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• W4297983863 abstract "Abstract Importance: COVID-19 is a multi-organ disease with broad-spectrum manifestations. Clinical data-driven research can be difficult because many patients do not receive prompt diagnoses, treatment, and follow-up studies. Social medias accessibility, promptness, and rich information provide an opportunity for large-scale and long-term analyses, enabling a comprehensive symptom investigation to complement clinical studies. Objective: Present an efficient workflow to identify and study the characteristics and co-occurrences of COVID-19 symptoms using social media. Design, Setting, and Participants: This retrospective cohort study analyzed 471,553,966 COVID-19-related tweets from February 1, 2020, to April 30, 2022. A comprehensive lexicon of symptoms was used to filter tweets through rule-based methods. 948,478 tweets with self-reported symptoms from 689,551 Twitter users were identified for analysis. Main Outcomes and Measures: The overall trends of COVID-19 symptoms reported on Twitter were analyzed (separately by the Delta strain and the Omicron strain) using weekly new numbers, overall frequency, and temporal distribution of reported symptoms. A co-occurrence network was developed to investigate relationships between symptoms and affected organ systems. Results: The weekly quantity of self-reported symptoms has a high consistency (0.8528, P<0.0001) and one-week leading trend (0. 8802, P<0.0001) with new infections in four countries. We grouped 201 common symptoms (mentioned ≥ 10 times) into 10 affected systems. The frequency of symptoms showed dynamic changes as the pandemic progressed, from typical respiratory symptoms in the early stage to more musculoskeletal and nervous symptoms at later stages. When comparing symptoms reported during the Delta strain versus the Omicron variant, significant changes were observed, with dropped odd ratios of coma (95%CI 0.55-0.49, P<0.01) and anosmia (95%CI, 0.6-0.56), and more pain in the throat (95%CI, 1.86-1.96) and concentration problems (95%CI, 1.58-1.70). The co-occurrence network characterizes relationships among symptoms and affected systems, both intra-systemic, such as cough and sneezing (respiratory), and inter-systemic, such as alopecia (integumentary) and impotence (reproductive). Conclusions and Relevance: We found dynamic COVID-19 symptom evolution through self-reporting on social media and identified 201 symptoms from 10 affected systems. This demonstrates that social medias prevalence trends and co-occurrence networks can efficiently identify and study public health problems, such as common symptoms during pandemics." @default.
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• W4297983863 date "2022-09-29" @default.
• W4297983863 modified "2023-10-18" @default.
• W4297983863 title "Trend and co-occurrence network study of symptoms through social media: an example of COVID-19" @default.
• W4297983863 doi "https://doi.org/10.1101/2022.09.28.22280462" @default.
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