1. Malaria temporal dynamic clustering for surveillance and intervention planning
- Author
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Legendre, E., Lehot, L., Dieng, S., Rebaudet, S., Thu, A. M., Rae, J. D., Delmas, G., Girond, F., Herbreteau, Vincent, Nosten, F., Landier, Jordi, Gaudart, J., Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale (SESSTIM - U1252 INSERM - Aix Marseille Univ - UMR 259 IRD), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut des sciences de la santé publique [Marseille] (ISSPAM), Hôpital Européen [Fondation Ambroise Paré - Marseille], Mahidol Oxford Tropical Medicine Research Unit, University of Oxford-Mahidol University [Bangkok], Mahidol University [Bangkok], University of Oxford, UMR Espace-Dev Guyane, Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Guyane (UG)-Université des Antilles (UA)-Université de Montpellier (UM), Institut Pasteur du Cambodge, Réseau International des Instituts Pasteur (RIIP), Hôpital de la Timone [CHU - APHM] (TIMONE), Biostatistique et technologies de l'information et de la communication (BioSTIC) - [Hôpital de la Timone - APHM] (BiosTIC ), and Assistance Publique - Hôpitaux de Marseille (APHM)- Hôpital de la Timone [CHU - APHM] (TIMONE)
- Subjects
Seasonal malaria ,Infectious Diseases ,[SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases ,Epidemiology ,Virology ,[SDE]Environmental Sciences ,Public Health, Environmental and Occupational Health ,Temporal dynamics ,Parasitology ,Microbiology ,Clustering - Abstract
BackgroundTargeting 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.MethodsWe 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.ResultsWe included 1,115 villages from 2016 to 2020. We identified elevenP. falciparumandP. vivaxincidence clusters which differed by amplitude, trends and seasonality. Specifically the 124 villages classified as “high transmission area” in the standardP. falciparumstratification belonged to the 11 distinct groups when accounting to inter-annual trends and intra-annual variations. Likewise forP. vivax, 399 “high transmission” villages actually corresponded to the 11 distinct dynamics.ConclusionOur 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.FundingThe Bill & Melinda Gates Foundation, The Global Fund against AIDS, Tuberculosis and Malaria (the Regional Artemisinin Initiative) and the Wellcome Trust funded the METF program.
- Published
- 2023