ABSTRACT
The novel corona respiratory viruses usually leave the host via droplets and float in the air or settle down. Medically, the decay characteristics of aerosolized viruses have been simulated and measured by atomizing suspensions into air, but there has been no systematic study on the effect of ambient temperature and humidity. In this study, we collected experimental data from existing medical studies, then introduced and calculated the initial decay constant (k(1)) and the secondary decay constant (k(2)) based on these data as a unified standard to quantify the decay process of the aerosolized viruses. Additionally, the influence of various factors on the decay constant of respiratory viruses in the air was statistically analyzed. The results showed that the phase decay of aerosolized viruses was very evident, and k(1) (0.1 similar to 50) was usually one order of magnitude higher than k(2) (0.005 similar to 3.2). Furthermore, the relationship between the decay constant and the survival rate of viruses, representing the risk of virus transmission, and the decay time constants, reflecting the effective period of prevention and control, was discussed. According to the decay law, the main contradiction between virus transmission and epidemic prevention and control at different time nodes was pointed out, and scientific measures to reduce the transmission risk in the built environment were recommended.
ABSTRACT
Objective: Based on the geographic information systems, we exploreed the spatiotemporal clustering and the development and evolution of COVID-19 epidemic at prefectural level in China from the time when the epidemic was discovered to the time when the lockdown ended in Wuhan. Methods: The information and data of the confirmed COVID-19 cases from December 8, 2019 to April 8, 2020 were collected from 367 prefectures in China for a spatial autocorrelation analysis with software GeoDa, and software ArcGIS was used to visualize the results. Software SatScan was used for spatiotemporal scanning analysis to visualize the hot-spot areas of the epidemic. Results: The incidence of new cases of COVID-19 had obvious global autocorrelation and the partial autocorrelation results showed that incidence of COVID-19 had different spatial distribution at different times from December 8, 2019 to March 4, 2020. There was no significant difference in global autocorrelation coefficient from March 5, 2020 to April 8, 2020. The statistical analysis of spatiotemporal scanning identified two kinds of spatiotemporal clustering areas, the first class clustering areas included 10 prefectures, mainly distributed in Hubei, from January 13 to February 25, 2020. The secondary class clustering areas included 142 prefectures, mainly distributed in provinces in the north and east of Hubei, from January 23 to February 1, 2020. Conclusions: There was a clear spatiotemporal correlation in the distribution of the outbreaks in the early phase of COVID-19 epidemic (December 8, 2019-March 4, 2020) in China. With the decrease of the case and effective prevention and control measures, the epidemics had no longer significant correlations among areas from March 5 to April 8. The study results showed relationship with time points of start and adjustment of emergency response at different degree in provinces. Furthermore, improving the early detection of new outbreaks and taking timely and effective prevention and control measures played an important role in blocking the transmission.