Informing epidemic (research) responses in a timely fashion by knowledge management - a Zika virus use case.


Journal

Biology open
ISSN: 2046-6390
Titre abrégé: Biol Open
Pays: England
ID NLM: 101578018

Informations de publication

Date de publication:
02 12 2020
Historique:
pubmed: 6 11 2020
medline: 13 10 2021
entrez: 5 11 2020
Statut: epublish

Résumé

The response of pathophysiological research to emerging epidemics often occurs after the epidemic and, as a consequence, has little to no impact on improving patient outcomes or on developing high-quality evidence to inform clinical management strategies during the epidemic. Rapid and informed guidance of epidemic (research) responses to severe infectious disease outbreaks requires quick compilation and integration of existing pathophysiological knowledge. As a case study we chose the Zika virus (ZIKV) outbreak that started in 2015 to develop a proof-of-concept knowledge repository. To extract data from available sources and build a computationally tractable and comprehensive molecular interaction map we applied generic knowledge management software for literature mining, expert knowledge curation, data integration, reporting and visualization. A multi-disciplinary team of experts, including clinicians, virologists, bioinformaticians and knowledge management specialists, followed a pre-defined workflow for rapid integration and evaluation of available evidence. While conventional approaches usually require months to comb through the existing literature, the initial ZIKV KnowledgeBase (ZIKA KB) was completed within a few weeks. Recently we updated the ZIKA KB with additional curated data from the large amount of literature published since 2016 and made it publicly available through a web interface together with a step-by-step guide to ensure reproducibility of the described use case. In addition, a detailed online user manual is provided to enable the ZIKV research community to generate hypotheses, share knowledge, identify knowledge gaps, and interactively explore and interpret data. A workflow for rapid response during outbreaks was generated, validated and refined and is also made available. The process described here can be used for timely structuring of pathophysiological knowledge for future threats. The resulting structured biological knowledge is a helpful tool for computational data analysis and generation of predictive models and opens new avenues for infectious disease research. ZIKV Knowledgebase is available at www.zikaknowledgebase.eu.

Identifiants

pubmed: 33148605
pii: bio.053934
doi: 10.1242/bio.053934
pmc: PMC7725600
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2020. Published by The Company of Biologists Ltd.

Déclaration de conflit d'intérêts

Competing interestsThe authors declare no competing or financial interests.

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Auteurs

Angela Bauch (A)

Biomax Informatics AG, 82152 Planegg, Germany angela.bauch@biomax.com.

Johann Pellet (J)

European Institute of Systems Biology and Medicine, 69390 Lyon, France.

Tina Schleicher (T)

Biomax Informatics AG, 82152 Planegg, Germany.

Xiao Yu (X)

Department of Medical Microbiology, Amsterdam UMC, University of Amsterdam, Amsterdam 1105 AZ, the Netherlands.

Andrea Gelemanović (A)

Department of Public Health, University of Split School of Medicine, 21000 Split, Croatia.

Cosimo Cristella (C)

Department of Medical Microbiology, Amsterdam UMC, University of Amsterdam, Amsterdam 1105 AZ, the Netherlands.

Pieter L Fraaij (PL)

Department of Viroscience and Department of Paediatrics, Erasmus Medical Centre, 3000 CA Rotterdam, the Netherlands.

Ozren Polasek (O)

Department of Public Health, University of Split School of Medicine, 21000 Split, Croatia.

Charles Auffray (C)

European Institute of Systems Biology and Medicine, 69390 Lyon, France.

Dieter Maier (D)

Biomax Informatics AG, 82152 Planegg, Germany.

Marion Koopmans (M)

Department of Viroscience and Department of Paediatrics, Erasmus Medical Centre, 3000 CA Rotterdam, the Netherlands.

Menno D de Jong (MD)

Department of Medical Microbiology, Amsterdam UMC, University of Amsterdam, Amsterdam 1105 AZ, the Netherlands.

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