A Comprehensive Dictionary and Term Variation Analysis for COVID -19 and SARS - C o V -2 (2020)

Abstract: The number of unique terms in the scientific literature used to refer to either SARS-CoV-2 or COVID-19 is remarkably large and has continued to increase rapidly despite well-established standardized terms. This high degree of term variation makes high recall identification of these important entities difficult. In this manuscript we present an extensive dictionary of terms used in the literature to refer to SARS-CoV-2 and COVID-19. We use a rule-based approach to iteratively generate new term variants, then locate these variants in a large text corpus. We compare our dictionary to an extensive collection of terminological resources, demonstrating that our resource provides a substantial number of additional terms. We use our dictionary to analyze the usage of SARS-CoV-2 and COVID-19 terms over time and show that the number of unique terms continues to grow rapidly. Our dictionary is freely available at https://github.com/ncbi-nlp/CovidTermVar .

## introduction in a public health crisis involving novel entities, such as covid-19, the disease caused by the sars-cov-2 virus, new scientific insights must be disseminated rapidly. researchers must therefore refer to the novel entities before many of their properties are understood and appropriate terminology can be proposed. for example, the first reports of the condition now known as covid-19 only refer to a general description of symptoms, such as "pneumonia of unknown aetiology" @xcite . the world health organization proposed the provisional name "2019-ncov" for the new virus on january 30, 2020 (world health organization, 2020a), prompting additional terms for the disease, including "2019-ncov infection" @xcite . the virus was then officially named "sars-cov-2" on february 11, 2020 (coronaviridae study group of the international committee on taxonomy of viruses, 2020; gorbalenya, 2020), with the disease officially named "covid-19" in a separate proposal on the same day @xcite . despite these proposals for standardized terminology, the number of unique terms used in the literature to refer to either covid-19 or sars-cov-2 has increased rapidly. figure 1 visualizes an illustrative sample of terms for covid-19 and sars-cov-2 as a timeline of their first use. some authors may be dissatisfied with the term "sars-cov-2" due to the potential for confusion with "sars-cov" @xcite . however, terminology that does not follow the existing nomenclature standards is common in the biomedical literature @xcite , and it seems likely that the exceptionally rapid growth of the literature discussing sars-cov-2 and covid-19 (teixeira da silva, 2020) would exaggerate this effect. handling term variation is critical for automatically identifying mentions of specific entities in text @xcite , thus despite many mentions of covid-19 and sars-cov-2 following the standardized terminology, identifying these important entities comprehensively -that is, with high recallrequires this high degree of variation to be addressed. while there have been many efforts to provide extensive resources related to covid-19 @xcite and systems that address identification of sars-cov-2 and covid-19 @xcite , there have been few efforts to provide comprehensive resources of terms for covid-19 or sars-cov-2 in the scientific literature. many terminological resources only include a few common variants. for example, medical subject headings (mesh) lists 12 terms for covid-19 foot_0 while the ncbi taxonomy lists 12 terms for sars-cov-2 foot_1 . a notable exception is @xcite , who provide extensive dictionaries for both covid-19 and sars-cov-2, though the last update to the dataset was in april 2020 3 . ## methods we approach the task of creating a dictionary of terms for the virus sars-cov-2 and the disease covid-19 by identifying terms that refer to these in the biomedical literature. while this approach has some similarities to named entity recognition (ner), our task differs from ner in at least two ways. first, ner is typically concerned with a class of similar entities, such as diseases, while our task is only concerned with two specific entities. second, while named entity recognition must consider each appearance of a potential term in context, in our task we only need consider whether the potential term is used to refer to the entity at least once. ## discussion while the number of unique terms for sars-cov-2 and covid-19 is large, a single term is responsible for most mentions of each concept. this suggests that an extensive dictionary may not be necessary. to determine if this is the case, we analyzed the frequency that the terms in our dictionary appear in litcovid. the distribution is long tail for both entities: the average frequency of terms for covid-19 is 275 with a median of 2, while the average frequency of terms for sars-cov-2 is 252 with a median of 3. plotting the frequency of each term against its rank on a log-log scale confirms that both distributions are zipfian (data not shown). since most terms are uncommon, however, the additional recall provided by each additional term is small. thus, achieving high recall does require an extensive dictionary. for example, identifying 99% of the covid-19 mentions identified by our dictionary requires 45 terms (27 for sars-cov-2) but identifying 99.5% of the covid-19 mentions requires 148 terms (89 for sars-cov-2). note that these values are likely underestimated, since our dictionary likely does not contain all terms for either entity. moreover, while some of the uncommon terms (e.g. "covid-2019 pneumonia") contain a more common term (e.g. "covid-2019"), many variations are not simple extensions: "covid-2019" does not contain "covid-19". thus, even for tasks where a term could be considered redundant if it contains a more common term, the list of non-redundant terms remains relatively long (112 for covid-19, 39 for sars-cov-2). ## conclusion we presented a dictionary of terms used to refer to covid-19 and sars-cov-2 in the biomedical literature. our resource cleanly separates terms for covid-19 and terms for sars-cov-2 and our comparison demonstrates that it provides a substantial number of additional synonyms not available in the existing terminologies of which we are aware. we will regularly add new terms and our dictionary is freely available at https://github.com/ncbi-nlp/covidtermvar . substantial limitations of our approach include the need to manually verify results and the limited domain, namely two specific entities. other studies have previously identified near synonyms as a significant source of synonymy for biomedical terms @xcite , and generalizing this approach to address normalization of a wider range of entities may be potentially interesting. there is also a significant amount of prior work on identifying terminology with reduced supervision @xcite @xcite , which would reduce manual involvement and/or allow more entities to be considered. finally, it would be valuable to automatically identify the most important terms to review systematically @xcite .