A widely held belief in network epidemiology is that information diffusion makes individuals aware of the epidemic and thus drives them to seek protections from nonpharmaceutical or pharmaceutical resources, which can help suppress its spread. However, as the COVID-19 pandemic has revealed, excessive information diffusion can trigger irrational acquisition and hoarding behaviors, which can lead to shortages of resources even for those in urgent need, consequently, worsening disease spreading. To develop a quantitative understanding of the effect of information diffusion on epidemic spreading, subject to allocations of limited resources, has become an urgently important problem with broad implications. We construct a multiplex network framework to characterize the complex interplay among resource allocation, information diffusion, and epidemic spreading, and develop a microscopic Markov chain theory to analyze their coevolution dynamics. There are two main findings. Firstly, if infected individuals have a large recovery probability, information diffusion plays the expected "normal"role of suppressing the epidemic. However, if the recovery probability is low, information diffusion can anomalously worsen the spread, regardless of the available resources insofar as they are limited. Secondly, different types of resources can lead to distinct phase transitions underlying the epidemic outbreak when the recovery probability is low: with limited cure-focused resources, the phase transition is of the second order, but if resources are of the protection type, the transition becomes first order, and a hysteresis loop emerges. The generality of the findings is established through simulations of synthetic and empirical three-layer networks with results in agreement with the theoretical predictions.
ASJC Scopus subject areas
- Physics and Astronomy(all)