|KARKI, SURENDRA - University Of Illinois
|WESTCOTT, NANCY - University Of Illinois
|BROWN, WILLIAM - University Of Illinois
|RUIZ, MARILYN - University Of Illinois
Submitted to: Zoonoses and Public Health
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/13/2017
Publication Date: 8/16/2017
Publication URL: http://handle.nal.usda.gov/10113/5852182
Citation: Karki, S., Westcott, N.E., Muturi, E.J., Brown, W.M., Ruiz, M.O. 2017. Assessing human risk of illness with West Nile virus mosquito surveillance data to improve public health preparedness. Zoonoses and Public Health. 65:177-184. doi: 10.1111/zph.12386.
Interpretive Summary: West Nile virus (WNV) is the most common mosquito-borne disease in the United States and causes substantial economic and public health burden. In Illinois, more than 2,200 human cases were reported between 2002 and 2015. Surveillance for this virus typically involves collection and testing of mosquito samples. However, this approach is costly, time-consuming, and sometimes hard to exploit due to the difficulty of interpreting mosquito data relative to human risk. We used historical mosquito surveillance and human cases data from the state of Illinois to develop a weather-based forecasting model to estimate the WNV minimum infection rate (MIR) one to three weeks ahead of mosquito testing both statewide and for nine regions. We further evaluated human illness risk relative to both the measured and the model-estimated MIR to provide guidelines for public health messages. Over ten years, more than 90% of human cases in Illinois occurred when statewide measured average MIR was higher than one, and this threshold for MIR can be applied to values forecasted by the models. The model results varied by region with much stronger results obtained from regions where more mosquito data were available. The differences observed among the regions may be related to the amount of surveillance or may be due to diverse landscape characteristics across Illinois.
Technical Abstract: 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 three 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 ten 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 modeling that can be expanded to other mosquito-borne illnesses.