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Exploring the Effect of Misinformation on Infectious Disease Transmission

Authors: Mumtaz Nabeela; Green Caroline; Duggan James;

Exploring the Effect of Misinformation on Infectious Disease Transmission

Abstract

Vaccines are one of the safest medical interventions in history and can protect against infectious diseases and ensure important health benefits. Despite these advantages, health professionals and policymakers face significant challenges in terms of vaccine rollout, as vaccine hesitancy is a global challenge, and varies greatly with context, i.e., place, time, and vaccines. The internet has rapidly become a widely used information source for health-related issues, and a medium where misinformation in relation to vaccines on social media can spread rapidly and influence many. This research models the impact of vaccine confidence on the transmission of infectious diseases. This involves two interacting contagion models, one for the disease itself, and the other for the public’s views on vaccination. Sensitivity analysis and loop impact analysis are used to explore the effects of misinformation and vaccine confidence on the spread of infectious diseases. The analysis indicates that high vaccine confidence has a reinforcing effect on vaccination levels and helps to reduce the spread of an infectious disease. The results show that higher vaccine confidence can mitigate against the impact of misinformation, and by doing so can contribute to the enhanced control of an infectious disease outbreak.

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Keywords

vaccine confidence; misinformation; infectious disease transmission; sensitivity analysis; loop impact analysis, vaccine confidence, infectious disease transmission, Systems engineering, TA168, sensitivity analysis, loop impact analysis, T1-995, misinformation, Technology (General)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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15
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