4.3 Article

Impact of the Southern Oscillation Index, Temperature, and Precipitation on Eastern Equine Encephalitis Virus Activity in Florida

Journal

JOURNAL OF MEDICAL ENTOMOLOGY
Volume 57, Issue 5, Pages 1604-1613

Publisher

OXFORD UNIV PRESS INC
DOI: 10.1093/jme/tjaa084

Keywords

distributed lag nonlinear model; El Nino Southern Oscillation; cooling degree days; Eastern Equine Encephalitis virus; vector-borne disease

Funding

  1. Centers for Disease Control and Prevention [U01CK000510]

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Eastern equine encephalitis virus (EEEV), an Alphavirus from family Togaviridae, is a highly pathogenic arbovirus affecting the eastern United States, especially Florida. Effects of the Southern Oscillation Index (SOI), precipitation, and cooling degree days on EEEV horse case data in Florida from 2004 to 2018 were modeled using distributed lag nonlinear models (DLNMs).The analysis was conducted at statewide and regional scales. DLNMs were used to model potential delayed effects of the covariates on monthly counts of horse cases. Both models confirmed a seasonal trend in EEEV transmission and found that precipitation, cooling degree days, and the SOI were all predictors of monthly numbers of horse cases. EEEV activity in horses was associated with higher amounts of rainfall during the month of transmission at the statewide scale, as well as the prior 3 mo at the regional scale, fewer cooling degree days during the month of transmission and the preceding 3 mo and high SOI values during the month and the previous 2 mo, and SOI values in the prior 2 to 8 mo. Horse cases were lower during El Nino winters but higher during the following summer, while La Nina winters were associated with higher numbers of cases and fewer during the following summer. At the regional scale, extremely low levels of precipitation were associated with a suppression of EEEV cases for 3 mo. Given the periodicity and potential predictability of El Nino Southern Oscillation (ENSO) cycles, precipitation, and temperature, these results may provide a method for predicting EEEV risk potential in Florida.

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