Lessons from the Mainland of China’s Epidemic Experience in the First Phase about the Growth Rules of Infected and Recovered Cases of COVID-19 Worldwide

Abstract

The first phase of the novel coronavirus disease (COVID-19) that emerged at the end of 2019 has been brought under control in the mainland of China in March, while it is still spreading globally. When the pandemic will end is a question of great concern. A logistic model that depicts the growth rules of infected and recovered cases in China's mainland may shed some light on this question. This model well explained the data by 13 April from 31 countries that have been experiencing serious COVID-2019 outbreaks (R 2 C 0.95). Based on this model, the semi-saturation period (SSP) of infected cases in those countries ranges from 3 March to 18 June. According to the linear relationship between the growth rules for infected and for recovered cases identified from the Chinese data, we predicted that the SSP of the recovered cases outside China ranges from 22 March to 8 July. More importantly, we found a strong positive correlation between the SSP of infected cases and the timing of a government's response. Finally, this model was also applied to four regions that went through other coronavirus or Ebola virus epidemics (R 2 C 0.95). There is a negative correlation between the death rate and the logistic growth rate. These findings provide strong evidence for the effectiveness of rapid epidemic control measures in various countries.

Mechanisms of recurrent outbreak of COVID-19: a model-based study

“…We proposed this two-fold framework for the first time, which will help us to have a more comprehensive understanding of the mechanism of recurrent outbreaks. We not only studied the epidemic situation on one wave [ 2 , 36 , 38 , 56 ], but also multiple waves. To test the robustness of this approach, we focused on the analysis of epidemics in dozens of regions and countries on many continents instead of only tested in specific countries or regions [ 27 – 29 , 57 , 58 ].…”

“…Each period wave of the epidemic can be described by three parameters: the maximum number of cases ( A i ), logistic growth rate ( k i ), and semi-saturation period ( t i ). The logistic growth rate is deemed to be related to the spread of the virus, and the semi-saturation period is deemed to be related to government response [ 36 ]. Using these model parameters, we can capture the underlying mechanisms possibly contributing to the increase of the cumulative infected cases during different waves.…”

“…The basic testing procedure has been described in detail in previous works [ 1 , 5 ]. The first wave of acquired dataset was analyzed in an initial study [ 36 ]. The data may involve systematic estimation errors since asymptomatic infections are not included in the data.…”

“…For this reason, we have developed a revised version of the previous possible solutions to this dilemma. A logistic function has been used in previous studies to depict the cumulative daily number of COVID-19 cases in the early phase of the epidemic outbreak [ 36 ]. Here, we used the logistic model of multiple waves sigmoid model to solve this problem.…”

“…Previous modeling studies have shown that the early phase of the epidemic outbreak inside or outside of China can be depicted by descriptive models (e.g., logistic models) [36], dynamical models such as SIR (Susceptible Infected Recovered Model), SEIR (Susceptible Exposed Infected Recovered Model) or SEIDR (Susceptible Exposed Infected Dead Recovered Model) [3,4,[37][38][39][40][41][42] or using deep learning techniques [43][44][45]. However, these previous works only depicted one epidemic process and none of these models can be reliably used to predict whether the outbreak will be recurrent in the future.…”