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

Chuanliang Han; Yimeng Liu; Jiting Tang; Yuyao Zhu; Carlo Jaeger; Saini Yang

doi:10.1007/s13753-020-00294-7

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Article Navigation > International Journal of Disaster Risk Science > 2020 > 11(4): 497-507

Chuanliang Han, Yimeng Liu, Jiting Tang, Yuyao Zhu, Carlo Jaeger, Saini Yang. 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[J]. International Journal of Disaster Risk Science, 2020, 11(4): 497-507. doi: 10.1007/s13753-020-00294-7

Citation:

Chuanliang Han, Yimeng Liu, Jiting Tang, Yuyao Zhu, Carlo Jaeger, Saini Yang. 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[J]. International Journal of Disaster Risk Science, 2020, 11(4): 497-507. doi: 10.1007/s13753-020-00294-7

Citation:

Chuanliang Han, Yimeng Liu, Jiting Tang, Yuyao Zhu, Carlo Jaeger, Saini Yang. 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[J]. International Journal of Disaster Risk Science, 2020, 11(4): 497-507. doi: 10.1007/s13753-020-00294-7

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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

doi: 10.1007/s13753-020-00294-7

Chuanliang Han¹,
Yimeng Liu^2,3,4,
Jiting Tang^2,3,4,
Yuyao Zhu^2,3,4,
Carlo Jaeger^{4,5
,
,},
Saini Yang^{2,3,4
,
,}

1 State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China;
2 Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China;
3 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;
4 Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;
5 Global Climate Forum, 10178 Berlin, Germany

Funds:

This study was sponsored by the National Key Research and Development Program of China (2018YFC1508903), and the National Natural Science Foundation of China (41621061), and supported by the International Center for Collaborative Research on Disaster Risk Reduction (ICCR-DRR).

Available Online: 2021-04-26

Abstract

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 (R²≥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 (R²≥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.
- China,
- COVID-2019,
- Epidemiological properties,
- Logistic model,
- Pandemic

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References(29)

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