Micro-geographic risk factors for malarial infection

Abstract

Background: Knowledge of geography is integral to the study of insect-borne infectious disease such as malaria. This study was designed to evaluate whether geographic parameters are associated with malarial infection in the East Sepik province of Papua New Guinea (PNG), a remote area where malaria is a major cause of morbidity and mortality.

Hydrological and geomorphological controls of malaria transmission

“…Bomblies et al (2008) found that such pools were the sole source of breeding habitats in a desert-fringe village in Niger and formed local hotspots for malaria risk. Despite reported flight ranges of anopheline vectors of several kilometres, heterogeneity in malaria risk at the village scale has been reported frequently Thomas and Lindsay, 2000;Myers et al, 2009).…”

“…Hay et al, 2000a;Snow et al, 2005;Guerra et al, 2006) to landscape (Carter et al, 2000) and village scale (e.g. Smith et al, 1995;Thomas and Lindsay, 2000;Gaudart et al, 2006;Myers et al, 2009). Moreover, local hotspots of high transmission are observed at all levels of transmission intensity (Bousema et al, 2012).…”

Mapping Malaria Risk in Bangladesh Using Bayesian Geostatistical Models

“…Maps of the median and lower 25% and upper 75% Bayesian credible interval for the posterior distributions of Pf PR [2][3][4][5][6][7][8][9][10] in Bangladesh are also presented in Figure 3 . The distribution of P. falciparum shows a large degree of heterogeneity ranging from 0% to 50%.…”

“…24 To help guide the program, a comprehensive malaria survey was conducted in malaria-endemic areas in Bangladesh in 2007. 25 The aim of this present study was to produce accurate, validated maps of predicted Plasmodium falciparum prevalence for 2-to 10-year-old children ( Pf PR [2][3][4][5][6][7][8][9][10] ) in the endemic areas of Bangladesh and identify high-risk populations at a precision useful for national and sub-national level control priority settings. The applicability of the methods and the implications of the results are discussed in the context of Bangladesh's malaria-control program, which faces challenges of highrisk areas in remote locations and the spread of multi-drugresistant P. falciparum .…”

“…[2][3][4][5][6][7][8][9] More recently, Bayesian geostatistics have been embraced for disease mapping, with the advantages that both environmental covariates and spatial autocorrelation are able to be estimated simultaneously and that full posterior distributions are produced, which can be used to quantify uncertainties in parameters of interest (e.g., predicted prevalence of infection). 10 Spatial prediction models have been used to produce malaria risk maps at national, 3,8,[11][12][13][14][15][16][17][18] sub-continental, 18,19 and global scales.…”