The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Epidemic analysis of COVID-19 in China by dynamical modeling
Open accessAbstract
The outbreak of novel coronavirus-caused pneumonia (COVID-19) in Wuhan has attracted worldwide attention. Here, we propose a generalized SEIR model to analyze this epidemic. Based on the public data of National Health Commission of China from Jan. 20th to Feb. 9th, 2020, we reliably estimate key epidemic parameters and make predictions on the inflection point and possible ending time for 5 different regions. According to optimistic estimation, the epidemics in Beijing and Shanghai will end soon within two weeks, while for most part of China, including the majority of cities in Hubei province, the success of anti-epidemic will be no later than the middle of March. The situation in Wuhan is still very severe, at least based on public data until Feb. 15th. We expect it will end up at the beginning of April. Moreover, by inverse inference, we find the outbreak of COVID-19 in Mainland, Hubei province and Wuhan all can be dated back to the end of December 2019, and the doubling time is around two days at the early stage.
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1 retracted
Epidemiological and clinical features of the 2019 novel coronavirus outbreak in China
Yang, Y., Lu, Q.-B., Liu, M.-J., Wang, Y.-X., Zhang, A.-R., Jalali, N., Dean, N. E., Longini, I., Halloran, M. E., Xu, B., et al. (2020) · medRxiv · doi:10.1101/2020.02.10.20021675 · Cited by 516
RetractedClinical features of patients infected with 2019 novel coronavirus in Wuhan, China
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Early dynamics of transmission and control of COVID-19: a mathematical modelling study
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Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts
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High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2
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Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study
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Pham, Q.-V., Nguyen, D. C., Huynh-The, T., Hwang, W.-J., & Pathirana, P. N. (2020) · IEEE Access · doi:10.1109/access.2020.3009328 · Cited by 413
López, L., & Rodó, X. (2021) · Results in Physics · doi:10.1016/j.rinp.2020.103746 · Cited by 328
A review on COVID-19 forecasting models
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Metadata
- Authors
- Peng, L., Yang, W., Zhang, D., Zhuge, C., & Hong, L.
- Venue
- medRxiv
- Publisher
- openRxiv
- Published
- 2020-02-18
- OpenAlex
- W3007602081
- Cited by
- 622
- References
- 24
- Type
- posted content
- Language
- en
- Open access
- green
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