Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore
Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore
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DOI：
10.1371/journal.pntd.0006935
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发表时间：
2018-12-01
影响因子：
3.8
通讯作者：
Markuzon, Natasha
中科院分区：
文献类型：
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作者：
Benedum, Corey M.;Seidahmed, Osama M. E.;Markuzon, Natasha
背景降雨模式是登革热传播的主要驱动力之一，因为蚊子需要静水来繁殖。然而，过多的降雨会破坏伊蚊的繁殖周期，因为它们会把繁殖地的水生阶段“冲走”。我们开发了模型来预测降雨数据中这种“潮红”事件的发生，并评估2000年至2016年新加坡登革热爆发风险的潮红影响。方法我们使用机器学习和回归模型来预测数据集中的“潮红”天数，该数据集基于新加坡收集的昆虫学和相应的降雨观测。我们使用了一个分布滞后非线性logistic回归模型，以估计每周的潮红事件的数量和登革热爆发的风险之间的关联。ResultsDays与潮红通过开发的logistic回归模型的基础上，昆虫学数据（测试集的准确性= 92%）。预测是基于阈值的总数，这些阈值表明在多周内有异常的降雨条件。我们观察到，在潮红事件发生后1至6周，登革热爆发风险在统计学上显著降低。与无潮红事件的周相比，有5次或5次以上潮红事件的周，随后几周登革热暴发的风险降低了16%~ 70%.ConclusionsWe已经建立了一个将时间降雨模式与潮红条件相关联的高精度预测模型。使用预测的冲水事件，我们已经证明冲水后登革热爆发风险在统计学上显着降低，并且时间滞后与蚊子从幼虫发育和感染传播的时间完全一致。病媒控制方案应考虑流行地区水文条件对登革热传播的影响。
BackgroundRainfall patterns are one of the main drivers of dengue transmission as mosquitoes require standing water to reproduce. However, excess rainfall can be disruptive to the Aedes reproductive cycle by "flushing out" aquatic stages from breeding sites. We developed models to predict the occurrence of such "flushing" events from rainfall data and to evaluate the effect of flushing on dengue outbreak risk in Singapore between 2000 and 2016.MethodsWe used machine learning and regression models to predict days with "flushing" in the dataset based on entomological and corresponding rainfall observations collected in Singapore. We used a distributed lag nonlinear logistic regression model to estimate the association between the number of flushing events per week and the risk of a dengue outbreak.ResultsDays with flushing were identified through the developed logistic regression model based on entomological data (test set accuracy = 92%). Predictions were based upon the aggregate number of thresholds indicating unusually rainy conditions over multiple weeks. We observed a statistically significant reduction in dengue outbreak risk one to six weeks after flushing events occurred. For weeks with five or more flushing events, compared with weeks with no flushing events, the risk of a dengue outbreak in the subsequent weeks was reduced by 16% to 70%.ConclusionsWe have developed a high accuracy predictive model associating temporal rainfall patterns with flushing conditions. Using predicted flushing events, we have demonstrated a statistically significant reduction in dengue outbreak risk following flushing, with the time lag well aligned with time of mosquito development from larvae and infection transmission. Vector control programs should consider the effects of hydrological conditions in endemic areas on dengue transmission.