Predicting Abundances of Aedes mcintoshi, a primary Rift Valley fever virus mosquito vector

Autoři: Lindsay P. Campbell aff001;  Daniel C. Reuman aff003;  Joel Lutomiah aff006;  A. Townsend Peterson aff003;  Kenneth J. Linthicum aff009;  Seth C. Britch aff009;  Assaf Anyamba aff010;  Rosemary Sang aff006
Působiště autorů: Florida Medical Entomology Laboratory, IFAS, University of Florida, Vero Beach, Florida, United States of America aff001;  Department of Entomology and Nematology, IFAS, University of Florida, Gainesville, Florida, United States of America aff002;  Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, Kansas, United States of America aff003;  Kansas Biological Survey, University of Kansas, Lawrence, Kansas, United States of America aff004;  Laboratory of Populations, Rockefeller University, New York, New York, United States of America aff005;  Kenya Medical Research Institute, Nairobi, Kenya aff006;  United States Army Medical Research Directorate – Africa, Nairobi, Kenya aff007;  Biodiversity Institute, University of Kansas, Lawrence, Kansas, United States of America aff008;  United States Department of Agriculture, Agricultural Research Service Center for Medical, Agricultural, and Veterinary Entomology, Gainesville, Florida, United States of America aff009;  Universities Space Research Association, Columbia, Maryland, United States of America aff010;  NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, Maryland, United States of America aff011
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article


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 elevated 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 high Ae. mcintoshi abundances in Somalia, independent of Kenya. Model-predicted abundances were low during the 2015–2016 period when documented outbreaks did not occur, 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.

Klíčová slova:

Epizootics – Kenya – Livestock – Mosquitoes – Surface temperature – Viral vectors – Rift Valley fever virus


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