Analysis Of Entomological Surveillance Data To Predict West Nile Virus Cases In Connecticut

Lena Marie Tayo

This thesis is restricted to Yale network users only. It will be made publicly available on 08/28/2020


INTRODUCTION: Since 1999, West Nile Virus (WNV) has been identified in a wide variety of mosquito species. Seasonal epidemics occur in the summer in all regions of the United States, with varying degrees of intensity and spread. In Connecticut, the state mosquito surveillance system for WNV has been conducted since 2000, collecting entomological and site data, and acting as an early warning system for potential human disease risk.

AIMS: Re-analysis of the epidemiology of WNV in Connecticut after years of established endemicity will be conducted using recent mosquito surveillance and human case data. A predictive model will also be developed to determine which entomological surveillance indicators are best for predicting human cases.

RESULTS AND ANALYSIS: The following were performed: 1) re-analysis of WNV epidemiology, 2) analysis of the predictive model, and 3) analysis of average seasonal lag between peak Culex pipiens abundance, significant predictive model indicators, and yearly human cases. Overall, the epidemiology of WNV in Connecticut has not differed significantly from earlier assessments. Only weekly Minimum Infection Rate (MIR) of bird-biting mosquitoes was a significant predictor of human WNV risk. Average lag between peak in human cases and peak in MIR of bird-biting mosquitoes suggested that peak MIR may indicate potential peak in human cases with a lag of 2 weeks (+3.13 weeks).

CONCLUSIONS: Entomological surveillance indicators, primarily MIR of bird-biting mosquitoes, is useful for seasonal prediction of human disease risk. Other factors important for WNV transmission, such as land use, climate, and human sociological factors, should also be included in future models to improve WNV human risk modeling.