Abstract

Background:

West Nile virus (WNV), a global arbovirus, is the most prevalent mosquito-transmitted infection in the United States. Forecasts of WNV risk during the upcoming transmission season could provide the basis for targeted mosquito control and disease prevention efforts. We developed the Arbovirus Mapping and Prediction (ArboMAP) WNV forecasting system and used it in South Dakota from 2016 to 2019. This study reports a post hoc forecast validation and model comparison.

Objectives:

Our objective was to validate historical predictions of WNV cases with independent data that were not used for model calibration. We tested the hypothesis that predictive models based on mosquito surveillance data combined with meteorological variables were more accurate than models based on mosquito or meteorological data alone.

Methods:

The ArboMAP system incorporated models that predicted the weekly probability of observing one or more human WNV cases in each county. We compared alternative models with different predictors including a) a baseline model based only on historical WNV cases, b) mosquito models based on seasonal patterns of infection rates, c) environmental models based on lagged meteorological variables, including temperature and vapor pressure deficit, d) combined models with mosquito infection rates and lagged meteorological variables, and e) ensembles of two or more combined models. During the WNV season, models were calibrated using data from previous years and weekly predictions were made using data from the current year. Forecasts were compared with observed cases to calculate the area under the receiver operating characteristic curve (AUC) and other metrics of spatial and temporal prediction error.

Results:

Mosquito and environmental models outperformed the baseline model that included county-level averages and seasonal trends of WNV cases. Combined models were more accurate than models based only on meteorological or mosquito infection variables. The most accurate model was a simple ensemble mean of the two best combined models. Forecast accuracy increased rapidly from early June through early July and was stable thereafter, with a maximum AUC of 0.85. The model predictions captured the seasonal pattern of WNV as well as year-to-year variation in case numbers and the geographic pattern of cases.

Discussion:

The predictions reached maximum accuracy early enough in the WNV season to allow public health responses before the peak of human cases in August. This early warning is necessary because other indicators of WNV risk, including early reports of human cases and mosquito abundance, are poor predictors of case numbers later in the season. https://doi.org/10.1289/EHP10287

中文翻译：

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基于公共卫生监测和气象数据的综合预测预测北美高风险地区的西尼罗河病毒

摘要

背景：

西尼罗河病毒 (WNV) 是一种全球虫媒病毒，是美国最普遍的蚊虫传播感染。在即将到来的传播季节对 WNV 风险的预测可以为有针对性的蚊子控制和疾病预防工作提供基础。我们开发了虫媒病毒绘图和预测 (ArboMAP) WNV 预测系统，并于 2016 年至 2019 年在南达科他州使用它。本研究报告了事后预测验证和模型比较。

目标：

我们的目标是使用未用于模型校准的独立数据验证 WNV 病例的历史预测。我们检验了这样一个假设，即基于蚊子监测数据和气象变量的预测模型比仅基于蚊子或气象数据的模型更准确。

方法：

ArboMAP 系统包含的模型可以预测每个县每周观察到一个或多个人类 WNV 病例的概率。我们比较了具有不同预测变量的替代模型，包括a）仅基于历史 WNV 病例的基线模型，b）基于感染率季节性模式的蚊子模型，c）基于滞后气象变量（包括温度和蒸汽压不足）的环境模型，d ) 结合了蚊子感染率和滞后气象变量的模型，以及e) 两个或多个组合模型的集合。在 WNV 季节，使用前几年的数据校准模型，并使用当年的数据进行每周预测。将预测与观察到的案例进行比较，以计算接收者操作特征曲线 (AUC) 下的面积和其他空间和时间预测误差的度量。

结果：

蚊子和环境模型优于包括县级平均值和 WNV 病例季节性趋势的基线模型。组合模型比仅基于气象或蚊虫感染变量的模型更准确。最准确的模型是两个最佳组合模型的简单集成平均值。预测准确度从 6 月初到 7 月初迅速提高，此后保持稳定，最大 AUC 为 0.85。模型预测捕获了 WNV 的季节性模式以及病例数的逐年变化和病例的地理模式。

讨论：

这些预测在 WNV 季节很早就达到了最大准确性，以便在 8 月人类病例高峰之前做出公共卫生反应。这种早期预警是必要的，因为 WNV 风险的其他指标，包括人类病例和蚊子数量的早期报告，是本季节后期病例数量的不良预测指标。https://doi.org/10.1289/EHP10287