Clinical Age-Specific Seasonal Conjunctivitis Patterns and Their Online Detection in Twitter, Blog, Forum, and Comment Social Media Posts

Purpose: We sought to determine whether big data from social media might reveal seasonal trends of conjunctivitis, most forms of which are nonreportable.

Methods: Social media posts (from Twitter, and from online forums and blogs) were classified by age and by conjunctivitis type (allergic or infectious) using Boolean and machine learning methods. Based on spline smoothing, we estimated the circular mean occurrence time (a measure of central tendency for occurrence) and the circular variance (a measure of uniformity of occurrence throughout the year, providing an index of seasonality). Clinical records from a large tertiary care provider were analyzed in a similar way for comparison.

Results: Social media posts machine-coded as being related to infectious conjunctivitis showed similar times of occurrence and degree of seasonality to clinical infectious cases, and likewise for machine-coded allergic conjunctivitis posts compared to clinical allergic cases. Allergic conjunctivitis showed a distinctively different seasonal pattern than infectious conjunctivitis, with a mean occurrence time later in the spring. Infectious conjunctivitis for children showed markedly greater seasonality than for adults, though the occurrence times were similar; no such difference for allergic conjunctivitis was seen.

Conclusions: Social media posts broadly track the seasonal occurrence of allergic and infectious conjunctivitis, and may be a useful supplement for epidemiologic monitoring.

Medienart:

E-Artikel

Erscheinungsjahr:

2018

Erschienen:

2018

Enthalten in:

Zur Gesamtaufnahme - volume:59

Enthalten in:

Investigative ophthalmology & visual science - 59(2018), 2 vom: 01. Feb., Seite 910-920

Sprache:

Englisch

Beteiligte Personen:

Deiner, Michael S [VerfasserIn]
McLeod, Stephen D [VerfasserIn]
Chodosh, James [VerfasserIn]
Oldenburg, Catherine E [VerfasserIn]
Fathy, Cherie A [VerfasserIn]
Lietman, Thomas M [VerfasserIn]
Porco, Travis C [VerfasserIn]

Links:

Volltext

Themen:

Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Validation Study

Anmerkungen:

Date Completed 06.08.2018

Date Revised 13.02.2024

published: Print

Citation Status MEDLINE

doi:

10.1167/iovs.17-22818

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM281091307