New Blog Post at

Find our new blog post at

Visit our new web site at

The Yale Human Animal Medicine Project has a new web site.  You can find it at

Yale Human Animal Medicine Project Blog

Please visit Yale Human Animal Medicine project Blog at

New MSN article on Pets and Health

New article featuring Dr. Rabinowitz on the Human-Animal connection for health.

The Yale Human Animal Medicine Project has a new website and is on Twitter too!

Yale Human Animal Medicine Project has a new website!  You can follow us on Twitter too!

New Article: Informatics, Bioinformatics and Phylogeography

At the Intersection of Public-health Informatics and Bioinformatics: Using Advanced Web Technologies for Phylogeography

Scotch, Matthew

Epidemiology: November 2010 – Volume 21 – Issue 6 – pp 764-768

Summary:  Web 2.0 and the Semantic Web (3.0) provide great opportunities for biomedical informatics research and the development of informatics tools and resources to address problems across the full spectrum of health science research. One example is ZooPhy, an automated workflow for phylogeography. This system may be useful for epidemiologists who conduct surveillance and analysis of zoonotic (animal-human) agents. In addition to genetic, taxonomic, and geographical data, ZooPhy will include traditional public-health data collected by health departments. Through phylogenetics, data-mining, and machine-learning approaches, this system may help epidemiologists better understand the migration of various zoonotic diseases in animal hosts, estimate of the viral population growth within these hosts, and calculate risk to humans within a defined geographic area.

Video on Sustaining Global Surveillance and Response to Emerging Zoonotic Diseases

One Health Initiative Website

HUMAN-ANIMAL MEDICINE book referenced on OneHealth Initiative Site

New Article: Linkages between animal and human health sentinel data.

Authors: Scotch M, Odofin L, Rabinowitz P.

Journal: BMC Veterinary Research

Volume:  5

Issue: 15

Pages: on-line

Abstract: In order to identify priorities for building integrated surveillance systems that effectively model and predict human risk of zoonotic diseases, there is a need for improved understanding of the practical options for linking surveillance data of animals and humans. We conducted an analysis of the literature and characterized the linkage between animal and human health data. We discuss the findings in relation to zoonotic surveillance and the linkage of human and animal data. METHODS: The Canary Database, an online bibliographic database of animal-sentinel studies was searched and articles were classified according to four linkage categories. RESULTS: 465 studies were identified and assigned to linkage categories involving: descriptive, analytic, molecular, or no human outcomes of human and animal health. Descriptive linkage was the most common, whereby both animal and human health outcomes were presented, but without quantitative linkage between the two. Rarely, analytic linkage was utilized in which animal data was used to quantitatively predict human risk. The other two categories included molecular linkage, and no human outcomes, which present health outcomes in animals but not humans. DISCUSSION: We found limited use of animal data to quantitatively predict human risk and listed the methods from the literature that performed analytic linkage. The lack of analytic linkage in the literature might not be solely related to technological barriers including access to electronic database, statistical software packages, and Geographical Information System (GIS). Rather, the problem might be from a lack of understanding by researchers of the importance of animal data as a ‘sentinel’ for human health. Researchers performing zoonotic surveillance should be aware of the value of animal-sentinel approaches for predicting human risk and consider analytic methods for linking animal and human data. Qualitative work needs to be done in order to examine researchers’ decisions in linkage strategies between animal and human data.

Article on PubMed