ERAIZDA: a model for holistic annotation of animal infectious and zoonotic diseases

There is an urgent need for a unified resource that integrates trans-disciplinary annotations of emerging and reemerging animal infectious and zoonotic diseases. Such data integration will provide wonderful opportunity for epidemiologists, researchers and health policy makers to make data-driven decisions designed to improve animal health. Integrating emerging and reemerging animal infectious and zoonotic disease data from a large variety of sources into a unified open-access resource provides more plausible arguments to achieve better understanding of infectious and zoonotic diseases. We have developed a model for interlinking annotations of these diseases. These diseases are of particular interest because of the threats they pose to animal health, human health and global health security. We demonstrated the application of this model using brucellosis, an infectious and zoonotic disease. Preliminary annotations were deposited into VetBioBase database (http://vetbiobase.igbb.msstate.edu). This database is associated with user-friendly tools to facilitate searching, retrieving and downloading of disease-related information. Database URL: http://vetbiobase.igbb.msstate.edu

[1]  A. Vibhagool,et al.  Case report: Brucellosis: a re-emerging disease in Thailand. , 2004, The Southeast Asian journal of tropical medicine and public health.

[2]  L. Madoff,et al.  The internet and the global monitoring of emerging diseases: lessons from the first 10 years of ProMED-mail. , 2005, Archives of medical research.

[3]  W C Maclean.  On Malta Fever; with a Suggestion , 1875, British medical journal.

[4]  Stephen J Mooney,et al.  Commentary: Epidemiology in the Era of Big Data , 2015, Epidemiology.

[5]  Tara Anderson,et al.  FAO‐OIE‐WHO Joint Technical Consultation on Avian Influenza at the Human‐Animal Interface , 2010, Influenza and other respiratory viruses.

[6]  Rowland R Kao,et al.  Supersize me: how whole-genome sequencing and big data are transforming epidemiology , 2014, Trends in Microbiology.

[7]  S. Fitzgerald,et al.  Antimicrobial Susceptibility Testing of Mycobacterium bovis Isolates from Michigan White-Tailed Deer during the 2009 Hunting Season , 2010, Veterinary medicine international.

[8]  Richard Platt,et al.  Big data in epidemiology: too big to fail? , 2013, Epidemiology.

[9]  Christian Drosten,et al.  Rapid Detection and Quantification of RNA of Ebola and Marburg Viruses, Lassa Virus, Crimean-Congo Hemorrhagic Fever Virus, Rift Valley Fever Virus, Dengue Virus, and Yellow Fever Virus by Real-Time Reverse Transcription-PCR , 2002, Journal of Clinical Microbiology.

[10]  W C Maclean.  Sequel to a Note on Malta Fever , 1876, British medical journal.

[11]  Philip E. Bourne,et al.  Confronting the Ethical Challenges of Big Data in Public Health , 2015, PLoS Comput. Biol..

[12]  J. Pasick,et al.  The scientific rationale for the World Organisation for Animal Health standards and recommendations on avian influenza. , 2014, Revue scientifique et technique.

[13]  U. Rösler,et al.  Brucellosis - regionally emerging zoonotic disease? , 2010, Croatian medical journal.

[14]  Yu Yang,et al.  [Multiplex real-time PCR method for rapid detection of Marburg virus and Ebola virus]. , 2012, Zhonghua shi yan he lin chuang bing du xue za zhi = Zhonghua shiyan he linchuang bingduxue zazhi = Chinese journal of experimental and clinical virology.

[15]  Gregory D. Schuler,et al.  Database resources of the National Center for Biotechnology Information: update , 2004, Nucleic acids research.

[16]  Scott Federhen,et al.  The NCBI Taxonomy database , 2011, Nucleic Acids Res..

[17]  Ara Darzi,et al.  Developing public policy to advance the use of big data in health care. , 2014, Health affairs.

[18]  V. Setiawaty,et al.  Avian Influenza A(H5N1) Virus Outbreak Investigation: Application of the FAO‐OIE‐WHO Four‐way Linking Framework in Indonesia , 2015, Zoonoses and public health.

[19]  J. Maslow,et al.  Tuberculosis at the human-animal interface: an emerging disease of elephants. , 2011, Tuberculosis.

[20]  C. Salas,et al.  A pncA polymorphism to differentiate between Mycobacterium bovis and Mycobacterium tuberculosis. , 2004, Molecular and cellular probes.

[21]  Notes on Mediterranean or Malta Fever , 1893, British medical journal.

[22]  D. Semple,et al.  On the Employment of Dead Bacteria in the Serum Diagnosis of Typhoid and Malta Fever, and on an Easy Method of Extemporising a Blowpipe Flame for Making Capillary Sero-Sedimentation Tubes , 1897, British medical journal.

[23]  J. Thorpe,et al.  Big Data and Public Health: Navigating Privacy Laws to Maximize Potential , 2015, Public health reports.

[24]  John P. A. Ioannidis,et al.  Big data meets public health , 2014, Science.

[25]  Stuart T. Nichol,et al.  Rapid Diagnosis of Ebola Hemorrhagic Fever by Reverse Transcription-PCR in an Outbreak Setting and Assessment of Patient Viral Load as a Predictor of Outcome , 2004, Journal of Virology.

[26]  S. Niemann,et al.  Evaluation of Molecular-Beacon, TaqMan, and Fluorescence Resonance Energy Transfer Probes for Detection of Antibiotic Resistance-Conferring Single Nucleotide Polymorphisms in Mixed Mycobacterium tuberculosis DNA Extracts , 2006, Journal of Clinical Microbiology.

[27]  D. Bruce.  Observations on Malta Fever1 , 1889, British medical journal.

[28]  C C Godding.  Malta (Remittent) Fever , 1891, British medical journal.

[29]  H. Carabin,et al.  Emerging Zoonoses in the Southern United States: Toxocariasis, Bovine Tuberculosis and Southern Tick-Associated Rash Illness , 2010, The American journal of the medical sciences.

[30]  Zion Tsz Ho Tse,et al.  Converting Big Data into public health. , 2015, Science.

[31]  M. Zeldenrust,et al.  The value of ProMED-mail for the Early Warning Committee in the Netherlands: more specific approach recommended. , 2008, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[32]  J. Potworowski,et al.  Envisioning the Future of Veterinary Medical Education: The Association of American Veterinary Medical Colleges Foresight Project, Final Report , 2007 .

[33]  J. Malathi,et al.  Identification of bacteria in culture negative and polymerase chain reaction (PCR) positive intraocular specimen from patients with infectious endopthalmitis. , 2011, Journal of microbiological methods.

[34]  W. Venter,et al.  Comparison of Xpert MTB/RIF with Other Nucleic Acid Technologies for Diagnosing Pulmonary Tuberculosis in a High HIV Prevalence Setting: A Prospective Study , 2011, PLoS medicine.

[35]  V. Vullo,et al.  Reemergence of Human and Animal Brucellosis, Bulgaria , 2009, Emerging infectious diseases.

[36]  S. Corning.  World Organisation for Animal Health: strengthening Veterinary Services for effective One Health collaboration. , 2014, Revue scientifique et technique.

[37]  M. Khaitsa,et al.  International Infectious Disease Management: A Case Study of Internationalizing Curricula , 2013 .

[38]  Peter R. Orszag,et al.  Evaluation of Commercial Universal rRNA Gene PCR plus Sequencing Tests for Identification of Bacteria and Fungi Associated with Infectious Endocarditis , 2011, Journal of Clinical Microbiology.

[39]  Using "big data" to optimize public health outreach: answering the call to action. , 2015, JAMA dermatology.

[40]  Donald Kaye,et al.  Evaluation of ProMED-mail as an electronic early warning system for emerging animal diseases: 1996 to 2004. , 2006, Journal of the American Veterinary Medical Association.