Impact of asymptomatic infection on coupled disease-behavior dynamics in complex networks

TL;DR

This paper models how asymptomatic infections influence disease spread and behavioral responses in complex networks, revealing how undetectable cases affect epidemic thresholds and control strategies.

Contribution

It introduces a novel coupled disease-behavior model distinguishing symptomatic and asymptomatic states within complex networks, providing analytical epidemic thresholds considering asymptomatic transmission.

Findings

Asymptomatic infections lower epidemic thresholds.

Behavioral responses are triggered only by symptomatic contacts.

The SIR model shows weaker suppression effects than SIS.

Abstract

Studies on how to model the interplay between diseases and behavioral responses (so-called coupled disease-behavior interaction) have attracted increasing attention. Owing to the lack of obvious clinical evidence of diseases, or the incomplete information related to the disease, the risks of infection cannot be perceived and may lead to inappropriate behavioral responses. Therefore, how to quantitatively analyze the impacts of asymptomatic infection on the interplay between diseases and behavioral responses is of particular importance. In this Letter, under the complex network framework, we study the coupled disease-behavior interaction model by dividing infectious individuals into two states: U-state (without evident clinical symptoms, labelled as U) and I-state (with evident clinical symptoms, labelled as I). A susceptible individual can be infected by U- or I-nodes, however, since the U-nodes cannot be easily observed, susceptible individuals take behavioral responses \emph{only} when they contact I-nodes. The mechanism is considered in the improved Susceptible-Infected-Susceptible (SIS) model and the improved Susceptible-Infected-Recovered (SIR) model, respectively. Then, one of the most concerned problems in spreading dynamics: the epidemic thresholds for the two models are given by two methods. The analytic results \emph{quantitatively} describe the influence of different factors, such as asymptomatic infection, the awareness rate, the network structure, and so forth, on the epidemic thresholds. Moreover, because of the irreversible process of the SIR model, the suppression effect of the improved SIR model is weaker than the improved SIS model.