ArticlePloS one2019
Incorporating media data into a model of infectious disease transmission.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- One health, one earth, one life: The overlooked role of veterinarians in the fight against COVID-19 and other public health emergencies in Italy.One health (Amsterdam, Netherlands) · 2025Article
- Forecasting the trend of tuberculosis incidence in Anhui Province based on machine learning optimization algorithm, 2013-2023.BMC pulmonary medicine · 2024Article
- Risk communication and community engagement capacity in the Eastern Mediterranean Region: a call for action.BMJ global health · 2024Article
- The role of social media literacy in infodemic management: a systematic review.Frontiers in digital health · 2024Review
- National or local infodemic? The demand for news in Italy during COVID-19.International journal of health economics and management · 2023Article
- Article
- Relations of Adolescent Knowledge of COVID-19, Social Media Engagement, and Experiences During Quarantine/Lockdown with Well-Being.Journal of child and family studies · 2023Article
- The dramatic increase in anti-vaccine discourses during the COVID-19 pandemic: a social network analysis of Twitter.Human vaccines & immunotherapeutics · 2022Article
- Research on the Influence of Information Diffusion on the Transmission of the Novel Coronavirus (COVID-19).International journal of environmental research and public health · 2022Article
- Examining the role of person-to-person transmission during a verocytotoxigenic Escherichia coli outbreak in Ontario, Canada.BMC research notes · 2022Article
- Role of Infodemics on Social Media in the Development of People's Readiness to Follow COVID-19 Preventive Measures.International journal of environmental research and public health · 2022Article
- Online Collaborative Documents as Media Logic: The Mediatization of Risk Response in the Post-pandemic Era.Frontiers in psychology · 2022Article
- Analysis of COVID-19 Collective Irrationalities Based on Epidemic Psychology.Frontiers in psychology · 2022Article
- Dynamical SEIR Model With Information Entropy Using COVID-19 as a Case Study.IEEE transactions on computational social systems · 2021Article
- Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study.JMIR medical informatics · 2021Article
- Promoting Mask Use on TikTok: Descriptive, Cross-sectional Study.JMIR public health and surveillance · 2021Article
- Infodemic vs. Pandemic Factors Associated to Public Anxiety in the Early Stage of the COVID-19 Outbreak: A Cross-Sectional Study in China.Frontiers in public health · 2021Article
- The Impact of Social Media on the Acceptance of the COVID-19 Vaccine: A Cross-Sectional Study from Saudi Arabia.Patient preference and adherence · 2021Article
- Understanding the dynamics emerging from infodemics: a call to action for interdisciplinary research.SN business & economics · 2021Article
- The COVID-19 social media infodemic.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the effect of media on disease spread can help improve epidemic forecasting and uncover preventive measures to slow the spread of disease. Most previously introduced models have approximated media effect through disease incidence, making media influence dependent on the size of epidemic. We propose an alternative approach, which relies on real data about disease coverage in the news, allowing us to model low incidence/high interest diseases, such as SARS, Ebola or H1N1. We introduce a network-based model, in which disease is transmitted through local interactions between individuals and the probability of transmission is affected by media coverage. We assume that media attention increases self-protection (e.g. hand washing and compliance with social distancing), which, in turn, decreases disease model. We apply the model to the case of H1N1 transmission in Mexico City in 2009 and show how media influence-measured by the time series of the weekly count of news articles published on the outbreak-helps to explain the observed transmission dynamics. We show that incorporating the media attention based on the observed media coverage of the outbreak better estimates the disease dynamics from what would be predicted by using media function that approximate the media impact using the number of cases and rate of spread. Finally, we apply the model to a typical influenza season in Washington, DC and estimate how the transmission pattern would have changed given different levels of media coverage.
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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.