A spatio-temporal model for disease spread at regional level
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1
Iowa State University, United States
The statistical theory on which surveillance was originally based assumes: (1) subjects (pigs, farms) are independent, (2) all subjects have an equal probability of being selected for sampling, and (3) the farms have stable, homogenous pig populations. Traditional farms fit these assumptions, but the industry has changed over time and no longer conforms to the assumptions under which our surveillance systems were originally designed. As a result, surveillance either is not done or is done ineffectively.
We studied surveillance at the regional level with the objective of developing more efficient regional surveillance methods. We tested the hypothesis that disease exhibited a spatiotemporal pattern of spread at the regional level (just as we saw on farms). The emergence of PEDV in April 2013 provided the opportunity to examine this question. Using PEDV testing results from the Iowa State University Veterinary Diagnostic Laboratory (at the county level to protect client confidentiality), we found a spatiotemporal pattern of PEDV spread. This means that, just as for on-farm sampling, the assumptions upon which regional surveillance have been based do not hold in today's world. This is important because it means that new guidelines for regional surveillance should be developed using statistically-appropriate modelling to account for the spatial and temporal correlation in disease spread.
Keywords:
Spatio-temporal,
surveillance,
Regional,
PEDV,
Sampling
Conference:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.
Presentation Type:
Senior oral presentation
Topic:
Spatio-temporal surveillance and modeling approaches
Citation:
Wang
C and
Zimmerman
JJ
(2019). A spatio-temporal model for disease spread at regional level.
Front. Vet. Sci.
Conference Abstract:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data.
doi: 10.3389/conf.fvets.2019.05.00040
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Received:
11 Jun 2019;
Published Online:
27 Sep 2019.
*
Correspondence:
Prof. Chong Wang, Iowa State University, Ames, Iowa, 50011, United States, chwang@iastate.edu