Real-time tracking of self-reported symptoms to predict potential COVID-19.
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ABSTRACT:
SUBMITTER: Menni C
PROVIDER: S-EPMC7751267 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
Publications
Real-time tracking of self-reported symptoms to predict potential COVID-19.
Menni Cristina C Valdes Ana M AM Freidin Maxim B MB Sudre Carole H CH Nguyen Long H LH Drew David A DA Ganesh Sajaysurya S Varsavsky Thomas T Cardoso M Jorge MJ El-Sayed Moustafa Julia S JS Visconti Alessia A Hysi Pirro P Bowyer Ruth C E RCE Mangino Massimo M Falchi Mario M Wolf Jonathan J Ourselin Sebastien S Chan Andrew T AT Steves Claire J CJ Spector Tim D TD
Nature medicine 20200511 7
A total of 2,618,862 participants reported their potential symptoms of COVID-19 on a smartphone-based app. Among the 18,401 who had undergone a SARS-CoV-2 test, the proportion of participants who reported loss of smell and taste was higher in those with a positive test result (4,668 of 7,178 individuals; 65.03%) than in those with a negative test result (2,436 of 11,223 participants; 21.71%) (odds ratio = 6.74; 95% confidence interval = 6.31-7.21). A model combining symptoms to predict probable ...[more]
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Project description:BackgroundHarnessing health-related data posted on social media in real time can offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time.ObjectiveThis study aimed to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the COVID-19 pandemic, to determine how discussion of these symptoms and medical conditions changed over time, and to identify correlations between frequency of the top 5 commonly mentioned symptoms post and daily COVID-19 statistics (new cases, new deaths, new active cases, and new recovered cases) in the United States.MethodsWe used natural language processing (NLP) algorithms to identify symptom- and medical condition-related topics being discussed on social media between June 14 and December 13, 2020. The sample posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of posts. We also assessed the frequency of health-related discussions on social media over time during the study period, and used Pearson correlation coefficients to identify statistically significant correlations between the frequency of the 5 most commonly mentioned symptoms and fluctuation of daily US COVID-19 statistics.ResultsWithin a total of 9,807,813 posts (nearly 70% were sourced from the United States), we identified a discussion of 120 symptom-related topics and 1542 medical condition-related topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). Changes in posts in the frequency of anxiety, generalized pain, and weight loss were significant but negatively correlated with daily new COVID-19 cases in the United States (r=-0.49, r=-0.46, and r=-0.39, respectively; P<.05). Posts on the frequency of anxiety, generalized pain, weight loss, fatigue, and the changes in fatigue positively and significantly correlated with daily changes in both new deaths and new active cases in the United States (r ranged=0.39-0.48; P<.05).ConclusionsCOVID-19 and symptoms of anxiety were the 2 most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population's mental health status and enhance public health surveillance for infectious disease.
| S-EPMC8480398 | biostudies-literature
Project description:ObjectivesWe aimed to objectify and compare persisting self-reported symptoms in initially hospitalized and non-hospitalized patients after infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by applying clinical standardized measures.MethodsWe conducted a cross-sectional study of adult patients with confirmed SARS-CoV-2 infection including medical history, neurological examination, blood markers, neuropsychological testing, patient-reported outcome measures (PROMs), and brain magnetic resonance imaging (MRI).ResultsFifty patients with persisting symptoms for at least 4 weeks were included and classified by initial hospitalization status. Median time from SARS-CoV-2 detection to investigation was 29.3 weeks (range 3.3-57.9). Although individual cognitive performance was generally within the normative range in both groups, mostly mild deficits were found in attention, executive functions, and memory. Hospitalized patients performed worse in global cognition, logical reasoning, and processes of verbal memory. In both groups, fatigue severity was associated with reduced performance in attention and psychomotor speed tasks (rs = -0.40, p < 0.05) and reduced quality of life (EQ5D, rs = 0.57, p < 0.001) and with more persisting symptoms (median 3 vs. 6, p < 0.01). PROMs identified fatigue, reduced sleep quality, and increased anxiety and depression in both groups but more pronounced in non-hospitalized patients. Brain MRI revealed microbleeds exclusively in hospitalized patients (n = 5).InterpretationRegardless of initial COVID-19 severity, an individuals' mental and physical health can be severely impaired in the long-term limitedly objectified by clinical standard diagnostic with abnormalities primarily found in hospitalized patients. This needs to be considered when planning rehabilitation therapies and should give rise to new biomarker research.
| S-EPMC8862437 | biostudies-literature
Project description:ObjectiveTo assess the utility of self-reported symptoms in identifying positive coronavirus disease 2019 (COVID-19) cases among predominantly healthy young adults in a military setting.MethodsA questionnaire regarding COVID-19 symptoms and exposure history was administered to all individuals contacting the Israeli Defence Forces Corona call-centre, before PCR testing. Surveyed symptoms included cough, fever, sore throat, rhinorrhoea, loss of taste or smell, chest pain and gastrointestinal symptoms. Factors were compared between positive and negative cases based on confirmatory test results, and positive likelihood ratios (LR) were calculated. Results were stratified by sex, body mass index, previous medical history and dates of questioning, and a multivariable analysis for association with positive test was conducted.ResultsOf 24 362 respondents, 59.1% were men with a median age of 20.5 years (interquartile range 19.6-22.4 years). Significant positive LRs were associated with loss of taste or smell (LR 3.38, 95% CI 3.01-3.79), suspected exposure (LR 1.33, 95% CI 1.28-1.39) and fever (LR 1.26, 95% CI 1.17-1.36). Those factors were also associated with positive PCR result in a multivariable analysis (OR 3.51, 95% CI 3.04-4.06; OR 1.86, 95% CI 1.65-2.09; and OR 1.34, 95% CI 1.19-1.51, respectively). Reports of loss of taste or smell increased gradually over time and were significantly more frequent during the late period of the study (63/5231, 1.21%; 156/7941, 1.96%; and 1505/11 190, 13.45%: p < 0.001).ConclusionLoss of taste or smell, report of a suspicious exposure and fever (>37.5°C) were consistently associated with positive LRs for a positive SARS-CoV-2 PCR test result, in a population of predominantly young and healthy adults.
| S-EPMC7837233 | biostudies-literature
Project description:Global misinformation and information overload have characterized the coronavirus disease (COVID-19) pandemic. Rumors are unverified pieces of information spreading online or person-to-person that reduce trust in health authorities and create barriers to protective practices. Risk communication and community engagement can increase transparency, build trust, and stop the spread of rumors. Building on previous work on Ebola and Zika viruses using Global Health Security Agenda systems strengthening support, the U.S. Agency for International Development-funded Breakthrough ACTION project developed a process and technology for systematically collecting, analyzing, and addressing COVID-19 rumors in real-time in Côte d'Ivoire. Rumors were submitted through community-based contributors and collected from callers to the national hotlines and then processed on a cloud-hosted database built on the open-source software District Health Information System 2 (DHIS2). Hotline teleoperators and data managers coded rumors in near-real-time according to behavioral theory frameworks within DHIS2 and visualized the findings on custom dashboards. The analysis and response were done in full collaboration with the Government of Côte d'Ivoire and implementing partners to ensure a timely and coordinated response. The system captured both widespread rumors consistent with misinformation in other settings, such as suspicions about case counts and the belief that masks were deliberately contaminated, as well as very localized beliefs related to specific influencers. The qualitative findings provided rapid insights on circulating beliefs, enabling risk communicators to nuance and tailor messaging around COVID-19.
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Project description:Analysis of COVID-19 hospitalized patients, with different kind of symptoms, by human rectal swabs collection and 16S sequencing approach.
2022-09-22 | E-MTAB-12236 | biostudies-arrayexpress
Project description: Not available
| S-BSST1493 | biostudies-other
Project description:BackgroundEarly in the pandemic, in 2020, Koehlmoos et al completed a framework synthesis of currently available self-reported symptom tracking programs for COVID-19. This framework described relevant programs, partners and affiliates, funding, responses, platform, and intended audience, among other considerations.ObjectiveThis study seeks to update the existing framework with the aim of identifying developments in the landscape and highlighting how programs have adapted to changes in pandemic response.MethodsOur team developed a framework to collate information on current COVID-19 self-reported symptom tracking programs using the "best-fit" framework synthesis approach. All programs from the previous study were included to document changes. New programs were discovered using a Google search for target keywords. The time frame for the search for programs ranged from March 1, 2021, to May 6, 2021.ResultsWe screened 33 programs, of which 8 were included in our final framework synthesis. We identified multiple common data elements, including demographic information such as race, age, gender, and affiliation (all were associated with universities, medical schools, or schools of public health). Dissimilarities included questions regarding vaccination status, vaccine hesitancy, adherence to social distancing, COVID-19 testing, and mental health.ConclusionsAt this time, the future of self-reported symptom tracking for COVID-19 is unclear. Some sources have speculated that COVID-19 may become a yearly occurrence much like the flu, and if so, the data that these programs generate is still valuable. However, it is unclear whether the public will maintain the same level of interest in reporting their symptoms on a regular basis if the prevalence of COVID-19 becomes more common.
| S-EPMC8651180 | biostudies-literature
Project description:To account for delays between specimen collection and report, the New York City Department of Health and Mental Hygiene used a time-correlated Bayesian nowcasting approach to support real-time COVID-19 situational awareness. We retrospectively evaluated nowcasting performance for case counts among residents diagnosed during March-May 2020, a period when the median reporting delay was 2 days. Nowcasts with a 2-week moving window and a negative binomial distribution had lower mean absolute error, lower relative root mean square error, and higher 95% prediction interval coverage than nowcasts conducted with a 3-week moving window or with a Poisson distribution. Nowcasts conducted toward the end of the week outperformed nowcasts performed earlier in the week, given fewer patients diagnosed on weekends and lack of day-of-week adjustments. When estimating case counts for weekdays only, metrics were similar across days the nowcasts were conducted, with Mondays having the lowest mean absolute error, of 183 cases in the context of an average daily weekday case count of 2,914. Nowcasting ensured that recent decreases in observed case counts were not overinterpreted as true declines and supported health department leadership in anticipating the magnitude and timing of hospitalizations and deaths and allocating resources geographically.
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Project description:Although mRNA coronavirus disease 2019 (COVID-19) vaccines have been reported for high effectiveness against symptoms, it remains unclear whether post-vaccination infections are less symptomatic than infections in vaccine-naive individuals. We included patients with COVID-19 diagnosed by polymerase chain reaction tests during Japan's alpha and delta variant epidemics. COVID-19 symptoms at approximately 4 weeks were compared based on COVID-19 vaccination status. In total, 398 cases (372 symptomatic and 26 asymptomatic; 286 unvaccinated, 66 vaccinated with one dose, and 46 with two doses) were analyzed. The most common symptoms were fever (78.4%), fatigue (78.4%), cough (74.4%), loss of taste or smell (62.8%), and headache (59.8%). Post-vaccination infections were significantly less likely to be symptomatic. Possible confounder-adjusted odds ratios of two vaccine doses against fatigue, dry eyes and mouth, insomnia, fever, shortness of breath, unusual muscle pains, and loss of taste or smell were 0.18 (95% confidence interval [CI]: 0.09-0.38), 0.22 (95% CI: 0.08-0.59), 0.33 (95% CI: 0.14-0.80), 0.31 (95% CI: 0.15-0.63), 0.36 (95% CI: 0.16-0.76), 0.40 (95% CI: 0.19-0.82), and 0.44 (95% CI: 0.22-0.87), respectively. Post-vaccination infections after two mRNA COVID-19 vaccine doses show milder and fewer symptoms than infections in unvaccinated patients, highlighting the effectiveness of vaccination.
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Project description:ImportanceAllergic history in individuals with confirmed anaphylaxis to a messenger RNA (mRNA) COVID-19 vaccine is common. However, the risk factors for allergy symptoms after receiving the vaccine are unknown.ObjectiveTo assess the association between self-reported history of high-risk allergy and self-reported allergic reactions after mRNA COVID-19 vaccination of health care employees.Design, setting, and participantsThis cohort study obtained demographic, medical, and vaccine administration data of employees of Mass General Brigham from the institutional electronic health record. Employees who received at least 1 dose of an mRNA COVID-19 vaccine between December 14, 2020, and February 1, 2021, and who completed at least 1 postvaccination symptom survey in the 3 days after vaccination were included.ExposuresSelf-reported history of high-risk allergy, defined as a previous severe allergic reaction to a vaccine, an injectable medication, or other allergen.Main outcomes and measuresThe primary outcome was 1 or more self-reported allergic reactions in the first 3 days after dose 1 or dose 2 of an mRNA COVID-19 vaccine. Multivariable log binomial regression was used to assess the association between allergic reactions and high-risk allergy status.ResultsA total of 52 998 health care employees (mean [SD] age, 42 [14] years; 38 167 women [72.0%]) were included in the cohort, of whom 51 706 (97.6%) received 2 doses of an mRNA COVID-19 vaccine and 474 (0.9%) reported a history of high-risk allergy. Individuals with vs without a history of high-risk allergy reported more allergic reactions after receiving dose 1 or 2 of the vaccine (11.6% [n = 55] vs 4.7% [n = 2461]). In the adjusted model, a history of high-risk allergy was associated with an increased risk of allergic reactions (adjusted relative risk [aRR], 2.46; 95% CI, 1.92-3.16), with risk being highest for hives (aRR, 3.81; 95% CI, 2.33-6.22) and angioedema (aRR, 4.36; 95% CI, 2.52-7.54).Conclusions and relevanceThis cohort study found that self-reported history of high-risk allergy was associated with an increased risk of self-reported allergic reactions within 3 days of mRNA COVID-19 vaccination. However, reported allergy symptoms did not impede the completion of the 2-dose vaccine protocol among a cohort of eligible health care employees, supporting the overall safety of mRNA COVID-19 vaccine.
| S-EPMC8548941 | biostudies-literature