Analysis of prevention measures in Shenzhen based on SIQR model during the novel coronavirus pneumonia

Abstract

This paper proposes a joint gain and input design method for observer-based asymptotic active fault diagnosis, which is based on a newly-defined notion named the excluding degree of the origin from a zonotope. Using the excluding degree, a quantitative specification is obtained to characterize the performance of set-based robust fault diagnosis. Furthermore, a single gain design method and a joint gain and input design method are proposed, respectively. This is the first work to achieve a joint observer gain and input design for set-based active fault diagnosis. Compared with the existing methods that design gains and input separately, the proposed joint gain and input design method has advantages to exploit the fault diagnosis potential of observer-based schemes. Finally, several examples are used to illustrate the effectiveness of the proposed methods.

Government dissemination of epidemic information as a policy instrument during COVID-19 pandemic: Evidence from Chinese cities

“…ID can also be a part of the containment measures during public health crises (Fu et al, 2020). In addition to aggregate statistics such as the total number of infected cases and the death toll, information about individual cases of infection can be released to the public with varying degrees of details (Fu et al, 2020;Jung et al, 2020;Xu et al, 2020). In the case of COVID-19, one study shows that the disclosure of details about confirmed cases such as their travel history, activities, and visible symptoms may alert and prompt people with close contact to undergo testing and self-quarantine (Smith et al, 2020).…”