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BACKGROUND For an emergent pandemic, such as COVID-19, the statistics of symptoms based on hospital data may be biased or delayed due to the high proportion of asymptomatic or mild-symptom infections that are not recorded in hospitals. Meanwhile, the difficulty in accessing large-scale clinical data also limits many researchers from conducting timely research. OBJECTIVE Given the wide coverage and promptness of social media, this study aimed to present an efficient workflow to track and visualize the dynamic characteristics and co-occurrence of symptoms for the COVID-19 pandemic from large-scale and long-term social media data. METHODS This retrospective study included 471,553,966 COVID-19–related tweets from February 1, 2020, to April 30, 2022. We curated a hierarchical symptom lexicon for social media containing 10 affected organs/systems, 257 symptoms, and 1808 synonyms. The dynamic characteristics of COVID-19 symptoms over time were analyzed from the perspectives of weekly new cases, overall distribution, and temporal prevalence of reported symptoms. The symptom evolutions between virus strains (Delta and Omicron) were investigated by comparing the symptom prevalence during their dominant periods. A co-occurrence symptom network was developed and visualized to investigate inner relationships among symptoms and affected body systems. RESULTS This study identified 201 COVID-19 symptoms and grouped them into 10 affected body systems. There was a significant correlation between the weekly quantity of self-reported symptoms and new COVID-19 infections (Pearson correlation coefficient=0.8528; PPP CONCLUSIONS This study identified more and milder COVID-19 symptoms than clinical research and characterized the dynamic symptom evolution based on 400 million tweets over 27 months. The symptom network revealed potential comorbidity risk and prognostic disease progression. These findings demonstrate that the cooperation of social media and a well-designed workflow can depict a holistic picture of pandemic symptoms to complement clinical studies.

ImportanceCOVID-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 media’s accessibility, promptness, and rich information provide an opportunity for large-scale and long-term analyses, enabling a comprehensive symptom investigation to complement clinical studies.ObjectivePresent an efficient workflow to identify and study the characteristics and co-occurrences of COVID-19 symptoms using social media.Design, Setting, and ParticipantsThis 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 MeasuresThe 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.ResultsThe weekly quantity of self-reported symptoms has a high consistency (0.8528,P) and one-week leading trend (0. 8802,P) 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) 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 RelevanceWe found dynamic COVID-19 symptom evolution through self-reporting on social media and identified 201 symptoms from 10 affected systems. This demonstrates that social media’s prevalence trends and co-occurrence networks can efficiently identify and study public health problems, such as common symptoms during pandemics.Key pointsQuestionsWhat are the epidemic characteristics and relationships of COVID-19 symptoms that have been extensively reported on social media?FindingsThis retrospective cohort study of 948,478 related tweets (February 2020 to April 2022) from 689,551 users identified 201 self-reported COVID-19 symptoms from 10 affected systems, mitigating the potential missing information in hospital-based epidemiologic studies due to many patients not being timely diagnosed and treated. Coma, anosmia, taste sense altered, and dyspnea were less common in participants infected during Omicron prevalence than in Delta. Symptoms that affect the same system have high co-occurrence. Frequent co-occurrences occurred between symptoms and systems corresponding to specific disease progressions, such as palpitations and dyspnea, alopecia and impotence.MeaningTrend and network analysis in social media can mine dynamic epidemic characteristics and relationships between symptoms in emergent pandemics.

Nucleic acid testing (NAT) has been widely used in many fields such as medical diagnosis, food safety testing and forensic identification. However, it can only be carried out in professional laboratory because the test process is complicated and rigorous. In this paper, a nucleic acid amplification system based on polymerase chain reaction (PCR) was developed to meet the requirements of point-of-care testing (POCT) for nucleic acids. Firstly, the mechanical structure and electronic control system were designed and constructed. Secondly, an integral separation PID algorithm for temperature control and an intelligent temperature compensation method based on support vector regression (SVR) were proposed. Finally, temperature measurement and biological experiments were performed to prove the stability and availability of the nucleic acid amplification system. The results showed that the system achieved a rapid temperature change velocity of 4.5 °C/s, and the steady-state error was within ± 0.5 °C. The nucleic acids in samples of different concentrations were well amplified, the system can be used for quantitative detection of nucleic acid with the help of a fluorescence detection system, and has higher sensitivity than Tianlong PCR instrument.