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Coherence of dengue incidence and climate in the wet and dry zones of Sri Lanka

机译:登革热发病率和气候在斯里兰卡湿和干燥区中的一致性

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摘要

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.
机译:我们研究了斯里兰卡两种疫情地区登革热病的动态,较为潮湿的科伦坡区,代表潮湿区,相对较少的人口稠密的Batticaloa区代表干燥区。分析了区域性气象因素的疾病动态区域差异。使用小波,格兰杰因果关系和回归方法。这两个区域的动态特征之间的差异可以通过两个区域的气候特征的差异来解释。小波分析显示,Colombo登革热发病率有6个月的周期,而Batticaloa登革热发病率为1年的周期。这很好地解释了Colombo降雨中的主要6个月的周期,并在Batticaloa降雨中的1年周期性。登革热发病率和温度之间的关联在干燥的batticaloa中是阴性的,在潮湿的Colombo中无关紧要。格兰杰因果关系结果表明,降雨,雨天,相对湿度和风速可用于预测Colombo登革热发病率,同时只有降雨和相对湿度在Batticaloa中显着。负二项式和线性回归模型用于识别最佳解释登革热发病率的变化的天气变量。大多数最近的可用发出数据作为最佳解释变量,超过了过去天气数据的重要性。因此,我们建议卫生当局密切监控案件的数量,并简化录音程序,以便最近的数据可用于早期检测流行病。我们还指出,与较少人口稠密的Batticaloa相比,在密集的Colombo相比,流行对天气变化的疫情迅速出现。过去的登革热发病率和天气变量解释了比特科亚的登革热发病率比科伦坡更好,因此人口密度和人类流动等其他外源性因素可能会影响科伦坡登革热发病率。

著录项

  • 来源
    《The Science of the Total Environment》 |2020年第jul1期|138269.1-138269.10|共10页
  • 作者单位

    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;

    UMMISCO UMI 209 Sorbonne Universite-IRD Paris France IBENS UMR CNRS 8197 Eco-Evolution Mathematique Ecole Normale Superieure Paris France;

    Mathematics School of Science RMIT University Melbourne Australia Biomathematics Unit School of Zoology Faculty of Life Sciences Tel Aviv University Tel Aviv-Yafo Israel;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Dengue; Sri Lanka; Wavelet analysis; Weather; Periodicity; Granger causality;

    机译:登革热;斯里兰卡;小波分析;天气;周期性;格兰杰因果关系;

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