Exploring the Effect of Misinformation on Infectious Disease Transmission

"In the past, epidemiological models failed to account for the important aspect of behavioural responses to diseases in human populations..."

Despite their public health successes, vaccines have always been challenged by individuals and groups who question, and sometimes refuse, them for a variety of reasons, including religious, scientific, and political ones. Vaccine hesitancy is also closely connected with increasing internet use and the widespread adoption of new information and communication technologies (ICTs). The internet has rapidly become a widely used information source for health-related issues, yet vaccine-related misinformation can spread rapidly on social media. This paper starts from the premise that the diffusion of ideas or information spreading in this way is analogous to the transmission of an infectious disease. In that vein, the research reported here models the impact of vaccine confidence on the transmission of infectious diseases.

The paper opens with a literature review of information/fear/rumour spread, infectious disease, and vaccination models. To the researchers' knowledge, existing research has not: (i) combined a model of vaccine misinformation spread with an infectious disease transmission model and (ii) investigated sensitivity and loop impact analysis on the model's feedback structure and behaviour. Thus, this paper combines the two diffusion dynamics: the spread of misinformation and the spread of disease, each of which can have different reproduction numbers. This research develops a dynamic model by combing the two diffusion dynamics and analysing its structure, behaviour, and feedback loops.

In system dynamics, stocks, flows, and feedback loops are the basic structural elements within the system boundary, and behaviour is generated through the interaction of reinforcing and balancing feedback loops. These model elements are responsible for generating the behaviour of the system. Section 3 of the paper explains the sensitivity and loop impact analysis method, the misinformation/disease model's formulation, differential equations, and the feedback loop's structure. They argue that the loops that matter (LTM) method is well suited to show a strong relationship between a model structure and behaviour. The process involves two interacting contagion models: one for the disease itself, and the other for the public's views on vaccination.

Section 4 describes the use of sensitivity analysis and loop impact analysis 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. That is, 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.

The final section presents insights from the experiments and the conclusions. "From a policy perspective, the model highlights the importance of understanding the inter-relationships between the contagion dynamics of misinformation and the spread of diseases. In the context of a fast-moving pathogen, the model's results indicate the importance of the speed of response and building population confidence in vaccines, a message that...highlights the urgent need for effective interventions to increase trust and inform the public...There may also be opportunities for enhancing education opportunities for schools, for example, by introducing simple SIR [Susceptible, Infected, and Recovered] models as part of the mathematics curriculum, and also by using these models to demonstrate the positive impact that human behaviour (mask wearing, vaccination uptake, and resilience to misinformation) can have on overall public health."

The researchers conclude by indicating the scope for future work with the model, such as: (i) applying the model to a country-specific case study, involving measuring and assessing key parameters, such as the results of vaccine confidence surveys; (ii) extending the model to different age cohorts to explore heterogeneities between a range of groups in terms of vaccine confidence and disease spread; and (iii) extending the loop dominance analysis to include techniques such as the eigenvalue elasticity analysis (EEA) and the partway participation method (PPM).