A Bayesian Spatio-Temporal Analysis of Malaria in the Greater Accra Region of Ghana from 2015 to 2019
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
The Greater Accra Region is the smallest of the 16 administrative regions in Ghana. It
is highly populated and characterized by tropical climatic conditions. Although efforts towards
malaria control in Ghana have had positive impacts, malaria remains in the top five diseases reported
at healthcare facilities within the Greater Accra Region. To further accelerate progress, analysis of
regionally generated data is needed to inform control and management measures at this level. This
study aimed to examine the climatic drivers of malaria transmission in the Greater Accra Region and
identify inter-district variation in malaria burden. Monthly malaria cases for the Greater Accra Region
were obtained from the Ghanaian District Health Information and Management System. Malaria
cases were decomposed using seasonal-trend decomposition, based on locally weighted regression
to analyze seasonality. A negative binomial regression model with a conditional autoregressive
prior structure was used to quantify associations between climatic variables and malaria risk and
spatial dependence. Posterior parameters were estimated using Bayesian Markov chain Monte
Carlo simulation with Gibbs sampling. A total of 1,105,370 malaria cases were recorded in the
region from 2015 to 2019. The overall malaria incidence for the region was approximately 47 per
1000 population. Malaria transmission was highly seasonal with an irregular inter-annual pattern.
Monthly malaria case incidence was found to decrease by 2.3% (95% credible interval: 0.7–4.2%) for
each 1 ◦C increase in monthly minimum temperature. Only five districts located in the south-central
part of the region had a malaria incidence rate lower than the regional average at >95% probability
level. The distribution of malaria cases was heterogeneous, seasonal, and significantly associated with
climatic variables. Targeted malaria control and prevention in high-risk districts at the appropriate
time points could result in a significant reduction in malaria transmission in the Greater Accra Region.
Description
Research Article
Keywords
Ghana, malaria, Bayesian, modelling, climatic