Department of Mathematics, Institute of Applied Pedagogy, University of Burundi, Burundi.
Malar J. 2010 Apr 29;9:114. doi: 10.1186/1475-2875-9-114.
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. 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.
This 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.
The 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.
This 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.
在布隆迪，疟疾无论是在发病率还是死亡率方面都是一个主要的公共卫生问题，每年有 250 万例临床病例和超过 1.5 万人死亡。它是孕妇和五岁以下儿童死亡的单一主要原因。由于疟疾对健康和经济造成的严重影响，仍然需要更多的方法来帮助人们了解其影响因素。已经有几项关于这个主题的研究，但结果不同，因为哪些因素是导致疟疾发病率上升的主要原因。本研究的目的是进行空间/纵向统计分析，以确定影响布隆迪疟疾发病率的重要气候变量。
本文研究了气候对布隆迪疟疾的影响。对于 1996 年至 2007 年期间，描述和分析了布隆迪地区疟疾流行病学和气候的实际月度数据。在此分析的基础上，得出了一个数学模型，并提出了一个评估哪些变量显著影响布隆迪疟疾发病率的模型。所提出的建模基于广义线性模型（GLM）和广义加性混合模型（GAMM）。建模是完全贝叶斯的，推断是通过马尔可夫链蒙特卡罗（MCMC）技术进行的。
讨论了所提出模型的结果，发现布隆迪某一月份的疟疾发病率与前一个月的最低温度呈强烈正相关。相反，发现某一月份的降雨量和最高温度可能对同月的疟疾发病率有负面影响。
本研究利用布隆迪 12 年来关于疟疾和气候的可用实际月度数据，得出并提出了一种回归建模方法，以评估与每月疟疾发病率相关的气候因素。所提出模型的结果表明，某一月份的疟疾发病率与前一个月的最低温度（夜间温度）之间存在强烈的正相关。因此，一个悬而未决的问题是如何应对夜间高温。
