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From serological surveys to disease burden: a modelling pipeline for Chagas disease
In 2012, the World Health Organization (WHO) set the elimination of Chagas disease intradomiciliary vectorial transmission as a goal by 2020. After a decade, some progress has been made, but the new 2021–2030 WHO roadmap has set even more ambitious targets. Innovative and robust modelling methods ar...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10440172/ https://www.ncbi.nlm.nih.gov/pubmed/37598701 http://dx.doi.org/10.1098/rstb.2022.0278 |
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author | Ledien, Julia Cucunubá, Zulma M. Parra-Henao, Gabriel Rodríguez-Monguí, Eliana Dobson, Andrew P. Adamo, Susana B. Castellanos, Luis Gerardo Basáñez, María-Gloria Nouvellet, Pierre |
author_facet | Ledien, Julia Cucunubá, Zulma M. Parra-Henao, Gabriel Rodríguez-Monguí, Eliana Dobson, Andrew P. Adamo, Susana B. Castellanos, Luis Gerardo Basáñez, María-Gloria Nouvellet, Pierre |
author_sort | Ledien, Julia |
collection | PubMed |
description | In 2012, the World Health Organization (WHO) set the elimination of Chagas disease intradomiciliary vectorial transmission as a goal by 2020. After a decade, some progress has been made, but the new 2021–2030 WHO roadmap has set even more ambitious targets. Innovative and robust modelling methods are required to monitor progress towards these goals. We present a modelling pipeline using local seroprevalence data to obtain national disease burden estimates by disease stage. Firstly, local seroprevalence information is used to estimate spatio-temporal trends in the Force-of-Infection (FoI). FoI estimates are then used to predict such trends across larger and fine-scale geographical areas. Finally, predicted FoI values are used to estimate disease burden based on a disease progression model. Using Colombia as a case study, we estimated that the number of infected people would reach 506 000 (95% credible interval (CrI) = 395 000–648 000) in 2020 with a 1.0% (95%CrI = 0.8–1.3%) prevalence in the general population and 2400 (95%CrI = 1900–3400) deaths (approx. 0.5% of those infected). The interplay between a decrease in infection exposure (FoI and relative proportion of acute cases) was overcompensated by a large increase in population size and gradual population ageing, leading to an increase in the absolute number of Chagas disease cases over time. This article is part of the theme issue ‘Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs’. |
format | Online Article Text |
id | pubmed-10440172 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-104401722023-08-21 From serological surveys to disease burden: a modelling pipeline for Chagas disease Ledien, Julia Cucunubá, Zulma M. Parra-Henao, Gabriel Rodríguez-Monguí, Eliana Dobson, Andrew P. Adamo, Susana B. Castellanos, Luis Gerardo Basáñez, María-Gloria Nouvellet, Pierre Philos Trans R Soc Lond B Biol Sci Articles In 2012, the World Health Organization (WHO) set the elimination of Chagas disease intradomiciliary vectorial transmission as a goal by 2020. After a decade, some progress has been made, but the new 2021–2030 WHO roadmap has set even more ambitious targets. Innovative and robust modelling methods are required to monitor progress towards these goals. We present a modelling pipeline using local seroprevalence data to obtain national disease burden estimates by disease stage. Firstly, local seroprevalence information is used to estimate spatio-temporal trends in the Force-of-Infection (FoI). FoI estimates are then used to predict such trends across larger and fine-scale geographical areas. Finally, predicted FoI values are used to estimate disease burden based on a disease progression model. Using Colombia as a case study, we estimated that the number of infected people would reach 506 000 (95% credible interval (CrI) = 395 000–648 000) in 2020 with a 1.0% (95%CrI = 0.8–1.3%) prevalence in the general population and 2400 (95%CrI = 1900–3400) deaths (approx. 0.5% of those infected). The interplay between a decrease in infection exposure (FoI and relative proportion of acute cases) was overcompensated by a large increase in population size and gradual population ageing, leading to an increase in the absolute number of Chagas disease cases over time. This article is part of the theme issue ‘Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs’. The Royal Society 2023-10-09 2023-08-21 /pmc/articles/PMC10440172/ /pubmed/37598701 http://dx.doi.org/10.1098/rstb.2022.0278 Text en This article is ©2023 https://royals-ociety.org/-/media/journals/author/Licence-to-Publish-20062019-final.pdfhttps://royalsociety.org/journals/ethics-policies/data-sharing-mining/Pan American Health Organization, Regional Office for the Americas of the World Health Organization. This is an open access article distributed under the terms of the Creative Commons Attribution IGO License (http://creativecommons.org/licenses/by/3.0/igo/legalcode (https://creativecommons.org/licenses/by/3.0/igo/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Ledien, Julia Cucunubá, Zulma M. Parra-Henao, Gabriel Rodríguez-Monguí, Eliana Dobson, Andrew P. Adamo, Susana B. Castellanos, Luis Gerardo Basáñez, María-Gloria Nouvellet, Pierre From serological surveys to disease burden: a modelling pipeline for Chagas disease |
title | From serological surveys to disease burden: a modelling pipeline for Chagas disease |
title_full | From serological surveys to disease burden: a modelling pipeline for Chagas disease |
title_fullStr | From serological surveys to disease burden: a modelling pipeline for Chagas disease |
title_full_unstemmed | From serological surveys to disease burden: a modelling pipeline for Chagas disease |
title_short | From serological surveys to disease burden: a modelling pipeline for Chagas disease |
title_sort | from serological surveys to disease burden: a modelling pipeline for chagas disease |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10440172/ https://www.ncbi.nlm.nih.gov/pubmed/37598701 http://dx.doi.org/10.1098/rstb.2022.0278 |
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