Misinformation making a disease outbreak worse: outcomes compared for influenza, monkeypox, and norovirus

Health misinformation can exacerbate infectious disease outbreaks. Especially pernicious advice could be classified as “fake news”: manufactured with no respect for accuracy and often integrated with emotive or conspiracy-framed narratives. We built an agent-based model that simulated separate but linked circulating contagious disease and sharing of health advice (classified as useful or harmful). Such advice has potential to influence human risk-taking behavior and therefore the risk of acquiring infection, especially as people are more likely in observed social networks to share bad advice. We test strategies proposed in the recent literature for countering misinformation. Reducing harmful advice from 50% to 40% of circulating information, or making at least 20% of the population unable to share or believe harmful advice, mitigated the influence of bad advice in the disease outbreak outcomes. How feasible it is to try to make people “immune” to misinformation or control spread of harmful advice should be explored.

[1]  David A. Broniatowski,et al.  Modeling Influenza by Modulating Flu Awareness , 2016, SBP-BRiMS.

[2]  Niel Hens,et al.  Lessons from a decade of individual-based models for infectious disease transmission: a systematic review (2006-2015) , 2017, BMC Infectious Diseases.

[3]  Patrick Vinck,et al.  Institutional trust and misinformation in the response to the 2018-19 Ebola outbreak in North Kivu, DR Congo: a population-based survey. , 2019, The Lancet. Infectious diseases.

[4]  E. Glaeser,et al.  Regulating Misinformation , 2006 .

[5]  U. Netlogo Wilensky,et al.  Center for Connected Learning and Computer-Based Modeling , 1999 .

[6]  Michael P. Wellman Putting the agent in agent-based modeling , 2016, Autonomous Agents and Multi-Agent Systems.

[7]  M. Cetron,et al.  Attitudes toward the use of quarantine in a public health emergency in four countries. , 2006, Health affairs.

[8]  Thespina Yamanis,et al.  Fears and Misperceptions of the Ebola Response System during the 2014-2015 Outbreak in Sierra Leone , 2016, PLoS neglected tropical diseases.

[9]  Louise Swift,et al.  Wildlife Trade and the Emergence of Infectious Diseases , 2007, EcoHealth.

[10]  Julii Brainard,et al.  An agent-based model about the effects of fake news on a norovirus outbreak. , 2020, Revue d'epidemiologie et de sante publique.

[11]  E. Tenkorang,et al.  Effect of knowledge and perceptions of risks on Ebola-preventive behaviours in Ghana. , 2018, International health.

[12]  Signe Smith Jervelund How social media is transforming the spreading of knowledge: Implications for our perceptions concerning vaccinations and migrant health , 2018, Scandinavian journal of public health.

[13]  Miaohua Jiang,et al.  Public avoidance and epidemics: insights from an economic model. , 2011, Journal of theoretical biology.

[14]  Elizabeth A. Casman,et al.  Incorporating individual health-protective decisions into disease transmission models: a mathematical framework , 2012, Journal of The Royal Society Interface.

[15]  Daniel B Di Giulio,et al.  Human monkeypox. , 2004, The Lancet. Infectious diseases.

[16]  Deqiao Tian,et al.  Comparison and Analysis of Biological Agent Category Lists Based On Biosafety and Biodefense , 2014, PloS one.

[17]  Martina Morris,et al.  EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks , 2017, bioRxiv.

[18]  Christopher C. French,et al.  Measuring Belief in Conspiracy Theories: The Generic Conspiracist Beliefs Scale , 2013, Front. Psychol..

[19]  R. Kelly Garrett,et al.  The “Echo Chamber” Distraction: Disinformation Campaigns are the Problem, Not Audience Fragmentation , 2017 .

[20]  Jakob D. Jensen,et al.  The Advantages of Compliance or the Disadvantages of Noncompliance? A Meta-Analytic Review of the Relative Persuasive Effectiveness of Gain-Framed and Loss-Framed Messages , 2006 .

[21]  C. Gerdil The annual production cycle for influenza vaccine. , 2003, Vaccine.

[22]  S. Cauchemez,et al.  Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature , 2014, BMC Infectious Diseases.

[23]  Junjie Chen,et al.  A novel rumour propagation model on social networks , 2017, Int. J. Sens. Networks.

[24]  Nick Rochlin,et al.  Fake news: belief in post-truth , 2017, Libr. Hi Tech.

[25]  R. Mikolajczyk,et al.  Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases , 2008, PLoS medicine.

[26]  Miriam J. Metzger,et al.  The science of fake news , 2018, Science.

[27]  Charles E. M. Pearce,et al.  The exact solution of the general stochastic rumour , 2000 .

[28]  Michael A. Andrews,et al.  Disease Interventions Can Interfere with One Another through Disease-Behaviour Interactions , 2015, PLoS Comput. Biol..

[29]  J. Mesquita,et al.  A foodborne outbreak of norovirus gastroenteritis associated with a Christmas dinner in Porto, Portugal, December 2008. , 2009, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[30]  Yamir Moreno,et al.  Theory of Rumour Spreading in Complex Social Networks , 2007, ArXiv.

[31]  Benjamin J Chapman,et al.  University students' hand hygiene practice during a gastrointestinal outbreak in residence: what they say they do and what they actually do. , 2009, Journal of environmental health.

[32]  Stuart Blume,et al.  Anti-vaccination movements and their interpretations. , 2006, Social science & medicine.

[33]  Madhav V. Marathe,et al.  Modeling interaction between individuals, social networks and public policy to support public health epidemiology , 2009, Proceedings of the 2009 Winter Simulation Conference (WSC).

[34]  Alice J Hausman,et al.  If You Ask Them, Will They Come? Predictors of Quarantine Compliance During a Hypothetical Avian Influenza Pandemic: Results From a Statewide Survey , 2010, Disaster Medicine and Public Health Preparedness.

[35]  Michael Taylor,et al.  Multiple sources and routes of information transmission: Implications for epidemic dynamics. , 2011, Mathematical biosciences.

[36]  Marc Van Ranst,et al.  Emergence of Monkeypox as the Most Important Orthopoxvirus Infection in Humans , 2018, Front. Public Health.

[37]  Peiqing Huang,et al.  Analyzing the Dynamics of a Rumor Transmission Model with Incubation , 2012 .

[38]  H. Beanlands,et al.  Risk perception and compliance with quarantine during the SARS outbreak. , 2005, Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing.

[39]  K. Gaythorpe,et al.  Norovirus transmission dynamics: a modelling review , 2017, Epidemiology and Infection.

[40]  Liang Mao,et al.  Predicting Self-Initiated Preventive Behavior Against Epidemics with an Agent-Based Relative Agreement Model , 2015, J. Artif. Soc. Soc. Simul..

[41]  Eli P. Fenichel,et al.  Adaptive human behavior in epidemiological models , 2011, Proceedings of the National Academy of Sciences.

[42]  Kimmo Kaski,et al.  Calling Dunbar's numbers , 2016, Soc. Networks.

[43]  Felipe Núñez,et al.  A Rule-based Model of a Hypothetical Zombie Outbreak: Insights on the role of emotional factors during behavioral adaptation of an artificial population , 2012, ArXiv.

[44]  Justin Lessler,et al.  Incubation periods of viral gastroenteritis: a systematic review , 2013, BMC Infectious Diseases.

[45]  A. Handel,et al.  Association of host, agent and environment characteristics and the duration of incubation and symptomatic periods of norovirus gastroenteritis , 2014, Epidemiology and Infection.

[46]  Chengqing Zong,et al.  Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) , 2015, IJCNLP 2015.

[47]  Katharine H. McVeigh,et al.  Case Fatality Rates Based on Population Estimates of Influenza-Like Illness Due to Novel H1N1 Influenza: New York City, May–June 2009 , 2010, PloS one.

[48]  Joshua M. Epstein,et al.  Coupled Contagion Dynamics of Fear and Disease: Mathematical and Computational Explorations , 2007, PloS one.

[49]  Daniel I. S. Rosenbloom,et al.  Imitation dynamics of vaccination behaviour on social networks , 2011, Proceedings of the Royal Society B: Biological Sciences.

[50]  Elia Gabarron,et al.  Ebola, Twitter, and misinformation: a dangerous combination? , 2014, BMJ : British Medical Journal.

[51]  Marco Conti,et al.  The structure of online social networks mirrors those in the offline world , 2015, Soc. Networks.

[52]  Michael Small,et al.  Impact of asymptomatic infection on coupled disease-behavior dynamics in complex networks , 2016, 1608.04049.

[53]  James O. Lloyd-Smith,et al.  Inference of R 0 and Transmission Heterogeneity from the Size Distribution of Stuttering Chains , 2013, PLoS Comput. Biol..

[54]  Katherine L. Milkman,et al.  What Makes Online Content Viral? , 2012 .

[55]  Kathleen A Martin Ginis,et al.  The Effects of Gain- versus Loss-Framed Messages Following Health Risk Information on Physical Activity in Individuals With Multiple Sclerosis , 2017, Journal of health communication.

[56]  Jeroen C. J. H. Aerts,et al.  The effectiveness of flood risk communication strategies and the influence of social networks-Insights from an agent-based model , 2016 .

[57]  A. Fajebe Computational modeling of spontaneous behavior changes and infectious disease spread , 2016 .

[58]  Benjamin J Cowling,et al.  Viral Shedding and Clinical Illness in Naturally Acquired Influenza Virus Infections , 2010, The Journal of infectious diseases.

[59]  P. Hobson-West,et al.  'Trusting blindly can be the biggest risk of all': organised resistance to childhood vaccination in the UK. , 2007, Sociology of health & illness.

[60]  Sinan Aral,et al.  The spread of true and false news online , 2018, Science.

[61]  Alberto d'Onofrio,et al.  Information-related changes in contact patterns may trigger oscillations in the endemic prevalence of infectious diseases. , 2009, Journal of theoretical biology.

[62]  T. R. Peters,et al.  Multi-scale modeling for the transmission of influenza and the evaluation of interventions toward it , 2015, Scientific Reports.

[63]  M. Gentzkow,et al.  Social Media and Fake News in the 2016 Election , 2017 .

[64]  John A Updegraff,et al.  Health Message Framing Effects on Attitudes, Intentions, and Behavior: A Meta-analytic Review , 2012, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[65]  Chris T. Bauch,et al.  Coevolution of risk perception, sexual behaviour, and HIV transmission in an agent-based model. , 2013, Journal of theoretical biology.