Biologically Informed Individual-Based Network Model for Rift Valley Fever in the US and Evaluation of Mitigation Strategies.
| Autor: | Scoglio CM; Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, United States of America., Bosca C; Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, United States of America.; Department of Electronic Engineering, 'La Sapienza' University of Rome, Rome, Italy., Riad MH; Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, United States of America., Sahneh FD; Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, United States of America., Britch SC; USDA-Agricultural Research Service Center for Medical, Agricultural, and Veterinary Entomology, Gainesville, FL, United States of America., Cohnstaedt LW; USDA-Agricultural Research Service Center for Grain and Animal Health Research, Manhattan, KS, United States of America., Linthicum KJ; USDA-Agricultural Research Service Center for Medical, Agricultural, and Veterinary Entomology, Gainesville, FL, United States of America. |
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| Jazyk: | angličtina |
| Zdroj: | PloS one [PLoS One] 2016 Sep 23; Vol. 11 (9), pp. e0162759. Date of Electronic Publication: 2016 Sep 23 (Print Publication: 2016). |
| DOI: | 10.1371/journal.pone.0162759 |
| Abstrakt: | Rift Valley fever (RVF) is a zoonotic disease endemic in sub-Saharan Africa with periodic outbreaks in human and animal populations. Mosquitoes are the primary disease vectors; however, Rift Valley fever virus (RVFV) can also spread by direct contact with infected tissues. The transmission cycle is complex, involving humans, livestock, and multiple species of mosquitoes. The epidemiology of RVFV in endemic areas is strongly affected by climatic conditions and environmental variables. In this research, we adapt and use a network-based modeling framework to simulate the transmission of RVFV among hypothetical cattle operations in Kansas, US. Our model considers geo-located livestock populations at the individual level while incorporating the role of mosquito populations and the environment at a coarse resolution. Extensive simulations show the flexibility of our modeling framework when applied to specific scenarios to quantitatively evaluate the efficacy of mosquito control and livestock movement regulations in reducing the extent and intensity of RVF outbreaks in the United States. Competing Interests: The authors have declared that no competing interests exist. |
| Databáze: | MEDLINE |
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