Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore

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.背景 降雨模式是登革热传播的主要驱动因素之一，因为蚊虫需要积水来完成繁殖。然而，过量降雨可通过“冲刷”滋生地中的蚊虫水生发育阶段，干扰埃及伊蚊的繁殖周期。本研究构建了基于降雨数据预测此类“冲刷”事件的模型，并评估了2000至2016年间新加坡境内冲刷事件对登革热暴发风险的影响。 方法 本研究采用机器学习与回归模型，基于新加坡采集的昆虫学观测数据及对应降雨数据，对数据集中的“冲刷”日进行预测。我们使用分布滞后非线性逻辑回归（distributed lag nonlinear logistic regression）模型，估算每周冲刷事件数量与登革热暴发风险之间的关联。 结果 本研究构建的逻辑回归模型可基于昆虫学数据识别冲刷日，测试集准确率达92%。预测基于多周内异常降雨条件的阈值总数量。研究发现，冲刷事件发生后的1至6周内，登革热暴发风险存在统计学意义上的显著降低。与无冲刷事件的周相比，发生5次及以上冲刷事件的周，后续数周内的登革热暴发风险降低了16%至70%。 结论 本研究构建了高精度的预测模型，可将时序降雨模式与蚊虫滋生地冲刷状况相关联。通过预测得到的冲刷事件，本研究证实了冲刷后登革热暴发风险存在统计学意义上的显著降低，其时滞与蚊虫从幼虫发育至感染传播的周期高度吻合。媒介控制项目应考虑流行区水文条件对登革热传播的影响。