Evaluation Of The Current State Of Florida West Nile Surveillance Program As A Predictor For Control And Prevention Of Human West Nile Diseases

West Nile is an important novel virus in the United States, having spread rapidly since it was first detected in New York in 1999. The Centers for Disease Control and Prevention as well as many State Health Departments, have mandated programs for surveillance of West Nile Virus activity. These programs incorporate many different aspects including existing arboserology programs with additional testing for West Nile Virus and new plans that incorporate active and passive surveillance methods. The objective of this study was to examine all aspects of the Florida West Nile surveillance program to determine if there was transmission in the animal systems prior to human cases. The predictive analyses were done using regional data graphs, spatial information, correlations and regression models. Data for sentinel chickens, bird necropsy and mosquito pool surveillance from participating counties in Florida were obtained from the State of Florida surveillance database. The human data was obtained from the State of Florida reportable disease database for each county whether participating in the state surveillance programs or not. Clinical cases were examined by demographics (gender and age) and an incidence rate was calculated to demonstrate the effects of disease. Specific statistical methods used included Pearson\u27s coefficient correlation, Poisson distribution regression modeling to show if any of the surveillance systems were predictors for human disease. The incidence rate analysis for clinical cases showed clustering of cases in adjacent counties within a region where Florida\u27s panhandle and adjacent counties northeast had the highest incidence. Florida\u27s central and southern regions had moderate human incidence. This provides useful information in transmission geography for prevention and control measures. Demographic analysis showed that there were twice as many males than females diagnosed with West Nile in Florida, this was true across the groups as well. The highest number of cases was seen within the age group over 55 years of age for West Nile Neuroinvasive Disease and for West Nile Fever the highest number of cases was within the 36-54 age range. The temporal distribution was determined using graphical representations of all of the surveillance types and clinical cases. In order to include all relevant data, the temporality was set from week 20 to week 52. This study found that all of the surveillance types (dead birds, mosquitoes and sentinels) offered a specialized strength for predicting clinical cases. However, mosquitoes proved to be the least efficient out of the three surveillance systems. The regional and spatial analysis showed that positive dead birds and sentinels provided the coverage for the surveillance systems in the state. However, Pearson\u27s correlation coefficient was low for sentinel surveillance; this may be due to higher participation showing West Nile Virus activity in areas (especially rural) that have no reported human cases. This analysis did show that West Nile is detected in mosquito pool samples before it is detected in the dead bird or sentinel surveillance systems which provides an earlier warning for human cases. The Poisson distribution regression model was only useful for the pooled years and 2003. These showed that mosquitoes, positive dead birds and sentinels were good predictors for clinical cases for the combined years and dead birds and sentinels were significant for 2003 as well. The recommendations based on the results from this study would be to continue all the current surveillance efforts but with the following enhancements: 1. Increase the coverage and consistency of submissions for all surveillance types. 2. Set standard levels of participation for all counties based on the regional analyses and populations at risk. 3. Create standardized approaches for sampling, shipping and submitting samples (especially for mosquito pool submissions) and require that participating counties adhere to these standards. 4. Only submit specific birds known to be especially susceptible to West Nile Virus (e.g. corvids). 5. Targeted prevention and education strategies for higher risk groups based on their potential levels of exposure