Mosquitos and West Nile Virus
Two mosquito species – Culex restuans, the white-spotted mosquito, and Culex pipiens, the northern house mosquito – are believed to maintain the natural transmission cycle of West Nile Virus (WNV) between birds and mosquitoes. The population of northern house mosquitoes, the primary suspect for WNV transmission to humans, is low in spring but grows to become the dominant species later in summer, especially in urban areas. Research has found that a rise of West Nile infection in mosquitoes parallels the rise in abundance of the northern house mosquito The term “crossover” is defined here as the time when the relative proportions are equal during this transition from an early season dominance of Culex restuans and a late season dominance of Culex pipiens. . The peak infection rate in mosquitoes occurs about two to three weeks after the northern house mosquito becomes the dominant species. This peak in infection obviously represents the period of greatest risk of transmission to incidental hosts such as horses, humans and other wildlife.
Climate Basis for the Models
On average, crossover occurs in early August. However, there is considerable variability from year to year, ranging from early July to mid September. This variation introduces considerable variation from year to year in the risk of WNV infection. Recent research indicates that simple models based on temperature are able to explain much of the variance in the crossover date. Two models are used here. One is based on the number of days when the maximum temperature exceeds 81°F. The second is based on degree days with a base of 63°F.
Additional research has shown that inclusion of Model Output Statistics (MOS) allows for a more timely (on average a 7-day lead time) forecast of the crossover date. MOS is a technique used to objectively interpret model output and produce site specific (Champaign-Urbana) guidance.
These two models are used here to provide a probabilistic assessment of the likely crossover time. The approach used here is very simple:
- We assume that the MOS-predicted maximum and minimum temperatures for the next ten days provides better temperature estimates than does climatology.
- We assume that the past climate history provides an envelope of what may happen during the rest of the current year.
- We assume that the probability of what will happen during the rest of the current year is equal to the past frequency of conditions.
Specifically, each year in the historical climate database is assumed to be one scenario for the outcome of the remainder of the year. To apply this concept, the temperature time series for one scenario is assumed to be the combination of the actual observed data up to today’s date, plus MOS forecasted temperatures for the next ten days, plus the observed temperature data from some past year for all days beginning 11 days out from the current day. We then use the two models to estimate the crossover date. This process is repeated over 100 times using each year from 1900 to last year as a possible scenario for the remainder of this year. These predicted crossover dates are sorted from earliest to latest day of year. The result is a probability distribution of crossover dates; thus, this provides an estimate of both the variance and the mean of estimated crossover date.
Researchers and Organizations
- Steve Hilberg , Prairie Research Institute, Illinois State Water Survey/Midwestern Regional Climate Center
- Nancy Westcott , Prairie Research Institute, Illinois State Water Survey/ Midwestern Regional Climate Center
- Rich Lampman, Prairie Research Institute, Illinois Natural History Survey, University of Illinois
- E.J. Muturi, Prairie Research Institute, Illinois Natural History Survey, University of Illinois
- Barry Alto, Florida Medical Entomology Laboratory, University of Florida
- Ken Kunkel, NOAA's Cooperative Institute for Climate and Satellites
- Bob Glahn, NOAA's National Weather Service Office of Science & Technology, Meteorological Development Laboratory
- Michael Baker , NOAA's National Weather Service Office of Science & Technology, Meteorological Development Lab, Statistical Modeling Branch
- Kathryn Gilbert, NOAA's National Weather Service Office of Science & Technology, Meteorological Development Lab, Statistical Modeling Branch
- Scott Scallion, NOAA's National Weather Service Office of Science & Technology, Meteorological Development Lab, Statistical Modeling Branch