The effect of delayed awareness and fatigue on the efficacy of self-isolation in epidemic control

Abstract

The isolation of infectious individuals is a key measure of public health for the control of communicable diseases. However, involving a strong perturbation of daily life, it often causes psychosocial distress, and severe financial and social costs. These may act as mechanisms limiting the adoption of the measure in the first place or the adherence throughout its full duration. In addition, difficulty of recognizing mild symptoms or lack of symptoms may impact awareness of the infection and further limit adoption. Here, we study an epidemic model on a network of contacts accounting for limited adherence and delayed awareness to self-isolation, along with fatigue causing overhasty termination. The model allows us to estimate the role of each ingredient and analyze the tradeoff between adherence and duration of self-isolation. We find that the epidemic threshold is very sensitive to an effective compliance that combines the effects of imperfect adherence, delayed awareness and fatigue. If adherence improves for shorter quarantine periods, there exists an optimal duration of isolation, shorter than the infectious period. However, heterogeneities in the connectivity pattern, coupled to a reduced compliance for highly active individuals, may almost completely offset the effectiveness of self-isolation measures on the control of the epidemic.

References in corpus (16)

• Epidemic spreading in scale-free networks
• Epidemic processes in complex networks
• Critical phenomena in complex networks
• Generation of uncorrelated random scale-free networks
• Thresholds for epidemic spreading in networks
• Discrete-time Markov chain approach to contact-based disease spreading in complex networks
• Localization and Spreading of Diseases in Complex Networks
• Nature of the epidemic threshold for the susceptible-infected-susceptible dynamics in networks
• Epidemic thresholds of the Susceptible-Infected-Susceptible model on networks: A comparison of numerical and theoretical results
• An epidemiological model with voluntary quarantine strategies governed by evolutionary game dynamics
• Optimized Gillespie algorithms for the simulation of Markovian epidemic processes on large and heterogeneous networks
• Active and inactive quarantine in epidemic spreading on adaptive activity-driven networks
• Impact of the distribution of recovery rates on disease spreading in complex networks
• Cumulative Merging Percolation and the epidemic transition of the Susceptible-Infected-Susceptible model in networks
• Covid19: unless one gets everyone to act, policies may be ineffective or even backfire
• Epidemics on networks with heterogeneous population and stochastic infection rates