The majority of emerging infectious diseases that affect humans are zoonotic;diseases that are transmittable between animals and humans. The health of animals can be a sentinel for zoonotic diseases in humans. Unfortunately, most local and state health department epidemiologists do not have automated access to this data. Using data on animal health to predict risk of zoonotic diseases in humans could allow epidemiologists to detect public health threats sooner. Earlier detection means earlier intervention which could lead to less morbidity and mortality. This career award will study this problem by: gaining an understanding of the data and technology needs for zoonotic disease surveillance at the local and state administrative level (Aim 1), applying these needs to the development of a pilot 'animal-human'surveillance system that integrates health data of animals and humans (Aim 2), and evaluating the potential of this novel system for zoonotic disease surveillance (Aim 3). The completion of this 3-step process will establish a framework for integrating health data of animals and humans.
Aim 1 will be addressed through a mixed model design using qualitative observation of applied zoonotic surveillance, and an electronic survey to asses the data and technology needs involved in this process. The qualitative portion will consist of observation and interviews of individuals who practice zoonotic surveillance at health departments and diagnostic laboratories in Connecticut.
Aim 2 will entail the development of a pilot animal-human zoonotic surveillance system, based on the identified needs from Aim 1, that contains a usability-tested interface for the analysis of disease trends in humans.
The final Aim (3) will serve to asses the potential of an animal-human zoonotic surveillance system by conducting a between subjects comparative evaluation of the 'animal-human'system vs. a 'human-only'(a system containing only human public health data). The two systems will be evaluated by current and future professionals (graduate students) in Connecticut for analyzing trends of different zoonotic diseases in humans. This work will provide a framework for integrating animal and human data and demonstrate the potential of this synergy in surveillance of zoonotic disease. It will hopefully lead to the development of powerful surveillance systems in local and state health departments.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Transition Award (R00)
Project #
5R00LM009825-04
Application #
8139966
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Sim, Hua-Chuan
Project Start
2008-09-30
Project End
2013-09-29
Budget Start
2011-09-30
Budget End
2012-09-29
Support Year
4
Fiscal Year
2011
Total Cost
$238,462
Indirect Cost
Name
Arizona State University-Tempe Campus
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
943360412
City
Tempe
State
AZ
Country
United States
Zip Code
85287
Scotch, Matthew; Suchard, Marc A; Rabinowitz, Peter M (2015) Analysis of Viral Genetics for Estimating Diffusion of Influenza A H6N1. AMIA Jt Summits Transl Sci Proc 2015:36-40
Magee, Daniel; Scotch, Matthew (2015) Conceptualizing a Novel Quasi-Continuous Bayesian Phylogeographic Framework for Spatiotemporal Hypothesis Testing. AMIA Jt Summits Transl Sci Proc 2015:212-6
Magee, Daniel; Beard, Rachel; Suchard, Marc A et al. (2015) Combining phylogeography and spatial epidemiology to uncover predictors of H5N1 influenza A virus diffusion. Arch Virol 160:215-24
Beard, Rachel; Magee, Daniel; Suchard, Marc A et al. (2014) Generalized linear models for identifying predictors of the evolutionary diffusion of viruses. AMIA Jt Summits Transl Sci Proc 2014:23-8
Kane, Michael J; Price, Natalie; Scotch, Matthew et al. (2014) Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks. BMC Bioinformatics 15:276
Scotch, Matthew; Lam, Tommy Tsan-Yuk; Pabilonia, Kristy L et al. (2014) Diffusion of influenza viruses among migratory birds with a focus on the Southwest United States. Infect Genet Evol 26:185-93
Scotch, Matthew; Mei, Changjiang (2013) Phylogeography of swine influenza H3N2 in the United States: translational public health for zoonotic disease surveillance. Infect Genet Evol 13:224-9
Scotch, Matthew; Baarson, Brittany; Beard, Rachel et al. (2013) Examining the differences in format and characteristics of zoonotic virus surveillance data on state agency websites. J Med Internet Res 15:e90
Scotch, Matthew; Mei, Changjiang; Makonnen, Yilma J et al. (2013) Phylogeography of influenza A H5N1 clade 2.2.1.1 in Egypt. BMC Genomics 14:871
Scotch, Matthew; Sarkar, Indra Neil; Mei, Changjiang et al. (2011) Enhancing phylogeography by improving geographical information from GenBank. J Biomed Inform 44 Suppl 1:S44-7

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