Coupled effects of epidemic information and risk awareness on contagion
Wen-Juan Xu, Chen-Yang Zhong, Hui-Fen Ye, Rong-Da Chen, Tian Qiu, Fei, Ren, Li-Xin Zhong

TL;DR
This paper investigates how delayed epidemic information and individual risk awareness influence contagion dynamics across populations with different movement patterns, revealing that timely info and high sensitivity can significantly curb epidemic spread.
Contribution
It introduces a coupled SIS model incorporating delayed information and self-restricted travel, providing new insights into epidemic control strategies under various movement behaviors.
Findings
Delayed information has no effect in local movement populations.
High sensitivity to epidemic info suppresses spread.
Timely info and sensitivity are crucial in mixed movement populations.
Abstract
By incorporating delayed epidemic information and self-restricted travel behavior into the SIS model, we have investigated the coupled effects of timely and accurate epidemic information and people's sensitivity to the epidemic information on contagion. In the population with only local random movement, whether the epidemic information is delayed or not has no effect on the spread of the epidemic. People's high sensitivity to the epidemic information leads to their risk aversion behavior and the spread of the epidemic is suppressed. In the population with only global person-to-person movement, timely and accurate epidemic information helps an individual cut off the connections with the infected in time and the epidemic is brought under control in no time. A delay in the epidemic information leads to an individual's misjudgment of who has been infected and who has not, which in turn…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Network Analysis Techniques · COVID-19 epidemiological studies · Opinion Dynamics and Social Influence
