The nexus between disease surveillance, adaptive human behavior and epidemic containment

TL;DR

The results show that robust surveillance systems that quickly detect a disease outbreak can trigger an early response from the population, leading to large epidemic sizes, and confirm that behavioral adaptation can create a hysteresis-like effect on the final epidemic size.

Abstract

Epidemics exhibit interconnected processes that operate at multiple time and organizational scales, a hallmark of complex adaptive systems. Modern epidemiological modeling frameworks incorporate feedback between individual-level behavioral choices and centralized interventions. Nonetheless, the realistic operational course for disease detection, planning, and response is often overlooked. Disease detection is a dynamic challenge, shaped by the interplay between surveillance efforts and transmission characteristics. It serves as a tipping point that triggers emergency declarations, information dissemination, adaptive behavioral responses, and the deployment of public health interventions. Evaluating the impact of disease surveillance systems as triggers for adaptive behavior and public health interventions is key to designing effective control policies. We examine the multiple behavioral and epidemiological dynamics generated by the feedback between disease surveillance and the intertwined dynamics of information and disease propagation. Specifically, we study the intertwined dynamics between: $(i)$ disease surveillance triggering health emergency declarations, $(ii)$ risk information dissemination producing decentralized behavioral responses, and $(iii)$ centralized interventions. Our results show that robust surveillance systems that quickly detect a disease outbreak can trigger an early response from the population, leading to large epidemic sizes. The key result is that the response scenarios that minimize the final epidemic size are determined by the trade-off between the risk information dissemination and disease transmission, with the triggering effect of surveillance mediating this trade-off. Finally, our results confirm that behavioral adaptation can create a hysteresis-like effect on the final epidemic size.

Figures (12)

• Figure 1: Schematic of our detection and response framework Our framework captures the intertwined dynamics between disease dynamics spreading over a naive population, defining the detection time, and the disease dynamics produced by including behavior and interventions. The detection and response times depend on the surveillance effort and disease progression. This alters the contagion dynamics by triggering dueling dynamics between information dissemination and disease transmission, which ultimately shape the effects of behavior and interventions on the epidemiological outcomes.
• Figure 2: The impact of disease transmission characteristics on the behavior-disease dynamics and the final epidemic size.(A) Time series of infectious individuals showing the potential dynamics produced by the proposed framework. We assume an average infectious period $1/\gamma=10$ days, and $\mathcal{R}_0=\{0.75,1.25,3\}$, respectively. (B) Final epidemic sizes are attained by varying the disease infectiousness and period. We assume that the information dissemination exhibits a transmission likelihood of $\beta_i=1.5$, and that aware individuals show an average response period of $1/\gamma_i=10$ days, during which their susceptibility reduces by a factor $\epsilon=0.8$. We let the centralized response to quarantine a fraction $\phi=0.2$ of newly infected individuals and that surveillance effort triggers behavioral responses at a prevalence level of $P^*=0.025$.
• Figure 3: The impact of risk-information transmission characteristics on the final epidemic size.(A) Final size as a function of the risk-information transmission rate ($\beta_i$) and response withdrawal rate ($\gamma_i$). Our results show that, regardless of the behavioral response withdrawal rate, the final size exhibits a non-monotonic reduction as the risk-information transmission rate increases. (B) Time series of infected and recovered subpopulations for distinct risk-information transmission rates ($\beta_i = 0.5,1,2$), and response withdrawal rate $\gamma_i$ of $10$ days. (C) Final size as a function of the information transmission rate, for surveillance efforts leading disease detection at $P^*=0.01$, $P^*=0.05$, and $P^*=0.1$. Early disease detection scenarios show a sharp minimum as a function of information transmission rate. Delayed disease detection requires higher information transmission rates and shows broader minimums.
• Figure 4: The trade-off between centralized interventions and adaptive behavior, on the final epidemic size.(A and B) Final epidemic sizes as a function of the behavioral response stringency ($\epsilon$) and the surveillance efforts ($P^*$), for quarantine capacities of $\phi=0.2$ and $\phi=0.4$. Our results show non-monotonic changes on the final epidemic size as the surveillance effort decreases. (C) The local minimum emerges and is exacerbated by increments on the behavioral response stringency. (D and E) Final epidemic sizes as a function of quarantine capacity ($\phi$) and surveillance efforts ($P^*$), for behavioral response stringency of $\epsilon=0.3$ and $\epsilon=0.3$. Low stringency of behavioral responses ($\epsilon=0.3$) vanishes the non-monotonic changes on the final epidemic size. High stringency of behavioral responses ($\epsilon=0.8$) exhibit non-monotonic changes on the final epidemic size regardless of the quarantine capacity.
• Figure S1: Grid of informative parameters comparison. Shows the gradual change when different infection and recovery rates are set in the informative section of the model, providing an idea of the 4-dimensional motion presented through the model's sensitivity. The grid is composed by the sets of $\beta_i\in\{1.5,2.5,3.5\}$ and $\gamma_i\in\{1/8,1/15,1/22\}$; while the other parameters are considered fixed.