Top

Published in:

Open Access 01-12-2017 | Database

An extensible framework and database of infectious disease for biosurveillance

Authors: Ashlynn R. Daughton, Reid Priedhorsky, Geoffrey Fairchild, Nicholas Generous, Andrea Hengartner, Esteban Abeyta, Nileena Velappan, Antonietta Lillo, Karen Stark, Alina Deshpande

Published in: BMC Infectious Diseases | Issue 1/2017

Abstract

Biosurveillance, a relatively young field, has recently increased in importance because of increasing emphasis on global health. Databases and tools describing particular subsets of disease are becoming increasingly common in the field. Here, we present an infectious disease database that includes diseases of biosurveillance relevance and an extensible framework for the easy expansion of the database.

Declich S, Carter AO. Public health surveillance: historical origins, methods and evaluation. Bull World Health Organ. 1994; 72(2):285–304.PubMedPubMedCentral

National Strategy for Biosurveillance. Technical report, The White House (July 2012). https://obamawhitehouse.archives.gov/the-pressoffice/2012/07/31/national-strategy-biosurveillance. Accessed 18 Apr 2016.

Margevicius KJ, et al. Advancing a framework to enable characterization and evaluation of data streams useful for biosurveillance. PloS ONE. 2014; 9(1):83730. doi:10.1371/journal.pone.0083730.CrossRef

Margevicius KJ, et al. The Biosurveillance Analytics Resource Directory (BARD): Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance. PLoS ONE. 2016; 11(1):0146600. doi:10.1371/journal.pone.0146600.CrossRef

FluNet. 2017. http://www.who.int/influenza/gisrs_laboratory/flunet/en/. Accessed 8 June 2017.

Brownstein JS, et al. Surveillance Sans Frontières: Internet-Based Emerging Infectious Disease Intelligence and the HealthMap Project. PLoS Med. 2008; 5(7):151. doi:10.1371/journal.pmed.0050151.CrossRef

Enterprises MOOS. Diseases Database Ver 2.0 ; Medical lists and links Diseases Database. 2016. http://www.diseasesdatabase.com/. Accessed 5 Oct 2016.

Disease Ontology - Institute for Genome Sciences @ University of Maryland. 2016. http://disease-ontology.org/. Accessed 5 Oct 2016.

Kibbe WA, et al. Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res. 2014;:1011. doi:10.1093/nar/gku1011.

10.

Schriml LM, et al. Disease Ontology: a backbone for disease semantic integration. Nucleic Acids Res. 2012; 40(D1):940–6. doi:10.1093/nar/gkr972.CrossRef

11.

Infectious disease ontology:2016. http://infectiousdiseaseontology.org/page/Main_Page. Accessed 5 Oct 2016.

12.

Cowell LG, Smith B. Infectious Disease Ontology In: Sintchenko V, editor. Infectious Disease Informatics. New York: Springer New York: 2010. p. 373–95. http://link.springer.com/chapter/10.1007%252F978-1-4419-1327-2_19. Accessed 5 Oct 2016.

13.

Brown H. Netlines. BMJ. 2001; 322(7290):872. doi:10.1136/bmj.322.7290.872/a.CrossRefPubMedCentral

14.

Rubella | German Measles | Home | CDC. 2016. http://www.cdc.gov/rubella/. Accessed 5 Oct 2016.

15.

Kassenborg H, et al. Human ingestion of Bacillus anthracis-contaminated meat–Minnesota, August 2000. MMWR. 2000; 49(36):813–6.

16.

Koetter R, Grabowski M. Anthracnose. 2017. http://www.extension.umn.edu/garden/yard-garden/trees-shrubs/anthracnose/. Accessed 5 Oct 2016.

17.

Wilcox WF. Apple Scab. Disease Identification Sheet 9, Integrated Pest Management. 1993. https://ecommons.cornell.edu/bitstream/handle/1813/43072/apple-scab-FS-NYSIPM.pdf?sequence=1%26isAllowed=y. Accessed 11 May 2017.

18.

Higa Y. Dengue Vectors and their Spatial Distribution. Trop Med Health. 2011; 39(4SUPPLEMENT):17–27. doi:10.2149/tmh.2011-S04.CrossRefPubMedPubMedCentral

19.

Hansen W. Chapter 19: Avian Pox In: Friend M, Franson JC, Chiganovich EA, editors. Field Manual of Wildlife Diseases: General Field Procedures and Diseases of Birds. Information and Technology Report 1999-2001. USGS: 2001. p. 163–70. http://www.nwhc.usgs.gov/publications/field_manual/chapter_19.pdf. Accessed 19 Apr 2016.

20.

Rolka H, O’Connor J. Real-Time Public Health Biosurveillance; Systems and Policy Considerations In: Zeng D, Chen H, Castillo-Chavez C, Lober WB, Thurmond M, editors. Infectious disease informatics and biosurveillance. London: Springer New York: 2011. p. 3–23.

21.

Overview of influenza surveillance in the United States. Technical report, Centers for Disease Control and Prevention (CDC) (February 2016). http://www.cdc.gov/flu/pdf/weekly/overview.pdf. Accessed 28 Apr 2016.

22.

Types of Influenza Viruses| Seasonal Influenza (Flu) | CDC. 2016. https://www.cdc.gov/flu/about/viruses/types.htm. Accessed 5 May 2017.

23.

PostgreSQL: Documentation. PostgreSQL Global Development Group. https://www.postgresql.org/docs/. Accessed 5 May 2017.

24.

Django. 2016. https://djangoproject.com. Accessed 19 April 2014.

25.

Lombardo JS, Burkom H, Pavlin J. ESSENCE II and the framework for evaluating syndromic surveillance systems. MMWR Suppl. 2004; 53:159–65.PubMed

26.

WordNet. A lexical database for English. 2015. https://wordnet.princeton.edu/. Accessed 5 Oct 2016.

27.

Fellbaum CD. WordNet and wordnets. In: Encyclopedia of Language and Linguistics, Set Set, vol 2.. Elsevier Science & Technology Books; Elsevier Distributor: 2005. http://www.sciencedirect.com/science/referenceworks/9780080448541. Accessed 5 Oct 2016.

28.

Christie T. Django REST Framework. 2016. http://www.django-rest-framework.org/. Accessed 19 Apr 2014.

29.

Couch RB. Orthomyxoviruses In: Baron S, editor. Medical Microbiology. 4th edn.. University of Texas Medical Branch at Galveston: 1996. http://www.ncbi.nlm.nih.gov/books/NBK8611/. Accessed 5 May 2017.

30.

Hyypiä T, et al. Classification of enteroviruses based on molecular and biological properties. J Gen Virol. 1997; 78(1):1–11. doi:10.1099/0022-1317-78-1-1.CrossRefPubMed

31.

Kuhn JH, et al. Proposal for a revised taxonomy of the family Filoviridae: classification, names of taxa and viruses, and virus abbreviations. Arch Virol. 2010; 155(12):2083–103. doi:10.1007/s00705-010-0814-x.CrossRefPubMedPubMedCentral

32.

Eick-Cost AA, Hunt DJ. Assessment of ICD-9-based Case Definitions for Influenza-like Illness Surveillance. MSMR. 2015; 22(9):2–5.PubMed

Title: An extensible framework and database of infectious disease for biosurveillance
Authors: Ashlynn R. Daughton
Reid Priedhorsky
Geoffrey Fairchild
Nicholas Generous
Andrea Hengartner
Esteban Abeyta
Nileena Velappan
Antonietta Lillo
Karen Stark
Alina Deshpande
Publication date: 01-12-2017
Publisher: BioMed Central
Published in: BMC Infectious Diseases / Issue 1/2017
Electronic ISSN: 1471-2334
DOI: https://doi.org/10.1186/s12879-017-2650-z

ACC 2024 Congress Coverage

Heart Failure Video

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

An extensible framework and database of infectious disease for biosurveillance

Abstract

ACC 2024 Congress Coverage

Year in Review: Heart failure and cardiomyopathies

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

Incentivised to exercise

New intervention for STEMI-related cardiogenic shock

» View more highlights on the ACC 2024 congress hub page

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 1/2017

Possible interference between seasonal epidemics of influenza and other respiratory viruses in Hong Kong, 2014–2017

Knowledge, attitudes, and behaviors of parents towards varicella and its vaccination

Independent prognostic role of human papillomavirus genotype in cervical cancer

Identification and antimicrobial resistance patterns of bacterial enteropathogens from children aged 0–59 months at the University Teaching Hospital, Lusaka, Zambia: a prospective cross sectional study

Relationships between sickle cell trait, malaria, and educational outcomes in Tanzania

Secondary organizing pneumonia following viral pneumonia caused by severe influenza B: a case report and literature reviews

ACC 2024 Congress Coverage

Year in Review: Heart failure and cardiomyopathies

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

Incentivised to exercise

New intervention for STEMI-related cardiogenic shock

» View more highlights on the ACC 2024 congress hub page