أعرض في EDS

Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016-2017

التفاصيل البيبلوغرافية
العنوان: Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016-2017
المؤلفون: D. Sokhna, A. Som eacute, Mady Cissoko, J. Gaudart, Gauthier Tougri, Boukary Ouedraogo, Roch K. Dabiré, Cédric Pennetier, S. Diloma Dieudonn eacute, Alphonsine A. Koffi, Issaka Zongo, Nicolas Moiroux, Cédric S. Bationo, P. Taconet
المساهمون: 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), Vector Control Group (MIVEGEC-VCG), Evolution des Systèmes Vectoriels (ESV), Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université des sciences, des techniques et des technologies de Bamako, Université des sciences, des techniques et des technologies de Bamako (USTTB), Institut de Recherche en Sciences de la Santé (IRSS), CNRST, Institut Pierre Richet (IPR), Université des Sciences, des Techniques et des Technologies de Bamako (USTTB), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Ministère de la Santé [Burkina Faso], Programme National de Lutte contre le Paludisme (PNLP), Génétique et évolution des maladies infectieuses (GEMI), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Génétique et évolution des maladies infectieuses (GEMI), Moiroux, Nicolas
المصدر: Scientific Reports
Scientific Reports, 2021, 11, pp.20027. ⟨10.1038/s41598-021-99457-9⟩
Scientific Reports, Nature Publishing Group, 2021, 11, pp.20027. ⟨10.1038/s41598-021-99457-9⟩
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
بيانات النشر: HAL CCSD, 2021.
سنة النشر: 2021
مصطلحات موضوعية: Wet season, Multivariate statistics, Multivariate analysis, Epidemiology, Science, 030231 tropical medicine, Spatial distribution, Article, 03 medical and health sciences, 0302 clinical medicine, Meteorology, Spatio-Temporal Analysis, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Burkina Faso, medicine, [SDV.EE.SANT] Life Sciences [q-bio]/Ecology, environment/Health, Humans, 030212 general & internal medicine, Remote sensing, [SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health, Incidence (epidemiology), Incidence, 1. No poverty, Outbreak, medicine.disease, 3. Good health, Malaria, Geography, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Relative risk, Remote Sensing Technology, [SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Medicine, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
الوصف: BackgroundMalaria control and prevention programs are more efficient and cost-effective when they target hotspots or select the best periods of year to implement interventions. This study aimed to identify the spatial distribution of malaria hotspots at the village level in Diébougou health district, Burkina Faso, and to model the temporal dynamics of malaria cases as a function of meteorological conditions and of the distance between villages and health centers (HCs).MethodsCase data for 27 villages were collected in 13 HCs using continuous passive case detection. Meteorological data were obtained through remote sensing. Two synthetic meteorological indicators (SMIs) were created to summarize meteorological variables. Spatial hotspots were detected using the Kulldorf scanning method. A General Additive Model was used to determine the time lag between cases and SMIs and to evaluate the effect of SMIs and distance to HC on the temporal evolution of malaria cases. The multivariate model was fitted with data from the epidemic year to predict the number of cases in the following outbreak.ResultsOverall, the incidence rate in the area was 429.13 cases per 1,000 person-year with important spatial and temporal heterogeneities. Four spatial hotspots, involving 7 of the 27 villages, were detected, for an incidence rate of 854.02 cases per 1,000 person-year. The hotspot with the highest risk (relative risk = 4.06) consisted of a single village, with an incidence rate of 1,750.75 cases per 1,000 person-years. The multivariate analysis found greater variability in incidence between HCs than between villages linked to the same HC. The epidemic year was characterized by a major peak during the second part of the rainy season and a secondary peak during the dry-hot season. The time lag that generated the better predictions of cases was 9 weeks for SMI1 (positively correlated with precipitation variables and associated with the first peak of cases) and 16 weeks for SMI2 (positively correlated with temperature variables and associated with the secondary peak of cases). Euclidian distance to HC was not found to be a predictor of malaria cases recorded in HC. The prediction followed the overall pattern of the time series of reported cases and predicted the onset of the following outbreak with a precision of less than 3 weeks.ConclusionsOur spatio-temporal analysis of malaria cases in Diébougou health district, Burkina Faso, provides a powerful prospective method for identifying and predicting high-risk areas and high-transmission periods that could be targeted in future malaria control and prevention campaigns.
وصف الملف: application/pdf
اللغة: English
تدمد: 2045-2322
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::625da445292a9ca7aada025c602cd114
https://hal.archives-ouvertes.fr/hal-03202020
حقوق: OPEN
رقم الأكسشن: edsair.doi.dedup.....625da445292a9ca7aada025c602cd114
قاعدة البيانات: OpenAIRE
الوصف
تدمد:20452322