In the midwestern United States, the summertime rise in infection rate by the West Nile virus is associated with a seasonal shift in the abundance of two mosquito populations, Culex restuans and Culex pipiens. This seasonal shift usually precedes the time of the peak infection rate in mosquitoes by 2–3 weeks and generally occurs earlier in the summer with above normal temperatures and later in the summer with below-normal temperatures. Two empirical models were developed to predict this seasonal shift in mosquito species, or the “crossover,” and have been run operationally since 2004 by the Midwestern Regional Climate Center located at the Illinois State Water Survey. These models are based on daily temperature data and have been verified by use of a unique dataset of daily records of mosquito species abundance collected by the Illinois Natural History Survey. An unfortunate characteristic of the original temperature models was that the crossover date often was reached with little or no lead time. In 2009, the models were modified to incorporate National Weather Service (NWS) model output statistics (MOS) 10-day temperature forecasts. This paper evaluates the effectiveness of these models to predict the crossover date and thus the period of increased risk of West Nile virus in the Midwest.

For the 8-yr period from 2002 to 2009, 6 yr had at least one model predicting the crossover within one week of the actual crossover date, and for 7 yr at least one of the model predictions was within 2 weeks of the actual crossover date. Incorporation of MOS temperature forecasts for a 10-day period, although not substantially changing the predicted crossover date, greatly improved the forecast lead time by about 9 days. From a disease management point of view, this improvement in advanced notice is significant. In 2009, there was an unprecedented early crossover date and a failed forecast. The poor forecast was likely caused by an unusually early summer prolonged and intense heat wave, followed immediately by a record cold July.

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