Misinformation can prevent the suppression of epidemics

Abstract

The effectiveness of non-pharmaceutical interventions, such as mask-wearing and social distancing, as control measures for pandemic disease relies upon a conscientious and well-informed public who are aware of and prepared to follow advice. Unfortunately, public health messages can be undermined by competing misinformation and conspiracy theories, spread virally through communities that are already distrustful of expert opinion. In this article, we propose and analyse a simple model of the interaction between disease spread and awareness dynamics in a heterogeneous population composed of both trusting individuals who seek better quality information and will take precautionary measures, and distrusting individuals who reject better quality information and have overall riskier behaviour. We show that, as the density of the distrusting population increases, the model passes through a phase transition to a state in which major outbreaks cannot be suppressed. Our work highlights the urgent need for effective interventions to increase trust and inform the public.

Keywords: awareness spread; epidemic modelling; heterogeneous population.

Figure 1.

Stronger interventions (increasing ρ) have a similar effect on disease dynamics as a more trusting population (decreasing d). Curves show time series of the total number of infected I(t) for varying (a) ρ (fixed d = 0.1) and (b) d (fixed ρ = 0.8). In both cases α_T = α_D = 5, β = 0.667, σ = 0.133 (R_e(0) = R₀ = 5), λ = 0.2 and ω = 0.333.

Figure 2.

Faster information spread (increasing α) heightens model sensitivity to the strength of interventions (ρ) and composition of the population (d). Curves show effects of varying ρ and d on the peak infection size and the final susceptible population. (a) Total infections peak for varying ρ and fixed d = 0.3. (b) Total infections peak for fixed ρ = 0.8 and varying d. (c) Final susceptible population for varying ρ and fixed d = 0.3. (d) Final susceptible population for fixed ρ = 0.8 and varying d. Remaining parameters: α_D = 1, λ = 0.2, ω = 0.333, β = 0.667 and σ = 0.133.

Figure 3.

Timing of the infectious peak (colours) reveals the boundary between epidemic suppression and large outbreaks. First row: R₀ = 3.5 (β = 0.467), (a) α_T = 0.5 and (b) α_T = 500. Second row: R₀ = 2 (β = 0.267), (c) α_T = 0.5 and (d) α_T = 500. The remaining parameters are: α_D = 1, λ = 0.2, ω = 0.333 and σ = 0.133.

Figure 4.

The agreement between the theoretical prediction (dashed lines) of the observed phase transition and the suppression/mitigation regions obtained through the numerical solutions of equations (2.3)–(2.10) for α_T = α_D = 10⁶, λ = 0.2, ω = 0.33, σ = 0.133, and (a) β = 0.333 (R₀ = 2.5) and (b) β = 0.467 (R₀ = 3.5). The white area (I) corresponds to the region where disease spread is suppressed. The area in blue (II) corresponds to the region where disease spread is only mitigated by behavioural feedback.