Karki S, Westcott N E, Muturi E J, Brown W M, Ruiz M O
Department of Pathobiology, University of Illinois, Urbana, IL, USA.
Illinois State Water Survey, Prairie Research Institute, University of Illinois at Urbana - Champaign, Urbana, IL, USA.
Zoonoses Public Health. 2018 Feb;65(1):177-184. doi: 10.1111/zph.12386. Epub 2017 Aug 16.
Surveillance for West Nile virus (WNV) and other mosquito-borne pathogens involves costly and time-consuming collection and testing of mosquito samples. One difficulty faced by public health personnel is how to interpret mosquito data relative to human risk, thus leading to a failure to fully exploit the information from mosquito testing. The objective of our study was to use the information gained from historic West Nile virus mosquito testing to determine human risk relative to mosquito infection and to assess the usefulness of our mosquito infection forecasting models to give advance warning. We compared weekly mosquito infection rates from 2004 to 2013 to WNV case numbers in Illinois. We then developed a weather-based forecasting model to estimate the WNV mosquito infection rate one to 3 weeks ahead of mosquito testing both statewide and for nine regions of Illinois. We further evaluated human illness risk relative to both the measured and the model-estimated infection rates to provide guidelines for public health messages. We determined that across 10 years, over half of human WNV cases occurred following the 29 (of 210) weeks with the highest mosquito infection rates. The values forecasted by the models can identify those time periods, but model results and data availability varied by region with much stronger results obtained from regions with more mosquito data. The differences among the regions may be related to the amount of surveillance or may be due to diverse landscape characteristics across Illinois. We set the stage for better use of all surveillance options available for WNV and described an approach to modelling that can be expanded to other mosquito-borne illnesses.
对西尼罗河病毒（WNV）和其他蚊媒病原体的监测涉及对蚊子样本进行昂贵且耗时的采集和检测。公共卫生人员面临的一个难题是如何解读与人类风险相关的蚊子数据，从而导致无法充分利用蚊子检测所获得的信息。我们研究的目的是利用从历史西尼罗河病毒蚊子检测中获得的信息来确定相对于蚊子感染的人类风险，并评估我们的蚊子感染预测模型发出预警的有效性。我们将2004年至2013年的每周蚊子感染率与伊利诺伊州的西尼罗河病毒病例数进行了比较。然后，我们开发了一个基于天气的预测模型，以在全州以及伊利诺伊州的九个地区提前一到三周估计西尼罗河病毒蚊子感染率，这是在进行蚊子检测之前。我们还进一步评估了相对于实测感染率和模型估计感染率的人类患病风险，以为公共卫生信息提供指导。我们确定，在十年间，超过一半的人类西尼罗河病毒病例发生在蚊子感染率最高的29周（共210周）之后。模型预测的值可以识别出这些时间段，但模型结果和数据可用性因地区而异，蚊子数据较多的地区得到的结果要强得多。各地区之间的差异可能与监测量有关，也可能是由于伊利诺伊州各地不同的景观特征所致。我们为更好地利用所有可用于西尼罗河病毒的监测选项奠定了基础，并描述了一种可扩展到其他蚊媒疾病的建模方法。
