ReviewBulletin of mathematical biology2020
Trending on Social Media: Integrating Social Media into Infectious Disease Dynamics.
Review in Bulletin of mathematical biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Modeling and analysis of a delayed fractional order COVID-19 SEIHRM model with media coverage in Malaysia.Scientific reports · 2025Article
- Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media.Npj complexity · 2025Article
- Chaos in Opinion-Driven Disease Dynamics.Entropy (Basel, Switzerland) · 2024Article
- Bilibili, TikTok, and YouTube as sources of information on gastric cancer: assessment and analysis of the content and quality.BMC public health · 2024Article
- Evolution of Select Epidemiological Modeling and the Rise of Population Sentiment Analysis: A Literature Review and COVID-19 Sentiment Illustration.International journal of environmental research and public health · 2022Review
- Vaccine Hesitancy towards COVID-19 Vaccination: Investigating the Role of Information Sources through a Mediation Analysis.Infectious disease reports · 2021Article
- Harnessing Social Media in the Modelling of Pandemics-Challenges and Opportunities.Bulletin of mathematical biology · 2021Article
- Article
- Study on an SIHRS Model of COVID-19 Pandemic With Impulse and Time Delay Under Media Coverage.IEEE access : practical innovations, open solutions · 2021Article
- Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries.PloS one · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Social media plays an important role in alerting and educating the public during disease outbreaks. By increasing awareness of the disease and its prevention, it can lead to a modification of behaviour which then affects contact/incidence rates. Social media data may also be used when formulating, developing and parameterising models. As mobile technology continues to evolve and proliferate, social media is expected to occupy an increasingly prominent role in the field of infectious disease modelling to improve their predictive power. This article presents a review of existing models incorporating media in general and highlights opportunities for social media to enhance traditional compartmental models so as to make the best use of this resource in controlling the spread of disease.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.