Characterizing local-scale heterogeneity of malaria risk: a case study in Bunkpurugu-Yunyoo district in northern Ghana

Abstract

BackgroundBayesian methods have been used to generate country-level and global maps of malaria prevalence. With increasing availability of detailed malaria surveillance data, these methodologies can also be used to identify fine-scale heterogeneity of malaria parasitaemia for operational prevention and control of malaria.MethodsIn this article, a Bayesian geostatistical model was applied to six malaria parasitaemia surveys conducted during rainy and dry seasons between November 2010 and 2013 to characterize the micro-scale spatial heterogeneity of malaria risk in northern Ghana.ResultsThe geostatistical model showed substantial spatial heterogeneity, with malaria parasite prevalence varying between 19 and 90%, and revealing a northeast to southwest gradient of predicted risk. The spatial distribution of prevalence was heavily influenced by two modest urban centres, with a substantially lower prevalence in urban centres compared to rural areas. Although strong seasonal variations were observed, spatial malaria prevalence patterns did not change substantially from year to year. Furthermore, independent surveillance data suggested that the model had a relatively good predictive performance when extrapolated to a neighbouring district.ConclusionsThis high variability in malaria prevalence is striking, given that this small area (approximately 30 km × 40 km) was purportedly homogeneous based on country-level spatial analysis, suggesting that fine-scale parasitaemia data might be critical to guide district-level programmatic efforts to prevent and control malaria. Extrapolations results suggest that fine-scale parasitaemia data can be useful for spatial predictions in neighbouring unsampled districts and does not have to be collected every year to aid district-level operations, helping to alleviate concerns regarding the cost of fine-scale data collection.Electronic supplementary materialThe online version of this article (10.1186/s12936-019-2703-4) contains supplementary material, which is available to authorized users.

Measuring the contribution of human mobility to malaria persistence

“…Another limitation is the fact that the time of day spent in each locality was not accounted for, which can affect the probability for malaria infection, due to vector activity preferences. Seasonality attributed to malaria transmission dynamics may display an important influence in this study, as reported in the literature [3,31,32,34,37].…”

“…Differences in malaria prevalence along rural-urban gradients can be explained by ecological, social, and behavioural factors [7][8][9]31]. In general, urban areas present certain conditions, such as housing, drainage network, pavement and easier access to health care, that contribute to decreased malaria prevalence compared to rural areas [32]. According to Braz et al [33], a spatial dependency is noted for malaria in the Amazon Basin, with differing characteristics between municipalities, states and international borders, reinforcing the importance of monitoring malaria clusters and integrative control actions.…”

Estimating the local spatio‐temporal distribution of malaria from routine health information systems in areas of low health care access and reporting

“…Fine-scale estimates of malaria spatio-temporal variations using methods presented here can then be used to characterize local socio-economic and environmental drivers of malaria risk, paving the way to the development of early warning or forecasting systems that could further guide local malaria control. Malaria heterogeneity and its drivers are commonly modelled at the national and regional level [ 62 – 64 ], but malaria can have extensive spatial variability in relatively small areas [ 42 , 43 , 65 ]. After adjustment, we observed significant spatial variations in malaria incidence in communities across the district, with 7% the population living in areas where annual incidence was twice the district’s average, as well as multiple short-term, localized hotspots during the study period (Additional file 3 : Video S1).…”

Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period

“… 101 Several studies on applying environmental variables that influence the transmission of malaria had applied and reported best-fit models with distance to streams, distance to main road, distance to health facility, and distance to border. 32 , 102 – 104 The experts in this study identified two proxy indicators of access to treatment and care, distance to road and distance to health facilities. Distance of a place from roads could affect or determine the effectiveness of measures to be taken to control the risk of malaria infestation.…”