Journal article
Dose-Response Model for Lassa Virus
Human and ecological risk assessment, v 14(4), pp 742-752
29 Jul 2008
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This article develops dose-response models for Lassa fever virus using data sets found in the open literature. Dose-response data were drawn from two studies in which guinea pigs were given subcutaneous and aerosol exposure to Lassa virus. In one study, six groups of inbred guinea pigs were inoculated subcutaneously with doses of Lassa virus and five groups of out-bred guinea pigs were similarly treated. We found that the out-bred subcutaneously exposed guinea pig did not exhibit a dose-dependent trend in response. The inbred guinea pigs data were best fit by an exponential dose-response model. In a second study, four groups of out-bred guinea pigs were exposed to doses of Lassa virus via the aerosol route. In that study, aerosol diameter was less than 4.5 μ m and both mortality and morbidity were used as endpoints. The log-probit dose-response model provided a somewhat better fit than the Beta-Poisson model for data with mortality as the endpoint, but the Beta-Poisson is considered the best fit model because it can be derived using biological considerations. Morbidity data were best fit with an exponential dose-response model.
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Details
- Title
- Dose-Response Model for Lassa Virus
- Creators
- Sushil B Tamrakar - Department of Civil, Architectural and Environmental Engineering , Drexel UniversityCharles N Haas - Department of Civil, Architectural and Environmental Engineering , Drexel University
- Publication Details
- Human and ecological risk assessment, v 14(4), pp 742-752
- Publisher
- Taylor & Francis Group
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:000258001200006
- Scopus ID
- 2-s2.0-48349122538
- Other Identifier
- 991014878042104721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Biodiversity Conservation
- Environmental Sciences