Abstract
BackgroundMalaria transmission is influenced by variations in meteorological conditions, which impact the biology of the parasite and its vector, but also socio-economic conditions, such as levels of urbanization, poverty and education, which impact human vulnerability and vector habitat. The many potential drivers of malaria, both extrinsic, such as climate, and intrinsic, such as population immunity are often difficult to disentangle. This presents a challenge for the modelling of malaria risk in space and time.MethodsA statistical mixed model framework is proposed to model malaria risk at the district level in Malawi, using an age-stratified spatio-temporal dataset of malaria cases from July 2004 to June 2011. Several climatic, geographic and socio-economic factors thought to influence malaria incidence were tested in an exploratory model. In order to account for the unobserved confounding factors that influence malaria, which are not accounted for using measured covariates, a generalized linear mixed model was adopted, which included structured and unstructured spatial and temporal random effects. A hierarchical Bayesian framework using Markov chain Monte Carlo simulation was used for model fitting and prediction.ResultsUsing a stepwise model selection procedure, several explanatory variables were identified to have significant associations with malaria including climatic, cartographic and socio-economic data. Once intervention variations, unobserved confounding factors and spatial correlation were considered in a Bayesian framework, a final model emerged with statistically significant predictor variables limited to average precipitation (quadratic relation) and average temperature during the three months previous to the month of interest.ConclusionsWhen modelling malaria risk in Malawi it is important to account for spatial and temporal heterogeneity and correlation between districts. Once observed and unobserved confounding factors are allowed for, precipitation and temperature in the months prior to the malaria season of interest are found to significantly determine spatial and temporal variations of malaria incidence. Climate information was found to improve the estimation of malaria relative risk in 41% of the districts in Malawi, particularly at higher altitudes where transmission is irregular. This highlights the potential value of climate-driven seasonal malaria forecasts.
Highlights
Malaria transmission is influenced by variations in meteorological conditions, which impact the biology of the parasite and its vector, and socio-economic conditions, such as levels of urbanization, poverty and education, which impact human vulnerability and vector habitat
Potential drivers A precursory view of the temporal and spatial variation of the malaria standardized morbidity ratio (SMR) in Malawi is given in Figure 1 and 2, rate over the seven year period and the number of health facilities per 1000 inhabitants, respectively
It is interesting to note that the spatial distribution of SMR for the under fives category broadly reflects the map of prevalence produced by the Malaria Atlas Project (MAP) Bayesian analysis of survey data [65], with higher SMR rates along the western shoreline of lake Malawi and central-west lowlands of the southern part of the country
Summary
Malaria transmission is influenced by variations in meteorological conditions, which impact the biology of the parasite and its vector, and socio-economic conditions, such as levels of urbanization, poverty and education, which impact human vulnerability and vector habitat. The many potential drivers of malaria, both extrinsic, such as climate, and intrinsic, such as population immunity are often difficult to disentangle This presents a challenge for the modelling of malaria risk in space and time. In a bid to reduce the spread of the disease, the Malawi government has embarked on different interventions such as vector control through indoor residual spraying and insecticidetreated nets (ITN) [4]. The latter primarily focuses on the high-risk groups of pregnant women and children under the age of five and is the main intervention method in Malawi. Despite the scaling up of malaria control measures over the last decade, the recent data have not suggested a decrease in the burden of disease [4,5]
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