Files and links (1)
Accepted (AM)Open Access (License Unspecified), Open
Journal article
Estimating the kernel parameters of premises-based stochastic models of farmed animal infectious disease epidemics using limited, incomplete, or ongoing data
Theoretical population biology, v 78(1), pp 46-53
01 Aug 2010
PMID: 20452368
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Three different estimators are presented for the types of parameters present in mathematical models of animal epidemics. The estimators make use of the data collected during an epidemic, which may be limited, incomplete, or under collection on an ongoing basis. When data are being collected on an ongoing basis, the estimated parameters can be used to evaluate putative control strategies. These estimators were tested using simulated epidemics based on a spatial, discrete-time, gravity-type, stochastic mathematical model containing two parameters. Target epidemics were simulated with the model and the three estimators were implemented using various combinations of collected data to independently determine the two parameters. (C) 2010 Elsevier Inc. All rights reserved.
Metrics
Details
- Title
- Estimating the kernel parameters of premises-based stochastic models of farmed animal infectious disease epidemics using limited, incomplete, or ongoing data
- Creators
- Chris Rorres - University of PennsylvaniaSky T. K. PelletierMatt J. Keeling - University of WarwickGary Smith
- Publication Details
- Theoretical population biology, v 78(1), pp 46-53
- Publisher
- Elsevier
- Number of pages
- 8
- Grant note
- 5U01GM-076426 / National Institute of General Medical Sciences; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of General Medical Sciences (NIGMS) U01GM076426 / NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of General Medical Sciences (NIGMS)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- [Retired Faculty]
- Web of Science ID
- WOS:000279767100005
- Scopus ID
- 2-s2.0-77953960708
- Other Identifier
- 991021879787904721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Ecology
- Evolutionary Biology
- Genetics & Heredity
- Mathematical & Computational Biology