Using machine learning to understand microgeographic determinants of the Zika vector, Aedes aegypti.

Alexander, Jagger; Wilke, André Barretto Bruno; Mantero, Alejandro; Vasquez, Chalmers; Petrie, William; Kumar, Naresh; Beier, John C

Alexander, Jagger; Wilke, André Barretto Bruno; Mantero, Alejandro; Vasquez, Chalmers; Petrie, William; Kumar, Naresh; Beier, John C.

Afiliación

Alexander J; University of Miami Department of Public Health, Miami, FL, United States of America.
Wilke ABB; Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, United States of America.
Mantero A; University of Miami Department of Public Health, Miami, FL, United States of America.
Vasquez C; Miami-Dade County Mosquito Control Division, Miami, FL, United States of America.
Petrie W; Miami-Dade County Mosquito Control Division, Miami, FL, United States of America.
Kumar N; University of Miami Department of Public Health, Miami, FL, United States of America.
Beier JC; University of Miami Department of Public Health, Miami, FL, United States of America.

PLoS One ; 17(12): e0265472, 2022.

Article en En | MEDLINE | ID: mdl-36584050

Asunto(s)

Aedes; Infección por el Virus Zika; Virus Zika; Animales; Humanos; Florida/epidemiología; Mosquitos Vectores

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 2_ODS3 / 3_ND Problema de salud: 2_enfermedades_transmissibles / 3_dengue Asunto principal: Aedes / Virus Zika / Infección por el Virus Zika Tipo de estudio: Prognostic_studies Límite: Animals / Humans País/Región como asunto: America do norte Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

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