Simulating nationwide coupled disease and fear spread in an agent-based model

TL;DR

A dynamic agent-based model is presented that explicitly couples the spread of disease with the spread of fear surrounding the disease, implemented within the EpiCast simulation framework, and shows that the addition of asymptomatic, exposed, and pre-symptomatic disease states can impact both the rate at which an outbreak progresses and its overall trajectory in compartmental models.

Abstract

Human cognitive responses, behavioral responses, and disease dynamics co-evolve over the course of any disease outbreak, and can result in complex feedbacks. We present a dynamic agent-based model that explicitly couples the spread of disease with the spread of fear surrounding the disease, implemented within the EpiCast simulation framework. EpiCast models transmission across a realistic synthetic population, capturing individual-level interactions. In our model, fear propagates through both in-person contact and broadcast media, prompting individuals to adopt protective behaviors that reduce disease spread. In order to better understand these coupled dynamics, we create and compare a range of compartmental surrogate models to analyze the impact of including various disease states. Additionally, we compare a range of behavioral scenarios within EpiCast, varying the level and intensity of fear and behavioral change. Our results show that the addition of asymptomatic, exposed, and pre-symptomatic disease states can impact both the rate at which an outbreak progresses and its overall trajectory. Moreover, the combination of non-local fear spread via broadcasters and strong behavioral responses by fearful individuals generally leads to multiple epidemic waves, an outcome that occurs only within a narrow parameter range when fear spreads purely through local contact. Accounting for the coupled spread of fear and disease is critical for understanding disease dynamics and designing timely, targeted responses to emerging infectious threats.

Figures (8)

• Figure 1: ODE model comparison: We show outputs with attention to parameter choices which impact the emergence of multiple epidemic waves. On the top and bottom we show solutions corresponding to parameter choices which fail to produce, or produce, respectively, a second wave within 360 days. We show these results for models combining 1) a basic Susceptible Infectious Recovered model with Neutral and Fearful states ($SIR \times NF$, \ref{['fig:sir_rho_1']} and \ref{['fig:sir_rho_0']}, varying the relative likelihood an individual becomes fearful after covering from a symptomatic infection, $\rho_f$), 2) seperating symptomatic and asymptomatic infectious and recovered states ($SI_sI_aR_sR_a \times NF$,\ref{['fig:si_ai_sr_sr_a_sigma_f_25']} and introducing Exposed and Presymptomatic states (\ref{['fig:si_ai_sr_sr_a_sigma_f_35']}, varying the susceptibility of fearful individuals to infection, $\sigma_f$), and $SEPI_sI_aR_sR_a \times NF$ (\ref{['fig:sepi_ai_sr_sr_a_sigma_f_25']} and \ref{['fig:sepi_ai_sr_sr_a_sigma_f_35']}, varying $\sigma_f$). For ease of comparison, we combine all infected (including $E$), recovered, and fearful compartments in each model into a single line in each of the plots above.
• Figure 2: EpiCast Fear Spread Scenarios: New cases (\ref{['fig:cases']}), total fear levels (\ref{['fig:fear']}), total broadcasters spreading fear (\ref{['fig:bc-spread']}), and total broadcasters countering fear spread (\ref{['fig:bc-counter']}) for six different scenarios run in EpiCast. These scenarios can include agents withdrawing from their normal schedules due to being hospitalized (hosp), being fearful of the disease while having symptoms of it (sick), and fear of the disease without symptoms (fear), as well as non-local fear spread through broadcast media (bc), and fearful agents having a lower susceptibility to the disease due to taking protective actions such as masking (reduced_sus).
• Figure 3: Geographic Distribution of Outbreaks in Two Epicast Fear Spread Scenarios: Geographic distribution of new case counts for selected timestamps (left) and overall new case trends for selected states (right) for scenarios with pure-fear withdrawals without (upper) and with (lower) broadcaster-based fear spread.
• Figure 4: Epicast local fear spread sensitivity analysis: New cases by day (with peaks indicated by dashed vertical line) for (\ref{['fig:sa-l-grid']}) a coarse-grained parameter search and (\ref{['fig:sa-l-sup']}) a finer-grained search highlighting cases with multiple waves, and (\ref{['fig:sa-l-attack']}) total attack rate for various rates of purely fear-based withdrawals ($p_\text{fear}$) and levels of susceptibility scaling due to fear ($\sigma_f$) with only local fear spread. This parameter search compares different levels of susceptibility to infection for fearful agents ($\sigma_f$) and rates of withdrawal by fearful agents ($p_\text{fear}$).
• Figure 5: Epicast broadcaster fear spread sensitivity analysis: (\ref{['fig:sa-lb-grid']}) new cases by day (with peaks indicated by dashed vertical line), (\ref{['fig:sa-lb-waves']}) number of epidemic waves, and (\ref{['fig:sa-lb-attack']}) total attack rate for various rates of purely fear-based withdrawals ($p_\text{fear}$) and levels of susceptibility scaling due to fear ($\sigma_f$) with both local and broadcaster-based fear spread. This parameter search compares different levels of susceptibility to infection for fearful agents ($\sigma_f$) and rates of withdrawal by fearful agents ($p_\text{fear}$).