Dataset Information

Coughing, sneezing, and aching online: Twitter and the volume of influenza-like illness in a pediatric hospital.

ABSTRACT: This study investigates the relation of the incidence of georeferenced tweets related to respiratory illness to the incidence of influenza-like illness (ILI) in the emergency department (ED) and urgent care clinics (UCCs) of a large pediatric hospital. We collected (1) tweets in English originating in our hospital's primary service area between 11/1/2014 and 5/1/2015 and containing one or more specific terms related to respiratory illness and (2) the daily number of patients presenting to our hospital's EDs and UCCs with ILI, as captured by ICD-9 codes. A Support Vector Machine classifier was applied to the set of tweets to remove those unlikely to be related to ILI. Time series of the pooled set of remaining tweets involving any term, of tweets involving individual terms, and of the ICD-9 data were constructed, and temporal cross-correlation between the social media and clinical data was computed. A statistically significant correlation (Spearman ? = 0.23) between tweets involving the term flu and ED and UCC volume related to ILI 11 days in the future was observed. Tweets involving the terms coughing (Spearman ? = 0.24) and headache (Spearman ? = 0.19) individually were also significantly correlated to ILI-related clinical volume four and two days in the future, respectively. In the 2014-2015 cold and flu season, the incidence of local tweets containing the terms flu, coughing, and headache were early indicators of the incidence of ILI-related cases presenting to EDs and UCCs at our children's hospital.

SUBMITTER: Hartley DM

PROVIDER: S-EPMC5533314 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Coughing, sneezing, and aching online: Twitter and the volume of influenza-like illness in a pediatric hospital.

Hartley David M DM Giannini Courtney M CM Wilson Stephanie S Frieder Ophir O Margolis Peter A PA Kotagal Uma R UR White Denise L DL Connelly Beverly L BL Wheeler Derek S DS Tadesse Dawit G DG Macaluso Maurizio M

PloS one 20170728 7

This study investigates the relation of the incidence of georeferenced tweets related to respiratory illness to the incidence of influenza-like illness (ILI) in the emergency department (ED) and urgent care clinics (UCCs) of a large pediatric hospital. We collected (1) tweets in English originating in our hospital's primary service area between 11/1/2014 and 5/1/2015 and containing one or more specific terms related to respiratory illness and (2) the daily number of patients presenting to our ho ...[more]

PMID: 28753678

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Project description:BackgroundEvidence for indoor airborne transmission of SARS-CoV-2 is accumulating.ObjectivesWe assessed of the risk of illness due to airborne SARS-CoV-2 particles from breathing, speaking, singing, coughing, and sneezing in indoor environments.MethodsA risk assessment model, AirCoV2, for exposure to SARS-CoV-2 particles in aerosol droplets was developed. Previously published data on droplets expelled by breathing, speaking, singing, coughing, and sneezing by an infected person were used as inputs. Scenarios encompassed virus concentration, exposure time, and ventilation. Newly collected data of virus RNA copies in mucus from patients are presented.ResultsThe expelled volume of aerosols was highest for a sneeze, followed by a cough, singing, speaking, and breathing. After 20 min of exposure, at 107 RNA copies/mL in mucus, all mean illness risks were largely estimated to be below 0.001, except for the "high" sneeze scenario. At virus concentrations above 108 RNA copies/mL, and after 2 h of exposure, in the high and "low" sneeze scenarios, the high cough scenario and the singing scenario, risks exceeded 0.01 and may become very high, whereas the low coughing scenario, the high and low speaking scenarios and the breathing scenario remained below 0.1. After 2 h of exposure, singing became the second highest risk scenario. One air exchange per hour reduced risk of illness by about a factor of 2. Six air exchanges per hour reduced risks of illness by a factor of 8-13 for the sneeze and cough scenarios and by a factor of 4-9 for the other scenarios.DiscussionThe large variation in the volume of expelled aerosols is discussed. The model calculations indicated that SARS-CoV-2 transmission via aerosols outside of the 1.5-m social distancing norm can occur. Virus concentrations in aerosols and/or the amount of expelled aerosol droplets need to be high for substantial transmission via this route. AirCoV2 is made available as interactive computational tool. https://doi.org/10.1289/EHP7886.

Project description:BackgroundInfluenza and Influenza-like-illness (ILI) represents a substantial public health problem, but it is difficult to measure the overall burden as many cases do not access health care. Community cohorts have the advantage of not requiring individuals to present at hospitals and surgeries and therefore can potentially monitor a wider variety of cases. This study reports on the incidence and risk factors for ILI in the UK as measured using Flusurvey, an internet-based open community cohort.MethodsUpon initial online registration participants were asked background characteristics, and every week were asked to complete a symptoms survey. We compared the representativeness of our sample to the overall population. We used two case definitions of ILI, which differed in whether fever/chills was essential. We calculated ILI incidence week by week throughout the season, and investigated risk factors associated with ever reporting ILI over the course of the season. Risk factor analysis was conducted using binomial regression.Results5943 participants joined the survey, and 4532 completed the symptoms survey at least twice. Participants who filled in symptoms surveys at least twice filled in a median of nine symptoms surveys over the course of the study. 46.1% of participants reported at least one episode of ILI, and 6.0% of all reports were positive for ILI. Females had slightly higher incidence, and individuals over 65 had the lowest incidence. Incidence peaked just before Christmas and declined dramatically during school holidays. Multivariate regression showed that, for both definitions of ILI considered, being female, unvaccinated, having underlying health issues, having contact with children, being aged between 35 and 64, and being a smoker were associated with the highest risk of reporting an ILI. The use of public transport was not associated with an increased risk of ILI.ConclusionsOur results show that internet based surveillance can be used to measure ILI and understand risk factors. Vaccination is shown to be linked to a reduced risk of reporting ILI. Taking public transport does not increase the risk of reporting ILI. Flusurvey and other participatory surveillance techniques can be used to provide reliable information to policy makers in nearly real-time.

Dataset Information

Coughing, sneezing, and aching online: Twitter and the volume of influenza-like illness in a pediatric hospital.

Publications

Coughing, sneezing, and aching online: Twitter and the volume of influenza-like illness in a pediatric hospital.

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