Using Bayesian state-space models to understand the population dynamics of the dominant malaria vector, Anopheles funestus in rural Tanzania.

Ngowo, Halfan S; Okumu, Fredros O; Hape, Emmanuel E; Mshani, Issa H; Ferguson, Heather M; Matthiopoulos, Jason

Ngowo, Halfan S; Okumu, Fredros O; Hape, Emmanuel E; Mshani, Issa H; Ferguson, Heather M; Matthiopoulos, Jason.

Afiliación

Ngowo HS; Department of Environmental Health & Ecological Sciences, Ifakara Health Institute, Ifakara, Tanzania. hngowo@ihi.or.tz.
Okumu FO; Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK. hngowo@ihi.or.tz.
Hape EE; Department of Environmental Health & Ecological Sciences, Ifakara Health Institute, Ifakara, Tanzania.
Mshani IH; Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK.
Ferguson HM; School of Public Health, University of the Witwatersrand, Braamfontein, Republic of South Africa.
Matthiopoulos J; School of Life Science and Bioengineering, Nelson Mandela African Institution of Science & Technology, Arusha, Tanzania.

Malar J ; 21(1): 161, 2022 Jun 03.

Article en En | MEDLINE | ID: mdl-35658961

Asunto(s)

Anopheles; Malaria; Animales; Anopheles/fisiología; Teorema de Bayes; Femenino; Malaria/prevención & control; Mosquitos Vectores/fisiología; Dinámica Poblacional; Tanzanía

Palabras clave

Abundance; Anopheles funestus; Density dependence; Population dynamic; Seasonality; State space model

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Malaria / Anopheles Tipo de estudio: Prognostic_studies Límite: Animals País/Región como asunto: Africa Idioma: En Revista: Malar J Asunto de la revista: MEDICINA TROPICAL Año: 2022 Tipo del documento: Article País de afiliación: Tanzania

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