Spatially-explicit risk profiling of Plasmodium falciparum infections at a small scale: a geostatistical modelling approach
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* Corresponding author: Giovanna Raso g.raso@uq.edu.au
- Equal contributors
Malaria Journal 2008, 7:111 doi:10.1186/1475-2875-7-111
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Geo-additive modelling of malaria in Burundi Hermenegilde Nkurunziza, Albrecht Gebhardt, Jürgen Pilz Malaria Journal 2011, 10:234 (11 August 2011) Models suggest a strong positive association between malaria incidence in a given month and the minimum temperature of the previous month. Non-climatic variables include socio-economic conditions, food shortage, limited access to healthcare service, precarious housing, promiscuity, poor hygienic conditions, limited access to drinking water, land use and displacement of the population.
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Giovanna Raso, Kigbafori D Silué, Penelope Vounatsou, Burton H Singer, Ahoua Yapi, Marcel Tanner, Jürg Utzinger, Eliézer K N'Goran Malaria Journal 2009, 8:252 (11 November 2009) This paper looks at the spatial distribution of risk in a high endemicity area in Cote d’Ivoire using parasite prevalence data from school children and social and environmental co-variates. The authors use both stationary and non-stationary Bayesian geostatistical models to map disease distribution.
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Jean Gaudart, Ousmane Touré, Nadine Dessay, A lassane Dicko, Stéphane Ranque, Loic Forest, Jacques Demongeot, Ogobara K Doumbo Malaria Journal 2009, 8:61 (10 April 2009) This paper reports an ambitious attempt to produce a spatially structured model for malaria transmission in Mali, based on remote sensed vegetation indices (NDVI), together with an SIRS type transmission model, calibrated using data from the village of Bancoumana.
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Spatio-seasonal modeling of the incidence rate of malaria in Mozambique Rosa Abellana, Carlos Ascaso, John Aponte, Francisco Saute, Delino Nhalungo, Ariel Nhacolo, Pedro Alonso Malaria Journal 2008, 7:228 (31 October 2008) The paper offers important insights to the possibility of disentangling the effects of two important dimensions for malaria incidence studies: time and space. The use of spatial models to understand the relationship of the incidence of malaria, including spatial correlation nested to climatic season is interesting and appropriate.
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