doi.org/10.1371/journal.pcbi.1003096 Text en © 2013 Slater et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Slater, Hannah C. Gambhir, Manoj Parham, Paul E. Michael, Edwin Modelling Co-Infection with Malaria and Lymphatic Filariasis |
title | Modelling Co-Infection with Malaria and Lymphatic Filariasis |
title_full | Modelling Co-Infection with Malaria and Lymphatic Filariasis |
title_fullStr | Modelling Co-Infection with Malaria and Lymphatic Filariasis |
title_full_unstemmed | Modelling Co-Infection with Malaria and Lymphatic Filariasis |
title_short | Modelling Co-Infection with Malaria and Lymphatic Filariasis |
title_sort | modelling co-infection with malaria and lymphatic filariasis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3681634/ https://www.ncbi.nlm.nih.gov/pubmed/23785271 http://dx.doi.org/10.1371/journal.pcbi.1003096 |
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