Characterization of long-term patient-reported symptoms of COVID-19: an analysis of social media data
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Abstract
As the SARS-CoV-2 virus (COVID-19) continues to affect people across the globe, there is limited understanding of the long term implications for infected patients 1–3 . While some of these patients have documented follow-ups on clinical records, or participate in longitudinal surveys, these datasets are usually designed by clinicians, and not granular enough to understand the natural history or patient experiences of ‘long COVID’. In order to get a complete picture, there is a need to use patient generated data to track the long-term impact of COVID-19 on recovered patients in real time. There is a growing need to meticulously characterize these patients’ experiences, from infection to months post-infection, and with highly granular patient generated data rather than clinician narratives. In this work, we present a longitudinal characterization of post-COVID-19 symptoms using social media data from Twitter. Using a combination of machine learning, natural language processing techniques, and clinician reviews, we mined 296,154 tweets to characterize the post-acute infection course of the disease, creating detailed timelines of symptoms and conditions, and analyzing their symptomatology during a period of over 150 days.
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SciScore for 10.1101/2021.07.13.21260449: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources While the Twitter API only allows free users to extract a very limited set of old tweets, we used two scraping Python packages, GetOldTweets 34 and our own scraper part of SMMT35 to extract the user timelines. Pythonsuggested: (IPython, RRID:SCR_001658)In order to normalize the annotations made by our clinicians, we used the Observational Health Data Sciences and Informatics (OHDSI) vocabulary that includes a collection of biomedical controlled vocabularies such as: RxNorm, SNOMED-CT, ICD9/10, CPT, among many others. RxNormsuggested: (RxNorm, RRID:SCR_006645)Results from OddPub: We did …
SciScore for 10.1101/2021.07.13.21260449: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources While the Twitter API only allows free users to extract a very limited set of old tweets, we used two scraping Python packages, GetOldTweets 34 and our own scraper part of SMMT35 to extract the user timelines. Pythonsuggested: (IPython, RRID:SCR_001658)In order to normalize the annotations made by our clinicians, we used the Observational Health Data Sciences and Informatics (OHDSI) vocabulary that includes a collection of biomedical controlled vocabularies such as: RxNorm, SNOMED-CT, ICD9/10, CPT, among many others. RxNormsuggested: (RxNorm, RRID:SCR_006645)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a protocol registration statement.
Results from scite Reference Check: We found no unreliable references.
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