  The emergence of infectious disease COVID-19 has challenged and changed the
world in an unprecedented manner. The integration of wireless networks with
edge computing (namely wireless edge networks) brings opportunities to address
this crisis. In this paper, we aim to investigate the prediction of the
infectious probability and propose precautionary measures against COVID-19 with
the assistance of wireless edge networks. Due to the availability of the
recorded detention time and the density of individuals within a wireless edge
network, we propose a stochastic geometry-based method to analyze the
infectious probability of individuals. The proposed method can well keep the
privacy of individuals in the system since it does not require to know the
location or trajectory of each individual. Moreover, we also consider three
types of mobility models and the static model of individuals. Numerical results
show that analytical results well match with simulation results, thereby
validating the accuracy of the proposed model. Moreover, numerical results also
offer many insightful implications. Thereafter, we also offer a number of
countermeasures against the spread of COVID-19 based on wireless edge networks.
This study lays the foundation toward predicting the infectious risk in
realistic environment and points out directions in mitigating the spread of
infectious diseases with the aid of wireless edge networks.

Social and Information Networks;Cryptography and Security;Computers and Society;Networking and Internet Architecture