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Malaria temporal dynamic clustering for surveillance and intervention planning

BACKGROUND: Targeting interventions where most needed and effective is crucial for public health. Malaria control and elimination strategies increasingly rely on stratification to guide surveillance, to allocate vector control campaigns, and to prioritize access to community-based early diagnosis an...

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Autores principales: Legendre, Eva, Lehot, Laurent, Dieng, Sokhna, Rebaudet, Stanislas, Thu, Aung Myint, Rae, Jade D., Delmas, Gilles, Girond, Florian, Herbreteau, Vincent, Nosten, François, Landier, Jordi, Gaudart, Jean
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280692/
https://www.ncbi.nlm.nih.gov/pubmed/37004429
http://dx.doi.org/10.1016/j.epidem.2023.100682
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author Legendre, Eva
Lehot, Laurent
Dieng, Sokhna
Rebaudet, Stanislas
Thu, Aung Myint
Rae, Jade D.
Delmas, Gilles
Girond, Florian
Herbreteau, Vincent
Nosten, François
Landier, Jordi
Gaudart, Jean
author_facet Legendre, Eva
Lehot, Laurent
Dieng, Sokhna
Rebaudet, Stanislas
Thu, Aung Myint
Rae, Jade D.
Delmas, Gilles
Girond, Florian
Herbreteau, Vincent
Nosten, François
Landier, Jordi
Gaudart, Jean
author_sort Legendre, Eva
collection PubMed
description BACKGROUND: Targeting interventions where most needed and effective is crucial for public health. Malaria control and elimination strategies increasingly rely on stratification to guide surveillance, to allocate vector control campaigns, and to prioritize access to community-based early diagnosis and treatment (EDT). We developed an original approach of dynamic clustering to improve local discrimination between heterogeneous malaria transmission settings. METHODS: We analysed weekly malaria incidence records obtained from community-based EDT (malaria posts) in Karen/Kayin state, Myanmar. We smoothed longitudinal incidence series over multiple seasons using functional transformation. We regrouped village incidence series into clusters using a dynamic time warping clustering and compared them to the standard, 5-category annual incidence standard stratification. RESULTS: We included 1115 villages from 2016 to 2020. We identified eleven P. falciparum and P. vivax incidence clusters which differed by amplitude, trends and seasonality. Specifically the 124 villages classified as “high transmission area” in the standard P. falciparum stratification belonged to the 11 distinct groups when accounting to inter-annual trends and intra-annual variations. Likewise for P. vivax, 399 “high transmission” villages actually corresponded to the 11 distinct dynamics. CONCLUSION: Our temporal dynamic clustering methodology is easy to implement and extracts more information than standard malaria stratification. Our method exploits longitudinal surveillance data to distinguish local dynamics, such as increasing inter-annual trends or seasonal differences, providing key information for decision-making. It is relevant to malaria strategies in other settings and to other diseases, especially when many countries deploy health information systems and collect increasing amounts of health outcome data. FUNDING: The Bill & Melinda Gates Foundation, The Global Fund against AIDS, Tuberculosis and Malaria (the Regional Artemisinin Initiative) and the Wellcome Trust funded the METF program.
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spelling pubmed-102806922023-06-21 Malaria temporal dynamic clustering for surveillance and intervention planning Legendre, Eva Lehot, Laurent Dieng, Sokhna Rebaudet, Stanislas Thu, Aung Myint Rae, Jade D. Delmas, Gilles Girond, Florian Herbreteau, Vincent Nosten, François Landier, Jordi Gaudart, Jean Epidemics Article BACKGROUND: Targeting interventions where most needed and effective is crucial for public health. Malaria control and elimination strategies increasingly rely on stratification to guide surveillance, to allocate vector control campaigns, and to prioritize access to community-based early diagnosis and treatment (EDT). We developed an original approach of dynamic clustering to improve local discrimination between heterogeneous malaria transmission settings. METHODS: We analysed weekly malaria incidence records obtained from community-based EDT (malaria posts) in Karen/Kayin state, Myanmar. We smoothed longitudinal incidence series over multiple seasons using functional transformation. We regrouped village incidence series into clusters using a dynamic time warping clustering and compared them to the standard, 5-category annual incidence standard stratification. RESULTS: We included 1115 villages from 2016 to 2020. We identified eleven P. falciparum and P. vivax incidence clusters which differed by amplitude, trends and seasonality. Specifically the 124 villages classified as “high transmission area” in the standard P. falciparum stratification belonged to the 11 distinct groups when accounting to inter-annual trends and intra-annual variations. Likewise for P. vivax, 399 “high transmission” villages actually corresponded to the 11 distinct dynamics. CONCLUSION: Our temporal dynamic clustering methodology is easy to implement and extracts more information than standard malaria stratification. Our method exploits longitudinal surveillance data to distinguish local dynamics, such as increasing inter-annual trends or seasonal differences, providing key information for decision-making. It is relevant to malaria strategies in other settings and to other diseases, especially when many countries deploy health information systems and collect increasing amounts of health outcome data. FUNDING: The Bill & Melinda Gates Foundation, The Global Fund against AIDS, Tuberculosis and Malaria (the Regional Artemisinin Initiative) and the Wellcome Trust funded the METF program. Elsevier 2023-06 /pmc/articles/PMC10280692/ /pubmed/37004429 http://dx.doi.org/10.1016/j.epidem.2023.100682 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Legendre, Eva
Lehot, Laurent
Dieng, Sokhna
Rebaudet, Stanislas
Thu, Aung Myint
Rae, Jade D.
Delmas, Gilles
Girond, Florian
Herbreteau, Vincent
Nosten, François
Landier, Jordi
Gaudart, Jean
Malaria temporal dynamic clustering for surveillance and intervention planning
title Malaria temporal dynamic clustering for surveillance and intervention planning
title_full Malaria temporal dynamic clustering for surveillance and intervention planning
title_fullStr Malaria temporal dynamic clustering for surveillance and intervention planning
title_full_unstemmed Malaria temporal dynamic clustering for surveillance and intervention planning
title_short Malaria temporal dynamic clustering for surveillance and intervention planning
title_sort malaria temporal dynamic clustering for surveillance and intervention planning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280692/
https://www.ncbi.nlm.nih.gov/pubmed/37004429
http://dx.doi.org/10.1016/j.epidem.2023.100682
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