Application of geographically-weighted regression analysis to assess risk factors for malaria hotspots in Keur Soce health and demographic surveillance site

Abstract

BackgroundIn Senegal, considerable efforts have been made to reduce malaria morbidity and mortality during the last decade. This resulted in a marked decrease of malaria cases. With the decline of malaria cases, transmission has become sparse in most Senegalese health districts. This study investigated malaria hotspots in Keur Soce sites by using geographically-weighted regression. Because of the occurrence of hotspots, spatial modelling of malaria cases could have a considerable effect in disease surveillance.MethodsThis study explored and analysed the spatial relationships between malaria occurrence and socio-economic and environmental factors in small communities in Keur Soce, Senegal, using 6 months passive surveillance. Geographically-weighted regression was used to explore the spatial variability of relationships between malaria incidence or persistence and the selected socio-economic, and human predictors. A model comparison of between ordinary least square and geographically-weighted regression was also explored. Vector dataset (spatial) of the study area by village levels and statistical data (non-spatial) on malaria confirmed cases, socio-economic status (bed net use), population data (size of the household) and environmental factors (temperature, rain fall) were used in this exploratory analysis. ArcMap 10.2 and Stata 11 were used to perform malaria hotspots analysis.ResultsFrom Jun to December, a total of 408 confirmed malaria cases were notified. The explanatory variables-household size, housing materials, sleeping rooms, sheep and distance to breeding site returned significant t values of −0.25, 2.3, 4.39, 1.25 and 2.36, respectively. The OLS global model revealed that it explained about 70 % (adjusted R2 = 0.70) of the variation in malaria occurrence with AIC = 756.23. The geographically-weighted regression of malaria hotspots resulted in coefficient intercept ranging from 1.89 to 6.22 with a median of 3.5. Large positive values are distributed mainly in the southeast of the district where hotspots are more accurate while low values are mainly found in the centre and in the north.ConclusionGeographically-weighted regression and OLS showed important risks factors of malaria hotspots in Keur Soce. The outputs of such models can be a useful tool to understand occurrence of malaria hotspots in Senegal. An understanding of geographical variation and determination of the core areas of the disease may provide an explanation regarding possible proximal and distal contributors to malaria elimination in Senegal.

Geospatial analysis of births with congenital disorders, Paraná, 2008-2015: ecological study

“…To evaluate the performance of OLS (classic) and GWR (spatial), these models were compared considering as parameters the adjusted R 2 , the AIC and the lowest variability of the resulting residues of each model (21,30) . The OLS model was processed in the GeoDa program, version 1.10.0.8 (Spatial Analysis Laboratory, University of Illinois at Urbana-Champaign, USA).…”

“…The OLS model was processed in the GeoDa program, version 1.10.0.8 (Spatial Analysis Laboratory, University of Illinois at Urbana-Champaign, USA). The GWR model was implemented with the GWR program, version 4.0 (26)(27)30) . The choropleth maps were generated in the QGIS software, version 2.14 (31) .…”

The Implementation of Spatial Weighted Regression on Detecting the Risk Factors of Malaria Incidences In Kulonprogo District

“…Statistical values entered into GIS vectors are prepared on polygon maps as non-spatial data. Spatial distribution of data was visualized with a chloropleth map to show malaria prevalence at the population density (Ndiath, et al, 2015) The equations produced in this study are (Nadya, et al,…”

Technically, the selection of Simulated annealing parameters, for instance, initial temperature, temperature decreasing function, function to generate new points only impact how the shape of the plot and how fast this model converge. From several trials,

“…The proper selection of input data affects prediction accuracy how malaria incidences spatially vary. In fact, there are two way to measure malaria occurrences, in which malaria occurrences are measured by point-based locations as in [2,3] or aggregated data (polygonbased aggregated data) as in [4]. The first manner requires exact coordinates of individual surveys and prediction map are usually measured for every single locations.…”