The effect of information-driven resource allocation on the propagation of epidemic with incubation period

Xuzhen Zhu; Yuxin Liu; Xiaochen Wang; Yuexia Zhang; Shengzhi Liu; Jinming Ma

doi:10.1007/s11071-022-07709-8

The effect of information-driven resource allocation on the propagation of epidemic with incubation period

Nonlinear Dyn. 2022;110(3):2913-2929. doi: 10.1007/s11071-022-07709-8. Epub 2022 Aug 2.

Authors

Xuzhen Zhu¹, Yuxin Liu¹, Xiaochen Wang², Yuexia Zhang³, Shengzhi Liu⁴, Jinming Ma⁵

Affiliations

¹ State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876 China.
² National Engineering Laboratory for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing, 100876 China.
³ School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing, 100101 China.
⁴ School of Digital Media and Design Art, Beijing University of Posts and Telecommunications, Beijing, 100876 China.
⁵ School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876 China.

Abstract

In the pandemic of COVID-19, there are exposed individuals who are infected but lack distinct clinical symptoms. In addition, the diffusion of related information drives aware individuals to spontaneously seek resources for protection. The special spreading characteristic and coevolution of different processes may induce unexpected spreading phenomena. Thus we construct a three-layered network framework to explore how information-driven resource allocation affects SEIS (susceptible-exposed-infected-susceptible) epidemic spreading. The analyses utilizing microscopic Markov chain approach reveal that the epidemic threshold depends on the topology structure of epidemic network and the processes of information diffusion and resource allocation. Conducting extensive Monte Carlo simulations, we find some crucial phenomena in the coevolution of information diffusion, resource allocation and epidemic spreading. Firstly, when E-state (exposed state, without symptoms) individuals are infectious, long incubation period results in more E-state individuals than I-state (infected state, with obvious symptoms) individuals. Besides, when E-state individuals have strong or weak infectious capacity, increasing incubation period has an opposite effect on epidemic propagation. Secondly, the short incubation period induces the first-order phase transition. But enhancing the efficacy of resources would convert the phase transition to a second-order type. Finally, comparing the coevolution in networks with different topologies, we find setting the epidemic layer as scale-free network can inhibit the spreading of the epidemic.

Keywords: Epidemic spreading; Exposed state; Information-driven resource allocation; Microscopic Markov chain; Multiplex network.

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