Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines.
Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines.
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DOI：
10.1186/s12879-018-3066-0
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发表时间：
2018-04-17
影响因子：
3.7
通讯作者：
Watanabe K
中科院分区：
文献类型：
--
作者：
Carvajal TM;Viacrusis KM;Hernandez LFT;Ho HT;Amalin DM;Watanabe K
有几项研究应用了气象变量等生态因素来开发模型，并准确预测登革热发病或发生的时间模式。在大量研究这一前提的情况下，建模方法与每项研究不同，只使用一种统计技术。它提出了一个问题，即哪种技术是否会是稳健和可靠的。因此，我们的研究旨在比较气象因素对马尼拉大都市登革热发病时间模式的预测精度，这四种建模技术是：(A)一般加性建模，(B)带有外生变量的季节性自回归综合移动平均(C)随机森林和(D)梯度增强。2009年1月1日至2013年12月31日期间，马尼拉的登革热发病率和气象数据(洪水、降水、气温、南方振荡指数、相对湿度、风速和风向)均来自各自的政府机构。分析中使用了两种类型的数据集：观测气象因素(MF)及其相应的延迟或滞后效应(LG)。之后，对这些数据集进行了四种建模技术。计算和评价了每种建模技术的预测精度和变量重要性。在统计建模技术中，随机森林显示出最好的预测精度。此外，气象变量的延迟或滞后效应被证明是用于这一目的的最佳数据集。因此，具有延迟气象效应的随机森林模型(RF-LG)被认为是所有评估模型中最好的。在最佳模型中，相对湿度是最重要的气象因子。研究表明，每种统计建模技术产生的预测结果确实不同，并进一步表明，具有延迟气象效应的随机森林模型在预测马尼拉大都市登革热发病的时间模式方面是最好的。同样值得注意的是，这项研究还确定相对湿度是一个重要的气象因素，与降雨和温度一起可以影响这种时间模式。本文的在线版本(10.1186/s12879-0183066-0)包含向授权用户提供的补充材料。
Several studies have applied ecological factors such as meteorological variables to develop models and accurately predict the temporal pattern of dengue incidence or occurrence. With the vast amount of studies that investigated this premise, the modeling approaches differ from each study and only use a single statistical technique. It raises the question of whether which technique would be robust and reliable. Hence, our study aims to compare the predictive accuracy of the temporal pattern of Dengue incidence in Metropolitan Manila as influenced by meteorological factors from four modeling techniques, (a) General Additive Modeling, (b) Seasonal Autoregressive Integrated Moving Average with exogenous variables (c) Random Forest and (d) Gradient Boosting. Dengue incidence and meteorological data (flood, precipitation, temperature, southern oscillation index, relative humidity, wind speed and direction) of Metropolitan Manila from January 1, 2009 – December 31, 2013 were obtained from respective government agencies. Two types of datasets were used in the analysis; observed meteorological factors (MF) and its corresponding delayed or lagged effect (LG). After which, these datasets were subjected to the four modeling techniques. The predictive accuracy and variable importance of each modeling technique were calculated and evaluated. Among the statistical modeling techniques, Random Forest showed the best predictive accuracy. Moreover, the delayed or lag effects of the meteorological variables was shown to be the best dataset to use for such purpose. Thus, the model of Random Forest with delayed meteorological effects (RF-LG) was deemed the best among all assessed models. Relative humidity was shown to be the top-most important meteorological factor in the best model. The study exhibited that there are indeed different predictive outcomes generated from each statistical modeling technique and it further revealed that the Random forest model with delayed meteorological effects to be the best in predicting the temporal pattern of Dengue incidence in Metropolitan Manila. It is also noteworthy that the study also identified relative humidity as an important meteorological factor along with rainfall and temperature that can influence this temporal pattern. The online version of this article (10.1186/s12879-018-3066-0) contains supplementary material, which is available to authorized users.
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