A probabilistic epidemiological model for infectious diseases: The case of COVID-19 at global-level

The synergy effect of multi-country policy actions announced in reaction to global risk: A network structure perspective

The policy actions of countries reflect adaptive responses of local components within the system to the dynamic global risk landscape. These responses can generate interactions and synergy effects on alleviating the evolution of global risks. Adopting a network perspective, the study proposes a theoretical framework that connects three structural characteristics of policy synergy, namely, synergy scale, alignment intensity, and timing synchronization. Focusing on the Covid-19 pandemic as a typical global risk context, the study finds that policy synergy with a larger scale, stronger alignment intensity, and more synchronized timing has a positive impact on mitigating global risks. The effect of alignment intensity is particularly pronounced when polycentric governance involves 20 countries facing severe risks, whereas the effect of timing synchronization is more significant when the multicenter group comprises more countries. Building upon the concept of an efficient scale of polycentric governance in various dimensions, this study develops a policy synergy index model. Through multiple empirical analyses, this study validates the causal relationship between policy synergy and the future evolution of global pandemic risk. Policymakers can leverage the dynamic changes in the policy synergy to predict future risk situations and implement well-rounded and appropriate policy actions, thereby enhancing the efficacy of the synergy effect of multi-country policy actions for risk governance.

Model of strategy control for delayed panic spread in emergencies

In emergencies similar to virus spreading in an epidemic model, panic can spread in groups, which brings serious bad effects to society. To explore the transmission mechanism and decision-making behavior of panic, a government strategy was proposed in this paper to control the spread of panic. First, based on the SEIR epidemiological model, considering the delay effect between susceptible and exposed individuals and taking the infection rate of panic as a time-varying variable, a SEIR delayed panic spread model was established and the basic regeneration number of the proposed model was calculated. Second, the control strategy was expressed as a state delayed feedback and solved using the exact linearization method of nonlinear control system; the control law for the system was determined, and its stability was proven. The aim was to eradicate panic from the group so that the recovered group tracks the whole group asymptotically. Finally, we simulated the proposed strategy of controlling the spread of panic to illustrate our theoretical results.