ABSTRACT
Traffic flow affects the transmission and distribution of pathogens. The large-scale traffic flow that emerges with the rapid development of global economic integration plays a significant role in the epidemic spread. In order to more accurately indicate the time characteristics of the traffic-driven epidemic spread, new parameters are added to represent the change of the infection rate parameter over time on the traffic-driven Susceptible-Infected-Recovered (SIR) epidemic spread model. Based on the collected epidemic data in Hebei Province, a linear regression method is performed to estimate the infection rate parameter and an improved traffic-driven SIR epidemic spread dynamics model is established. The impact of different link-closure rules, traffic flow and average degree on the epidemic spread is studied. The maximum instantaneous number of infected nodes and the maximum number of ever infected nodes are obtained through simulation. Compared to the simulation results of the links being closed between large-degree nodes, closing the links between small-degree nodes can effectively inhibit the epidemic spread. In addition, reducing traffic flow and increasing the average degree of the network can also slow the epidemic outbreak. The study provides the practical scientific basis for epidemic prevention departments to conduct traffic control during epidemic outbreaks.
ABSTRACT
Objective: To analyze the epidemiological characteristics of close contacts of COVID-19 cases and infection-related risk factors in Beijing and provide evidences for COVID-19 prevention and control. Methods: A total of 20 681 close contacts of COVID-19 cases, who had exposures during January 6, 2020 to February 15, 2021, were traced in Beijing. The information about their demographic characteristics, exposure history, and quarantine outcomes were collected and analyzed with descriptive statistics. The logistic regression analysis was used to identify the risk factors for COVID-19. Results: The infection rate SARS-CoV-2 in close contacts was 2.16% (447/20 681). The age M(P25, P75) was 35 (27, 49) years. The majority of the close contacts were aged 20-59 years, accounting for 81.77% (16 912/20 681). Centralized isolation was the major type of medical observation, accounting for 82.15% (16 989/20 681). Among the exposure types, working and studying in the same room (16.06%, 3 322/20 681), sharing same transport vehicle (12.88%, 2 664/20 681), performing diagnosis and treatment nursing (7.80%,1 612/20 681), and living together (7.23%,1 495/20 681), accounting for 43.96% (9 093/20 681). The index cases included staff (19.34%, 3 999/20 681), the unemployed (17.34%, 3 586/20 681), people engaged in business service (13.85%, 2 864/20 681), people engaged in food service (10.77%, 2 228/20 681), their close contacts accounted for 61.30% (12 677/20 681). Multivariate logistic regression analysis showed that compared with other types of exposure, the risk factors for infection were having meal together (OR=3.96, 95%CI: 2.30-6.83) and living together (OR=6.41, 95%CI:4.48-9.17); Compared with the other occupations, the index case being engaged in food service (OR=3.06, 95%CI:1.29-7.25) and teacher (OR=4.94, 95%CI:1.43-17.08) were risk factors for the infection. Conclusions: The main environmental exposure types of SARS-CoV-2 infection in close contacts were having meal together and living together. Contact with the index case being engaged in food service and teacher increased the risk for COVID-19. Comprehensive prevention and control measures such as centralized isolation and vaccination should be continued.