Mathematics, School of Science, RMIT University, Melbourne, Australia; Department of Industrial Management, Faculty of Applied Sciences, Wayamba University of Sri Lanka, Kuliyapitiya 60200, Sri Lanka.
Mathematics, School of Science, RMIT University, Melbourne, Australia; Rutgers Business School, Rutgers University, NJ, United States.
Sci Total Environ. 2020 Jul 1;724:138269. doi: 10.1016/j.scitotenv.2020.138269. Epub 2020 Mar 28.
We studied the dynamics of dengue disease in two epidemic regions in Sri Lanka, the densely populated Colombo district representing the wet zone and the relatively less populated Batticaloa district representing the dry zone. Regional differences in disease dynamics were analysed against regional weather factors. Wavelets, Granger causality and regression methods were used. The difference between the dynamical features of these two regions may be explained by the differences in the climatic characteristics of the two regions. Wavelet analysis revealed that Colombo dengue incidence has 6 months periodicity while Batticaloa dengue incidence has 1 year periodicity. This is well explained by the dominant 6 months periodicity in Colombo rainfall and 1 year periodicity in Batticaloa rainfall. The association between dengue incidence and temperature was negative in dry Batticaloa and was insignificant in wet Colombo. Granger causality results indicated that rainfall, rainy days, relative humidity and wind speed can be used to predict Colombo dengue incidence while only rainfall and relative humidity were significant in Batticaloa. Negative binomial and linear regression models were used to identify the weather variables which best explain the variations in dengue incidence. Most recent available incidence data performed as best explanatory variables, outweighing the importance of past weather data. Therefore we recommend the health authorities to closely monitor the number of cases and to streamline recording procedures so that most recent data are available for early detection of epidemics. We also noted that epidemic responses to weather changes appear quickly in densely populated Colombo compared to less populated Batticaloa. The past dengue incidence and weather variables explain the dengue incidence better in Batticaloa than in Colombo and thus other exogenous factors such as population density and human mobility may be affecting Colombo dengue incidence.
我们研究了斯里兰卡两个流行地区的登革热疾病动态,人口密集的科伦坡区代表湿区,相对人口较少的拜蒂克洛区代表干区。针对区域天气因素分析了疾病动态的区域差异。使用了小波、格兰杰因果关系和回归方法。这两个地区动态特征的差异可以用两个地区气候特征的差异来解释。小波分析表明,科伦坡登革热发病率具有 6 个月的周期性,而拜蒂克洛登革热发病率具有 1 年的周期性。这很好地解释了科伦坡降雨的主要 6 个月周期性和拜蒂克洛降雨的 1 年周期性。干热的拜蒂克洛地区登革热发病率与温度呈负相关,而湿热的科伦坡地区则不显著。格兰杰因果关系结果表明,降雨量、降雨天数、相对湿度和风速可用于预测科伦坡登革热发病率,而仅降雨量和相对湿度在拜蒂克洛具有显著性。使用负二项式和线性回归模型来确定最佳解释登革热发病率变化的天气变量。最新可用的发病数据作为最佳解释变量,比过去的天气数据更为重要。因此,我们建议卫生当局密切监测病例数量,并简化记录程序,以便尽早发现疫情,提供最新数据。我们还注意到,与人口较少的拜蒂克洛相比,人口密集的科伦坡对天气变化的疫情反应更快。过去的登革热发病率和天气变量在拜蒂克洛比在科伦坡更好地解释了登革热发病率,因此其他外生因素,如人口密度和人类流动性,可能会影响科伦坡的登革热发病率。