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Stochastic modeling of animal epidemics using data collected over three different spatial scales
Chris Rorres1, Sky T K Pelletier, Gary Smith
1School of Veterinary Medicine, University of Pennsylvania, Kennett Square, PA 19348, USA. rorres@vet.upenn.edu
Epidemics
|May 10, 2011
Summary
This study models avian influenza (bird flu) in Pennsylvania poultry farms using different spatial scales. Farm and ZIP code data accurately simulate epidemics, unlike county-level data, aiding intervention strategies.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Veterinary Science
Background:
- Avian influenza poses a significant threat to poultry health and global food security.
- Understanding epidemic dynamics across different spatial scales is crucial for effective control.
Purpose of the Study:
- To formulate and analyze a stochastic, spatial, discrete-time SEIR model for avian influenza in Pennsylvania poultry farms.
- To evaluate the impact of different spatial scales (farm, ZIP code, county) on epidemic modeling.
- To assess the utility of different data granularities for informing intervention strategies.
Main Methods:
- Developed a stochastic, spatial, discrete-time SEIR model for avian influenza.
- Clustered poultry farm data into three spatial scales: farm, ZIP code, and county.
- Estimated viral-transmission kernel parameters using simulated epidemic data for each spatial scale.
Main Results:
- Simulated epidemics using farm-level and ZIP code-level data closely mirrored actual epidemic behavior.
- County-level data provided a less accurate representation of the underlying epidemics.
- Model parameter estimation was successful across the tested spatial scales.
Conclusions:
- The spatial scale of data significantly impacts the accuracy of avian influenza epidemic simulations.
- Farm and ZIP code level data are more suitable for modeling and predicting avian influenza outbreaks in poultry.
- Accurate spatial data analysis is vital for designing effective control measures like vaccination and culling policies.
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