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The influence of climate variables on dengue in Singapore

机译:气候变量对新加坡登革热的影响

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In this work we correlated dengue cases with climatic variables for the city of Singapore. This was done through a Poisson Regression Model (PRM) that considers dengue cases as the dependent variable and the climatic variables (rainfall, maximum and minimum temperature and relative humidity) as independent variables. We also used Principal Components Analysis (PCA) to choose the variables that influence in the increase of the number of dengue cases in Singapore, where PC1 (Principal component 1) is represented by temperature and rainfall and PC2 (Principal component 2) is represented by relative humidity. We calculated the probability of occurrence of new cases of dengue and the relative risk of occurrence of dengue cases influenced by climatic variable. The months from July to September showed the highest probabilities of the occurrence of new cases of the disease throughout the year. This was based on an analysis of time series of maximum and minimum temperature. An interesting result was that for every 2-10°C of variation of the maximum temperature, there was an average increase of 22.2-184.6% in the number of dengue cases. For the minimum temperature, we observed that for the same variation, there was an average increase of 26.1-230.3% in the number of the dengue cases from April to August. The precipitation and the relative humidity, after analysis of correlation, were discarded in the use of Poisson Regression Model because they did not present good correlation with the dengue cases. Additionally, the relative risk of the occurrence of the cases of the disease under the influence of the variation of temperature was from 1.2-2.8 for maximum temperature and increased from 1.3-3.3 for minimum temperature. Therefore, the variable temperature (maximum and minimum) was the best predictor for the increased number of dengue cases in Singapore.View full textDownload full textKeywordsdengue, Poisson Regression Model, Principal Component Analysis, temperature, relative riskRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/09603123.2011.572279
机译:在这项工作中,我们将登革热病例与新加坡市的气候变量相关联。这是通过Poisson回归模型(PRM)完成的,该模型将登革热病例视为因变量,并将气候变量(降雨,最高和最低温度和相对湿度)视为自变量。我们还使用主成分分析(PCA)来选择影响新加坡登革热病例数量增加的变量,其中PC 1 (主成分1)由温度和降雨量表示,PC 2 (主要成分2)用相对湿度表示。我们计算了新登革热病例的发生概率以及受气候变量影响的登革热病例发生的相对风险。从七月到九月的月份显示了一年中该疾病新病例发生的最高可能性。这基于对最高和最低温度的时间序列的分析。一个有趣的结果是,最高温度每变化2-10°C,登革热病例的平均增加22.22-184.6%。对于最低温度,我们观察到,对于相同的变化,从4月到8月,登革热病例平均增加26.1-230.3%。经过相关性分析后,降水和相对湿度在使用泊松回归模型时被丢弃了,因为它们与登革热病例没有很好的相关性。另外,在温度变化的影响下该疾病病例发生的相对风险对于最高温度为1.2-2.8,对于最低温度为1.3-3.3。因此,可变温度(最高和最低)是新加坡登革热病例增加的最佳预测指标。查看全文下载全文关键词登革热,泊松回归模型,主成分分析,温度,相对风险相关var addthis_config = {ui_cobrand:“ Taylor &Francis Online”,services_compact:“ citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,更多”,发布号:“ ra-4dff56cd6bb1830b”};添加到候选列表链接永久链接http://dx.doi.org/10.1080/09603123.2011.572279

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