Effects of Chinese strategies for controlling the diffusion and deterioration of novel coronavirus–infected pneumonia in China

Abstract

Background: In December 2019, an outbreak of new type of coronavirus named COVID-19 occurred in Wuhan, Hubei Province, China. In a very short time, this virus spread rapidly over China, greatly threatening public health and economic development.The Chinese government acted quickly and implemented a series of strategies to prevent diffusion of this disease. We therefore sought to evaluate the effects of these Chinese strategies for controlling the spread of COVID-19. Methods: From the data of cumulative confirmed cases from provincial HealthCommission websites of China, we performed model fitting and calculated the growth speed of cumulative confirmed patients. We further analyzed the time when this growth speed, the rate of the number of new cases, reached its maximum (Speedmax).Comparing different times to Speedmax of different areas in China, we calculated the dates at which the growth speed began to decline in different areas. Also, The number of plateaus were analyzed.Results: The quartic model showed the best fit. For almost all areas in mainland China, the speed of infections reached Speedmax and began to decline within 14 days; exceptions were Hebei, Heilongjiang, Hainan, Guizhou, and Hubei. The number of plateaus was significantly correlated with the emigration index. However, the distance from other areas to Hubei and the number of plateaus had little influence on when a province or area arrived at Speedmax. Once strict intervention strategies were implemented, diffusion and deterioration of COVID-19 were inhibited quickly and effectively over China. Conclusion:Our study suggests that Chinese strategies are highly effective on controlling the diffusion and deterioration of the novel coronavirus-infected pneumonia.These strategies supply experience and guidelines for other countries to control the COVID-19 epidemic.

Dynamics of COVID-19 pandemic at constant and time-dependent contact rates

“…Data describing epidemic dynamics in China suggest that very strict quarantine and other preventive efforts imposed in the most affected China province, Hubei, [9,20] allowed to reduce R 0 about 25-fold (which is on par with the estimated reduction of transmissibility of 97-100% [18]). This allowed the Chinese to reduce coefficient θ from about 1.4 in the exponential growth phase to about θ = 0.89 in the regression phase.…”

Dynamics of COVID-19 pandemic at constant and time-dependent contact rates

“…The exponential distribution or even Erlang(k = 2, λ = k/τ) distribution of the incubation period ( figure 4C) is not supported by data [1,2], which indicates much narrower distribution isolated from zero, so we think that our estimate of the basic reproduction number is closer to a real value. Data describing epidemic dynamics in China suggest that very strict quarantine and other preventive efforts imposed in the most affected China province, Hubei, [7,16] allowed to reduce R about 25-fold (which is on par with the estimated reduction of transmissibility of 97-100% [17]). This allowed the Chinese to reduce coefficient β from about 1.4 in the exponential growth phase to about β = 0.89 in the regression phase.…”

“…Data describing epidemic dynamics in China suggest that very strict quarantine and other preventive efforts imposed in the most affected China province, Hubei, [7, 16] allowed to reduce R about 25-fold (which is on par with the estimated reduction of transmissibility of 97– 100% [17]). This allowed the Chinese to reduce coefficient β from about 1.4 in the exponential growth phase to about β ′ = 0.89 in the regression phase.…”

Transformed time series analysis of first-wave COVID-19: universal similarities found in the Group of Twenty (G20) Countries

“…37 Interestingly, China’s short-term CCC was fitted using a fourth-order polynomial of time, defined as the number of days from January 21 to February 23, 2020. 38 Because a polynomial function usually does not have an asymptotic limit, extending this model for the long-term trend is questionable. Although these studies modeled CCC trends using various mathematical functional approximations at different time and space scales, specific correlations of nations’ pandemic trends were not found.…”