Invited Symposium Presentation Submission
Transforming surveillance and control effort using GIS/RS Symposium
Mohamed Sallam, Ph.D.
Uniformed Service University of the Health Sciences
Bethesda, Maryland, United States
Mosquito vectors of eastern equine encephalitis virus (EEEV) and West Nile virus (WNV) in the US reside within broad multi-species assemblages that vary in spatial and temporal composition, relative abundances, and vector competence. These variations impact the risk of pathogen transmission and the operational management of these species by local public health vector control districts. However, most models of mosquito vector dynamics focus on single species and do not account for co-occurrence probabilities between mosquito species pairs across environmental gradients. In this investigation, we use for the first time conditional Markov Random Fields (CRF) to evaluate spatial co-occurrence patterns between host-seeking mosquito vectors of EEEV and WNV around sampling sites in Manatee County, FL. Specifically, we aimed to 1) quantify correlations between mosquito vector species and other mosquito species, 2) quantify correlations between mosquito vectors and landscape and climate variables, and 3) investigate whether the strength of correlations between species pairs are conditional on landscape or climate variables. We hypothesized that either mosquito species pairs co-occur in patterns driven by the landscape and/or climate variables, or these vector species pairs are unconditionally dependent on each other regardless of the environmental variables. Results indicated that landscape and bioclimatic covariates did not substantially improve the overall model performance and that the log abundances of the majority of WNV and EEEV vector species were positively dependent on other vector and non-vector mosquito species, unconditionally. Only five individual mosquito vectors were weakly dependent on environmental variables with one exception, Culiseta melanura, the primary vector for EEEV, which showed a strong correlation with woody wetland, precipitation seasonality, and average temperature of driest quarter. Our analyses showed that majority of the studied mosquito species’ abundance and distribution are insignificantly better predicted by the biotic correlations than environmental variables. Additionally, these mosquito vector species may be habitatÂ