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ARS Home » Southeast Area » Gainesville, Florida » Center for Medical, Agricultural and Veterinary Entomology » Mosquito and Fly Research » Research » Publications at this Location » Publication #364569

Research Project: Biting Arthropod Surveillance and Control

Location: Mosquito and Fly Research

Title: Predicting abundances of Aedes mcintoshi, a primary Rift Valley fever virus mosquito vector

Author
item CAMPBELL, LINDSAY - Florida Medical Entomology Laboratory
item REUMAN, DANIEL - University Of Kansas
item LUTOMIAH, JOEL - Rockefeller University
item PETERSON, A - University Of Kansas
item SANG, ROSEMARY - Kenya Medical Research Institute
item Linthicum, Kenneth - Ken
item Britch, Seth
item ANYAMBA, A - Nasa Goddard Institute For Space Studies

Submitted to: PLoS ONE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/2/2019
Publication Date: 12/17/2019
Citation: Campbell, L.P., Reuman, D.C., Lutomiah, J., Peterson, A.T., Sang, R., Linthicum, K., Britch, S.C., Anyamba, A. 2019. Predicting abundances of Aedes mcintoshi, a primary Rift Valley fever virus mosquito vector. PLoS One. 14(12):e0226617. https://doi.org/10.1371/journal.pone.0226617.
DOI: https://doi.org/10.1371/journal.pone.0226617

Interpretive Summary: The risk of globalization of viruses transmitted by mosquitoes is ever present given the complexity and density of international commerce and the myriad routes that infected mosquitoes may be transported among distant continents. One prominent high-risk pathogen is Rift Valley fever virus which under certain favorable environmental conditions may circulate widely among livestock, humans, wildlife, and mosquitoes in the Horn of Africa and be at risk of movement to North America. Current assets such as the Rift Valley Fever Monitor climate-based early warning system can provide months of lead time before an outbreak which can help target vaccines, control efforts, and public education. However, this system focuses on the environmental signals that lead to explosive growth of mosquito populations and does not include information on dynamics of mosquito populations themselves. In the present study, we investigate environmental signals that have historically been coincident with dynamics of populations of one of the main mosquito species responsible for Rift Valley fever virus transmission, Aedes mcintoshi, and show that we may be able to predict potential hot spots of virus transmission at finer scales than other models. This information could be used to enhance predictions of other models such as the Rift Valley Fever Monitor to better target virus monitoring, containment, and control, thus better protecting the US and other areas from potential escape of this virus from its native region.

Technical Abstract: Rift Valley fever virus (RVFV) is a mosquito-borne zoonotic arbovirus with important livestock and human health, and economic consequences across Africa and the Arabian Peninsula. Climate and vegetation monitoring guide RVFV forecasting models and early warning systems; however, these approaches make monthly predictions and a need exists to predict primary vector abundances at finer temporal scales. In Kenya, an important primary RVFV vector is the mosquito Aedes mcintoshi. We used a zero-inflated negative binomial regression and multimodel averaging approach with georeferenced Ae. mcintoshi mosquito counts and remotely sensed climate and topographic variables to predict where and when abundances would be high in Kenya and western Somalia. The data supported a positive effect on abundance of minimum wetness index values within 500 m of a sampling site, cumulative precipitation values 0 to 14 days prior to sampling, and land surface temperature values ~3 weeks prior to sampling. The probability of structural zero counts of mosquitoes increased as percentage clay in the soil decreased. Weekly retrospective predictions for unsampled locations across the study area between 1 September and 25 January from 2002 to 2016 predicted high abundances prior to RVFV outbreaks in multiple foci during the 2006-2007 epizootic, except for two districts in Kenya. Additionally, model predictions supported the possibility of virus circulation within Somalia, independent of Kenya. Model-predicted abundances were low during the 2015-2016 period when no outbreaks occurred, although several surveillance systems issued warnings. Model predictions prior to the 2018 RVFV outbreak indicated elevated abundances in Wajir County, Kenya, along the border with Somalia, but RVFV activity occurred west of the focus of predicted high Ae. mcintoshi abundances.