1. A GIS Tool to Estimate West Nile Virus Risk Based on a Degree-Day Model
Paper by Zou, L., S. N. Miller and E. T. Schmidtmann A GIS tool to estimate West Nile Virus risk based on a degree-day model. Environmental Monitoring and Assessment, 129(1-3): 1-7.
Presented by Sean Daniel Weyrich, Environmental Science, GEOG 370, 23 February 2008

2. Problem: Prediction using a degree-day model
Problem: As of 2004, there had been 785 human deaths as a result of the West Nile Virus. There were no means for predicting the virus’ spread. The researchers saw the use of a degree-day model as a potential aid in fighting the unknown spread of the West Nile Virus.
Hypothesis: A degree-day model would be able to accurately predict the risk of the West Nile Virus.

3. Methods, data, and test:
Site: Mosquito testing sites throughout the state of Wyoming.
The researchers received temperature data from the National Climatic Data Center. This data was requested for the months of June, July, August, and September.
Each location was analyzed based on temperature for the previous 12 days (mosquitoes maintain 12 days of host feeding activity, and thus the potential to spread the virus).
They placed the data into the degree-day model and compared their results with the actual results from the Wyoming Department of Health.

4. Results
The experiment proved that the degree-day model was a useful tool in analyzing the activity of the West Nile Virus based on insect vector populations.
Because mosquito growth is primarily dependent on temperature, they were able to predict West Nile Virus events relatively well during the warmest years of the test. For the summers of 2003 and 2005 they were able to predict 91.3%, and 78.3% of the cases.

5. Model prediction table
2003
2004
2005
Correctly identified (positive tests) 21 15 18
Not identified 2 6
Wrongly identified 3
Total accuracy (%)
91.3
65.2
78.3

6. Conclusions
The use of the degree-day model was useful for predicting the distribution of the West Nile Virus.
This program would ensure greater accuracy if it was coupled with avian and mosquito surveillance efforts.
Their model could be a useful tool because of the relatively large number of surveillance personnel required to monitor an avian surveillance system that several states employ already.
Criticism:
Their prediction vastly underestimates the West Nile Virus. The Virus’ spread does not solely depend on atmospheric temperature (what the model accounts for) but a collection of other variables most notably early summer precipitation.