Ankamah Sylvia, Nokoe Kaku S, Iddrisu Wahab A
Department of Mathematics and Statistics, University of Energy and Natural Resources, P.O. Box 214, Sunyani, Ghana.
Office of Deputy Vice Chancellor, Catholic University of Eastern Africa, P.O. Box 62157-00200, Nairobi, Kenya.
Malar Res Treat. 2018 May 29;2018:6124321. doi: 10.1155/2018/6124321. eCollection 2018.
Malaria is considered endemic in over hundred countries across the globe. Many cases of malaria and deaths due to malaria occur in Sub-Saharan Africa. The disease is of great public health concern since it affects people of all age groups more especially pregnant women and children because of their vulnerability. This study sought to use vector autoregression (VAR) models to model the impact of climatic variability on malaria. Monthly climatic data (rainfall, maximum temperature, and relative humidity) from 2010 to 2015 were obtained from the Ghana Meteorological Agency while data on malaria for the same period were obtained from the Ghana Health Service. Results of the Granger and instantaneous causality tests led to a conclusion that malaria is influenced by all three climatic variables. The impulse response analyses indicated that the highest positive effect of maximum temperature, relative humidity, and rainfall on malaria is observed in the months of September, March, and October, respectively. The decomposition of forecast variance indicates varying degree of malaria dependence on the climatic variables, with as high as 12.65% of the variability in the trend of malaria which has been explained by past innovations in maximum temperature alone. This is quite significant and therefore, policy-makers should not ignore temperature when formulating policies to address malaria.
疟疾在全球一百多个国家被视为地方病。撒哈拉以南非洲地区出现了许多疟疾病例以及因疟疾导致的死亡。该疾病引发了极大的公共卫生关注，因为它影响所有年龄组的人群，尤其是孕妇和儿童，因为他们较为脆弱。本研究试图使用向量自回归（VAR）模型来模拟气候变异性对疟疾的影响。2010年至2015年的月度气候数据（降雨量、最高温度和相对湿度）取自加纳气象机构，而同期的疟疾数据则取自加纳卫生服务部门。格兰杰因果检验和即时因果检验的结果得出结论，疟疾受到所有这三个气候变量的影响。脉冲响应分析表明，最高温度、相对湿度和降雨量对疟疾的最大正向影响分别在9月、3月和10月观察到。预测方差分解表明疟疾对气候变量的依赖程度各不相同，仅过去最高温度的变化就解释了高达12.65%的疟疾趋势变异性。这相当显著，因此，政策制定者在制定应对疟疾的政策时不应忽视温度。
