Integrating the mass of growing and constantly changing data generated by the multiple disciplines - from immunochemistry to behavioral population modeling - involved in fighting the COVID-19 pandemic is one of the biggest challenges faced by researchers and public health officials.
Integrating the mass of growing and constantly changing data generated by the multiple disciplines - from immunochemistry to behavioral population modeling - involved in fighting the COVID-19 pandemic is one of the biggest challenges faced by researchers and public health officials.
All the data collected by biologists, pathologists, sociologists, geographers, physicians, and epidemiologists are captured using discipline-specific terms and are often accessible only to those working in the same field.
Integrating data deriving from multiple diverse sources is critical because it is difficult to predict the combinations of factors that help in understanding how the SARS-CoV-2 pathogen affects its human hosts. Ontologies can help scientists and public health officials in overcoming this challenge as they are data-sharing tools that provide for interoperability through a computerized lexicon with taxonomy and a set of terms and relations with logically structured definitions.
According to Barry Smith, SUNY Distinguished Professor of philosophy and director of National Center for Ontological Research (NCOR), ontologies help to fill the need for standardized terminology in describing coronavirus data and information, and because they are all constructed in the same way, they make it easier to compare COVID-19 data with data about other coronavirus diseases and the novel coronavirus diseases of the future. Smith added, “An Infectious Disease Ontology (IDO) can also contribute to solving the problem of reproducibility.”
Reproducing the results of experiments as part of the research process requires a precise description not merely of the results achieved, but also of the protocols, statistics, equipment, samples, and tests used. It is expected that the IDO when used in combination with other life science ontologies such as the ontology for biomedical investigations, offers a promising strategy for the creation of comparable, integratable, and discoverable provenance metadata for the data generated in infectious disease research.
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