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                    "firstName": "Diamantina",
                    "lastName": "Moreno-Gutierrez"
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                    "creatorType": "author",
                    "firstName": "Philippe",
                    "lastName": "Lejeune"
                },
                {
                    "creatorType": "author",
                    "firstName": "Alejandro",
                    "lastName": "Llanos-Cuentas"
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                    "creatorType": "author",
                    "firstName": "Niko",
                    "lastName": "Speybroeck"
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                    "creatorType": "author",
                    "firstName": "Marie-Pierre",
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                },
                {
                    "creatorType": "author",
                    "firstName": "Angel",
                    "lastName": "Rosas-Aguirre"
                }
            ],
            "abstractNote": "Abstract\n            \n              This is the first study to assess the risk of co-endemic\n              Plasmodium vivax\n              and\n              Plasmodium falciparum\n              transmission in the Peruvian Amazon using boosted regression tree (BRT) models based on social and environmental predictors derived from satellite imagery and data. Yearly cross-validated BRT models were created to discriminate high-risk (annual parasite index API > 10 cases/1000 people) and very-high-risk for malaria (API > 50 cases/1000 people) in 2766 georeferenced villages of Loreto department, between 2010–2017 as other parts in the article (graphs, tables, and texts). Predictors were cumulative annual rainfall, forest coverage, annual forest loss, annual mean land surface temperature, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), shortest distance to rivers, time to populated villages, and population density. BRT models built with predictor data of a given year efficiently discriminated the malaria risk for that year in villages (area under the ROC curve (AUC) > 0.80), and most models also effectively predicted malaria risk in the following year. Cumulative rainfall, population density and time to populated villages were consistently the top three predictors for both\n              P. vivax\n              and\n              P. falciparum\n              incidence. Maps created using the BRT models characterize the spatial distribution of the malaria incidence in Loreto and should contribute to malaria-related decision making in the area.",
            "publicationTitle": "Scientific Reports",
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            "place": "",
            "date": "2019-10-23",
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            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "15173",
            "series": "",
            "seriesTitle": "",
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            "journalAbbreviation": "Sci Rep",
            "DOI": "10.1038/s41598-019-51564-4",
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            "url": "https://www.nature.com/articles/s41598-019-51564-4",
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            "language": "en",
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            "title": "The biting and resting behavior of Anopheles albimanus in northern Haiti",
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                {
                    "creatorType": "author",
                    "firstName": "J. H.",
                    "lastName": "Hobbs"
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                    "creatorType": "author",
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                    "lastName": "Sexton"
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                    "firstName": "Y.",
                    "lastName": "St Jean"
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                    "creatorType": "author",
                    "firstName": "J. R.",
                    "lastName": "Jacques"
                }
            ],
            "abstractNote": "A one-year study of the biting and resting habits of the malaria vector Anopheles albimanus was carried out in four rural villages of northern Haiti. Man-biting rates and nightly biting cycles were determined by the use of all-night man-biting captures inside and outside houses. Seasonal changes in density and behavior were determined by repeating the captures on a bimonthly basis throughout one year. Exophily was demonstrated in these anopheline populations by a comparison of inside-biting with inside-resting densities. These behavior characteristics are discussed in relation to malaria transmission and to the choice of malaria control methods.",
            "publicationTitle": "Journal of the American Mosquito Control Association",
            "publisher": "",
            "place": "",
            "date": "1986-06",
            "volume": "2",
            "issue": "2",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "150-153",
            "series": "",
            "seriesTitle": "",
            "seriesText": "",
            "journalAbbreviation": "J Am Mosq Control Assoc",
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            "ISSN": "8756-971X",
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            "extra": "PMID: 3507484",
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                    "type": 1
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                    "type": 1
                },
                {
                    "tag": "Feeding Behavior",
                    "type": 1
                },
                {
                    "tag": "Haiti",
                    "type": 1
                },
                {
                    "tag": "Humans",
                    "type": 1
                },
                {
                    "tag": "Insect Bites and Stings",
                    "type": 1
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                    "type": 1
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            "creatorSummary": "McMahon et al.",
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            "version": 3002,
            "itemType": "journalArticle",
            "title": "Remote sensing of environmental risk factors for malaria in different geographic contexts",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Andrea",
                    "lastName": "McMahon"
                },
                {
                    "creatorType": "author",
                    "firstName": "Abere",
                    "lastName": "Mihretie"
                },
                {
                    "creatorType": "author",
                    "firstName": "Adem Agmas",
                    "lastName": "Ahmed"
                },
                {
                    "creatorType": "author",
                    "firstName": "Mastewal",
                    "lastName": "Lake"
                },
                {
                    "creatorType": "author",
                    "firstName": "Worku",
                    "lastName": "Awoke"
                },
                {
                    "creatorType": "author",
                    "firstName": "Michael Charles",
                    "lastName": "Wimberly"
                }
            ],
            "abstractNote": "Abstract\n            \n              Background\n              Despite global intervention efforts, malaria remains a major public health concern in many parts of the world. Understanding geographic variation in malaria patterns and their environmental determinants can support targeting of malaria control and development of elimination strategies.\n            \n            \n              Methods\n              \n                We used remotely sensed environmental data to analyze the influences of environmental risk factors on malaria cases caused by\n                Plasmodium falciparum\n                and\n                Plasmodium vivax\n                from 2014 to 2017 in two geographic settings in Ethiopia. Geospatial datasets were derived from multiple sources and characterized climate, vegetation, land use, topography, and surface water. All data were summarized annually at the sub-district (\n                kebele\n                ) level for each of the two study areas. We analyzed the associations between environmental data and malaria cases with Boosted Regression Tree (BRT) models.\n              \n            \n            \n              Results\n              We found considerable spatial variation in malaria occurrence. Spectral indices related to land cover greenness (NDVI) and moisture (NDWI) showed negative associations with malaria, as the highest malaria rates were found in landscapes with low vegetation cover and moisture during the months that follow the rainy season. Climatic factors, including precipitation and land surface temperature, had positive associations with malaria. Settlement structure also played an important role, with different effects in the two study areas. Variables related to surface water, such as irrigated agriculture, wetlands, seasonally flooded waterbodies, and height above nearest drainage did not have strong influences on malaria.\n            \n            \n              Conclusion\n              We found different relationships between malaria and environmental conditions in two geographically distinctive areas. These results emphasize that studies of malaria-environmental relationships and predictive models of malaria occurrence should be context specific to account for such differences.",
            "publicationTitle": "International Journal of Health Geographics",
            "publisher": "",
            "place": "",
            "date": "12/2021",
            "volume": "20",
            "issue": "1",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "28",
            "series": "",
            "seriesTitle": "",
            "seriesText": "",
            "journalAbbreviation": "Int J Health Geogr",
            "DOI": "10.1186/s12942-021-00282-0",
            "citationKey": "",
            "url": "https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-021-00282-0",
            "accessDate": "2025-12-17T16:17:44Z",
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            "PMCID": "",
            "ISSN": "1476-072X",
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            "shortTitle": "",
            "language": "en",
            "libraryCatalog": "DOI.org (Crossref)",
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            "dateAdded": "2025-12-17T16:17:44Z",
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