Integrating Environmental Monitoring and Mosquito Surveillance to Predict Vector-borne Disease: Prospective Forecasts of a West Nile Virus Outbreak.
Integrating Environmental Monitoring and Mosquito Surveillance to Predict Vector-borne Disease: Prospective Forecasts of a West Nile Virus Outbreak.
复制标题
DOI：
10.1371/currents.outbreaks.90e80717c4e67e1a830f17feeaaf85de
复制
发表时间：
2017-05-23
期刊：
影响因子：
--
通讯作者：
Wimberly, Michael C
中科院分区：
文献类型：
--
作者：
Davis, Justin K;Vincent, Geoffrey;Wimberly, Michael C
导读：预测未来蚊媒疾病暴发的时间和地点有可能改善蚊虫控制和疾病预防工作的目标。在这里，我们提出并评估了在2016年西尼罗病毒（WNV）季节之前和期间在南达科他州（美国西尼罗病毒人类传播的热点）做出的前瞻性预测。方法：采用县级logistic回归模型，结合气温、降水和蚊虫感染状况指标，预测每周人类西尼罗河病毒病例发生概率。该模型使用2004-2015年的历史数据进行了指定和拟合，并于2016年应用于对未来一周的西尼罗河病毒人间病例进行短期预测，以及对整个传播季节的西尼罗河病毒病例进行全年预测。在2016年西尼罗河病毒季节结束时，通过将这些预测与发生的人间病例的时空格局进行比较，对这些预测进行了评估。结果：2016年发生了一起西尼罗河病毒疫情，共有167例人感染病例，而2015年仅为40例。模型结果总体上是准确的，短期预测的AUC为0.856。季节早期的温度数据足以预测比正常情况更早开始的西尼罗河病毒季节和高于平均水平的病例数，但低估了总体病例负担。随着获得更多的蚊子感染数据，模型预测在整个季节得到改善，到7月底，该模型提供了对疫情总体规模的接近估计。结论：以气象变量和蚊虫感染指数为预测变量的综合模型准确预测了2016年南达科他州西尼罗河病毒的卷土重来。未来研究的关键领域包括改进模型以提高预测技能，并制定将预测与特定的蚊虫控制和疾病预防活动联系起来的战略。
INTRODUCTION: Predicting the timing and locations of future mosquito-borne disease outbreaks has the potential to improve the targeting of mosquito control and disease prevention efforts. Here, we present and evaluate prospective forecasts made prior to and during the 2016 West Nile virus (WNV) season in South Dakota, a hotspot for human WNV transmission in the United States.METHODS: We used a county-level logistic regression model to predict the weekly probability of human WNV case occurrence as a function of temperature, precipitation, and an index of mosquito infection status. The model was specified and fitted using historical data from 2004-2015 and was applied in 2016 to make short-term forecasts of human WNV cases in the upcoming week as well as whole-year forecasts of WNV cases throughout the entire transmission season. These predictions were evaluated at the end of the 2016 WNV season by comparing them with spatial and temporal patterns of the human cases that occurred.RESULTS: There was an outbreak of WNV in 2016, with a total of 167human cases compared to only 40 in 2015. Model results were generally accurate, with an AUC of 0.856 for short-termpredictions. Early-season temperature datawere sufficient to predict an earlier-than-normal start to the WNV season and an above-average number of cases, but underestimated the overall case burden. Model predictions improved throughout the season as more mosquito infection data were obtained, and by the end of July the model provided a close estimate of the overall magnitude of the outbreak.CONCLUSIONS: An integrated model that included meteorological variables as well as a mosquito infection index as predictor variables accurately predicted the resurgence of WNV in South Dakota in 2016. Key areas for future research include refining the model to improve predictive skill and developing strategies to link forecasts with specific mosquito control and disease prevention activities.