Controlling epidemic diseases based only on social distancing level: General case

Introduction to Group-Structured-Epidemic Model

The spread of an infectious disease in a population is a random process when considering a small group of individuals. However, to a great group of individuals, the use of deterministic behavior is better. Based on these facts, in the literature, there were proposed stochastic and deterministic epidemic models. This work proposes a mixed compartmental epidemic model that allows stratifying the population into groups, considers demographic and environmental variability, presents an approximation to stochastic effects, and contemplates the network effects. The proposed model has a compact form to assist in the synthesis of the control law and parameters estimation strategies. Its objective is to overcome the difficulties encountered when used purely deterministic or purely stochastic models. In the end, to detail and verify the functioning of the proposed model, we present a set of flowcharts and simulations.

A novel social distancing analysis in urban public space: A new online spatio-temporal trajectory approach

Social distancing in public spaces plays a crucial role in controlling or slowing down the spread of coronavirus during the COVID-19 pandemic. Visual Social Distancing (VSD) offers an opportunity for real-time measuring and analysing the physical distance between pedestrians using surveillance videos in public spaces. It potentially provides new evidence for implementing effective prevention measures of the pandemic. The existing VSD methods developed in the literature are primarily based on frame-by-frame pedestrian detection, addressing the VSD problem from a static and local perspective. In this paper, we propose a new online multi-pedestrian tracking approach for spatio-temporal trajectory and its application to multi-scale social distancing measuring and analysis. Firstly, an online multi-pedestrian tracking method is proposed to obtain the trajectories of pedestrians in public spaces, based on hierarchical data association. Then, a new VSD method based on spatio-temporal trajectories is proposed. The proposed method not only considers the Euclidean distance between tracking objects frame-by-frame but also takes into account the discrete Fréchet distance between trajectories, hence forms a comprehensive solution from both static and dynamic, local and holistic perspectives. We evaluated the performance of the proposed tracking method using the public dataset MOT16 benchmark. We also collected our own pedestrian dataset "SCU-VSD" and designed a multi-scale VSD analysis scheme for benchmarking the performance of the social distancing monitoring in the crowd. Experiments have demonstrated that the proposed method achieved outstanding performance on the analysis of social distancing.