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      Geo-Epidemiology of Malaria at the Health Area Level, Dire Health District, Mali, 2013–2017

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          Abstract

          Background: According to the World Health Organization, there were more than 228 million cases of malaria globally in 2018, with 93% of cases occurring in Africa; in Mali, a 13% increase in the number of cases was observed between 2015 and 2018; this study aimed to evaluate the impact of meteorological and environmental factors on the geo-epidemiology of malaria in the health district of Dire, Mali. Methods: Meteorological and environmental variables were synthesized using principal component analysis and multiple correspondence analysis, the relationship between malaria incidence and synthetic indicators was determined using a multivariate general additive model; hotspots were detected by SaTScan. Results: Malaria incidence showed high inter and intra-annual variability; the period of high transmission lasted from September to February; health areas characterized by proximity to the river, propensity for flooding and high agricultural yield were the most at risk, with an incidence rate ratio of 2.21 with confidence intervals (95% CI: 1.85–2.58); malaria incidence in Dire declined from 120 to 20 cases per 10,000 person-weeks between 2013 and 2017. Conclusion: The identification of areas and periods of high transmission can help improve malaria control strategies.

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          Smoothing Parameter and Model Selection for General Smooth Models

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            Space-time clustering of childhood malaria at the household level: a dynamic cohort in a Mali village

            Background Spatial and temporal heterogeneities in the risk of malaria have led the WHO to recommend fine-scale stratification of the epidemiological situation, making it possible to set up actions and clinical or basic researches targeting high-risk zones. Before initiating such studies it is necessary to define local patterns of malaria transmission and infection (in time and in space) in order to facilitate selection of the appropriate study population and the intervention allocation. The aim of this study was to identify, spatially and temporally, high-risk zones of malaria, at the household level (resolution of 1 to 3 m). Methods This study took place in a Malian village with hyperendemic seasonal transmission as part of Mali-Tulane Tropical Medicine Research Center (NIAID/NIH). The study design was a dynamic cohort (22 surveys, from June 1996 to June 2001) on about 1300 children (<12 years) distributed between 173 households localized by GPS. We used the computed parasitological data to analyzed levels of Plasmodium falciparum, P. malariae and P. ovale infection and P. falciparum gametocyte carriage by means of time series and Kulldorff's scan statistic for space-time cluster detection. Results The time series analysis determined that malaria parasitemia (primarily P. falciparum) was persistently present throughout the population with the expected seasonal variability pattern and a downward temporal trend. We identified six high-risk clusters of P. falciparum infection, some of which persisted despite an overall tendency towards a decrease in risk. The first high-risk cluster of P. falciparum infection (rate ratio = 14.161) was detected from September 1996 to October 1996, in the north of the village. Conclusion This study showed that, although infection proportions tended to decrease, high-risk zones persisted in the village particularly near temporal backwaters. Analysis of this heterogeneity at the household scale by GIS methods lead to target preventive actions more accurately on the high-risk zones identified. This mapping of malaria risk makes it possible to orient control programs, treating the high-risk zones identified as a matter of priority, and to improve the planning of intervention trials or research studies on malaria.
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              Rice volatiles lure gravid malaria mosquitoes, Anopheles arabiensis

              Mosquito oviposition site selection is essential for vector population dynamics and malaria epidemiology. Irrigated rice cultivations provide ideal larval habitats for malaria mosquitoes, which has resulted in increased prevalence of the malaria vector, Anopheles arabiensis, in sub-Saharan Africa. The nature and origin of the cues regulating this behaviour are only now being elucidated. We show that gravid Anopheles arabiensis are attracted and oviposit in response to the odour present in the air surrounding rice. Furthermore, we identify a synthetic rice odour blend, using electrophysiological and chemical analyses, which elicits attraction and oviposition in laboratory assays, as well as attraction of free-flying gravid mosquitoes under semi-field conditions. This research highlights the intimate link between malaria vectors and agriculture. The identified volatile cues provide important substrates for the development of novel and cost-effective control measures that target female malaria mosquitoes, irrespective of indoor or outdoor feeding and resting patterns.
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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                04 June 2020
                June 2020
                : 17
                : 11
                : 3982
                Affiliations
                [1 ]Malaria Research and Training Center—Ogobara K. Doumbo (MRTC-OKD), FMOS-FAPH, Mali-NIAID-ICER, Université des Sciences, des Techniques et des Technologies de Bamako, Bamako 1805, Mali; isagara@ 123456icermali.org (I.S.); aguindo15@ 123456yahoo.fr (A.G.); jean.gaudart@ 123456univ-amu.fr (J.G.)
                [2 ]Aix Marseille Université (AMU), Institut national de la santé et de la recherche médicale (INSERM), Institut de Recherche pour le Développement (IRD), 13005 Marseille, France; melmadine@ 123456hotmail.com (S.D.); marc-karim.bendiane@ 123456inserm.fr (M.K.B.); jordi.landier@ 123456ird.fr (J.L.)
                [3 ]Direction Régionale de la Santé de Tombouctou, Tombouctou 59, Mali; tondifarma1@ 123456yahoo.fr (M.H.S.); zoumana.doumbia@ 123456yahoo.fr (Z.D.); allousmed@ 123456yahoo.fr (B.A.)
                [4 ]Mère et Enfant face aux Infections Tropicales (MERIT), IRD, Université Paris 5, 75006 Paris, France
                [5 ]Programme National de la Lutte contre le Paludisme (PNLP Mali), Bamako 233, Mali; dtkone@ 123456hotmail.fr (D.T.); drfomba@ 123456hotmail.fr (S.F.)
                [6 ]ESPACE-DEV, UMR228 IRD/UM/UR/UG/UA, Institut de Recherche pour le Développement (IRD), 34093 Montpellier, France; nadine.dessay@ 123456ird.fr
                [7 ]Aix Marseille Université, APHM, INSERM, IRD, SESSTIM, Hop Timone, BioSTIC, Biostatistic & ICT, 13005 Marseille, France
                Author notes
                Author information
                https://orcid.org/0000-0002-0200-8191
                https://orcid.org/0000-0001-9006-5729
                Article
                ijerph-17-03982
                10.3390/ijerph17113982
                7312793
                32512740
                28ec510c-f1d5-4965-a90a-b32aa5a5f018
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 21 April 2020
                : 26 May 2020
                Categories
                Article

                Public health
                malaria,hotspot and incidence,spatio-temporal analysis,environment and meteorology,geo-epidemiology

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