Department of Mathematics, Faculty of Science, Silpakorn University, Nakhon Pathom, 73000, Thailand.
The Center of Excellence in Mathematics, CHE, Bangkok, 10400, Thailand.
BMC Infect Dis. 2020 Mar 12;20(1):208. doi: 10.1186/s12879-020-4902-6.
In Thailand, dengue fever is one of the most well-known public health problems. The objective of this study was to examine the epidemiology of dengue and determine the seasonal pattern of dengue and its associate to climate factors in Bangkok, Thailand, from 2003 to 2017.
The dengue cases in Bangkok were collected monthly during the study period. The time-series data were extracted into the trend, seasonal, and random components using the seasonal decomposition procedure based on loess. The Spearman correlation analysis and artificial neuron network (ANN) were used to determine the association between climate variables (humidity, temperature, and rainfall) and dengue cases in Bangkok.
The seasonal-decomposition procedure showed that the seasonal component was weaker than the trend component for dengue cases during the study period. The Spearman correlation analysis showed that rainfall and humidity played a role in dengue transmission with correlation efficiency equal to 0.396 and 0.388, respectively. ANN showed that precipitation was the most crucial factor. The time series multivariate Poisson regression model revealed that increasing 1% of rainfall corresponded to an increase of 3.3% in the dengue cases in Bangkok. There were three models employed to forecast the dengue case, multivariate Poisson regression, ANN, and ARIMA. Each model displayed different accuracy, and multivariate Poisson regression was the most accurate approach in this study.
This work demonstrates the significance of weather in dengue transmission in Bangkok and compares the accuracy of the different mathematical approaches to predict the dengue case. A single model may insufficient to forecast precisely a dengue outbreak, and climate factor may not only indicator of dengue transmissibility.
在泰国，登革热是最著名的公共卫生问题之一。本研究的目的是研究登革热的流行病学，并确定 2003 年至 2017 年期间泰国曼谷登革热的季节性模式及其与气候因素的关系。
在研究期间，每月收集曼谷的登革热病例。使用基于局部加权回归的季节分解程序将时间序列数据提取为趋势、季节性和随机分量。使用 Spearman 相关分析和人工神经网络（ANN）来确定曼谷气候变量（湿度、温度和降雨量）与登革热病例之间的关联。
季节分解程序显示，在研究期间，登革热病例的季节性分量弱于趋势分量。Spearman 相关分析表明，降雨和湿度在登革热传播中起作用，相关效率分别为 0.396 和 0.388。ANN 显示降水是最重要的因素。时间序列多元泊松回归模型表明，降雨量增加 1%，曼谷登革热病例增加 3.3%。采用多元泊松回归、ANN 和 ARIMA 三种模型来预测登革热病例。每个模型的准确性不同，多元泊松回归是本研究中最准确的方法。
这项工作表明天气对曼谷登革热传播的重要性，并比较了预测登革热病例的不同数学方法的准确性。单一模型可能不足以精确预测登革热疫情，气候因素可能不仅仅是登革热传播的指标。
