ABSTRACT
In this paper, we investigate the close contact detection for COVID-19 patients based on the heterogeneous mobile edge computing (MEC) framework. Collecting the spatial-temporal data of a large number of mobile users, the base stations equipped with MEC servers organize these data via the R-tree structure. The cloud center (CC) aggregates the spatial-temporal data from all MEC servers. Considering the mobility of users as well as various positions of MEC servers, the CC then partitions and assigns the close contact detection tasks to different servers for faster processing. Aiming to minimize the system latency, we propose a Deep Deterministic Policy Gradient-based task and resource allocation scheme, where the computing loads are balanced among different servers. Simulation results show that a minimum system latency is reached while maintaining the load balance among all servers. Up to 37% detection accuracy enhancement is achieved compared with an existing task allocation scheme without load balance.
ABSTRACT
Objective: To analyze the epidemiological characteristics of novel coronavirus positive cases including confirmed cases with clinical symptoms and asymptomatic infected cases in Guangzhou. Methods: Epidemiological data were collected on the nucleic acid positive cases of COVID-19 in Guangzhou from January to September 2020. The epidemiological characteristics, the distribution of time intervals between the confirmed/isolation date and the date of the first positive detection were analyzed, at last the influencing factors for the confirmed cases and asymptomatic infected persons were discussed. Results: From January 7 to September 4 in 2020, a total of 1 097 nucleic acid positive cases were identified, including 658 confirmed cases (59.98%) and 439 asymptomatic infected cases (40.02%). Among the 658 confirmed cases, the median age was 42 years old, the cases indicated two significant peaks. one of the peaks was related to the imported and associated cases from Hubei province, and the other peak was connected with individuals from overseas. In terms of 439 asymptomatic infected cases, the median age was 32 years old. There were two stages in these cases. The first stage followed the second peak of confirmed cases, and the second stage overlapped with the confirmed cases in Guangzhou when the epidemic was in a period of normal prevention and control, mainly related to imported cases from abroad. The asymptomatic infected persons accounted for 57.32% in all the imported infected cases. In both of asymptomatic and symptomatic cases, the positive rate of pharyngeal swabs was higher than that of nasopharyngeal swabs and anal swabs. There were statistically significant differences in age, source of infection and gender composition between confirmed cases and asymptomatic infected persons (P<0.05). Older age groups were more likely to have clinical symptoms, with ≥40 years being the risk factor for confirmed cases (OR=2.334, P=0.001), and 20-39 years less likely to have clinical symptoms (OR=0.620, P=0.047), compared with the 0-19 years old group. Compared with those infected in China, those infected abroad were less likely to develop clinical symptoms and became confirmed cases (OR=0.723, P=0.013). Women were more likely to have clinical symptoms than men (OR=1.574, P=0.001). Conclusions: At present, asymptomatic infected persons and confirmed patients with clinical symptoms co-existed, and the number of asymptomatic infected patients was higher than that of confirmed cases in Guangzhou. High age, domestic infection and female may be risk factors for confirmed cases. It was of great value to further explore these underlying mechanisms for the prevention and treatment of the COVID-19.