Use of temperature to improve West Nile virus forecasts

Ecological and laboratory studies have demonstrated that temperature modulates West Nile virus (WNV) transmission dynamics and spillover infection to humans. Here we explore whether inclusion of temperature forcing in a model depicting WNV transmission improves WNV forecast accuracy relative to a baseline model depicting WNV transmission without temperature forcing. Both models are optimized using a data assimilation method and two observed data streams: mosquito infection rates and reported human WNV cases. Each coupled model-inference framework is then used to generate retrospective ensemble forecasts of WNV for 110 outbreak years from among 12 geographically diverse United States counties. The temperature-forced model improves forecast accuracy for much of the outbreak season. From the end of July until the beginning of October, a timespan during which 70% of human cases are reported, the temperature-forced model generated forecasts of the total number of human cases over the next 3 weeks, total number of human cases over the season, the week with the highest percentage of infectious mosquitoes, and the peak percentage of infectious mosquitoes that on average increased absolute forecast accuracy 5%, 10%, 12%, and 6%, respectively, over the non-temperature forced baseline model. These results indicate that use of temperature forcing improves WNV forecast accuracy and provide further evidence that temperature influences rates of WNV transmission. The findings provide a foundation for implementation of a statistically rigorous system for real-time forecast of seasonal WNV outbreaks and their use as a quantitative decision support tool for public health officials and mosquito control programs.

生态学与实验室研究均已证实，温度可调控西尼罗河病毒（West Nile Virus, WNV）的传播动态以及向人类的溢出感染。本研究旨在探究：相较于未纳入温度驱动项的西尼罗河病毒传播基线模型，在描述西尼罗河病毒传播的模型中加入温度驱动项，是否能够提升西尼罗河病毒的预测精度。两款模型均通过数据同化（data assimilation）方法，结合两类观测数据流——蚊虫感染率与报告的人类西尼罗河病毒感染病例——进行优化。随后，每一套耦合模型-推断框架均被用于生成12个地理分布各异的美国县的110个暴发年份的西尼罗河病毒回顾性集合预报。纳入温度驱动项的模型在暴发季的多数时段均可提升预报精度。在7月底至10月初这一报告了70%人类感染病例的时段内，相较于未纳入温度驱动项的基线模型，温度驱动模型对以下四类指标的预报绝对精度平均提升了5%、10%、12%与6%：未来3周的人类感染总病例数、季内人类感染总病例数、感染性蚊虫占比最高的周次，以及感染性蚊虫占比的峰值。上述结果表明，纳入温度驱动项可提升西尼罗河病毒的预报精度，并进一步证实温度会影响西尼罗河病毒的传播速率。本研究结果为构建具备统计严谨性的季节性西尼罗河病毒暴发实时预报系统奠定了基础，同时也为公共卫生官员与蚊虫防控项目提供了可量化的决策支持工具。