Bayesian modelling of the effect of climate on malaria in Burundi

Hermenegilde Nkurunziza,Albrecht Gebhardt,Jürgen Pilz

doi:10.1186/1475-2875-9-114

Hermenegilde Nkurunziza, Albrecht Gebhardt + Show 1 more

Open Access

https://doi.org/10.1186/1475-2875-9-114

Copy DOI

Journal: Malaria journal	Publication Date: Apr 29, 2010
Citations: 72	License type: CC BY 2.0

Affiliation: Université du Burundi, University of Klagenfurt

Abstract

BackgroundIn Burundi, malaria is a major public health issue in terms of both morbidity and mortality with around 2.5 million clinical cases and more than 15,000 deaths each year. It is the single main cause of mortality in pregnant women and children below five years of age. Due to the severe health and economic cost of malaria, there is still a growing need for methods that will help to understand the influencing factors. Several studies have been done on the subject yielding different results as which factors are most responsible for the increase in malaria. The purpose of this study has been to undertake a spatial/longitudinal statistical analysis to identify important climatic variables that influence malaria incidences in Burundi.MethodsThis paper investigates the effects of climate on malaria in Burundi. For the period 1996-2007, real monthly data on both malaria epidemiology and climate in the area of Burundi are described and analysed. From this analysis, a mathematical model is derived and proposed to assess which variables significantly influence malaria incidences in Burundi. The proposed modelling is based on both generalized linear models (GLM) and generalized additive mixed models (GAMM). The modelling is fully Bayesian and inference is carried out by Markov Chain Monte Carlo (MCMC) techniques.ResultsThe results obtained from the proposed models are discussed and it is found that malaria incidence in a given month in Burundi is strongly positively associated with the minimum temperature of the previous month. In contrast, it is found that rainfall and maximum temperature in a given month have a possible negative effect on malaria incidence of the same month.ConclusionsThis study has exploited available real monthly data on malaria and climate over 12 years in Burundi to derive and propose a regression modelling to assess climatic factors that are associated with monthly malaria incidence. The results obtained from the proposed models suggest a strong positive association between malaria incidence in a given month and the minimum temperature (night temperature) of the previous month. An open question is, therefore, how to cope with high temperatures at night.

Highlights

In Burundi, malaria is a major public health issue in terms of both morbidity and mortality with around 2.5 million clinical cases and more than 15,000 deaths each year
This suggests that malaria incidence in a given month is positively associated with the minimum temperature of the same month and that of the previous month
In both tables the intercepts (β0 and α0) have positive mean with a positive 95% credible interval (CI). They have the largest absolute value. This suggests that variables other than climate may have a greater influence on malaria incidence

Summary

Introduction

In Burundi, malaria is a major public health issue in terms of both morbidity and mortality with around 2.5 million clinical cases and more than 15,000 deaths each year It is the single main cause of mortality in pregnant women and children below five years of age. Due to the severe health and economic cost of malaria, there is still a growing need for methods that will help to understand the influencing factors. The purpose of this study has been to undertake a spatial/longitudinal statistical analysis to identify important climatic variables that influence malaria incidences in Burundi. Temperature and altitude were the most plausible predictors of malaria prevalence in Botswana [14]

Methods

Results

Conclusion