Dataset Information


Study on SARS-CoV-2 transmission and the effects of control measures in China.

ABSTRACT: Objective: To reconstruct the transmission trajectory of SARS-CoV-2 and analyze the effects of control measures in China.

Methods: Python 3.7.1 was used to write a SEIR class to model the epidemic procedure and proportional estimation method to estimate the initial true infected number. The epidemic area in China was divided into three parts, Wuhan city, Hubei province (except Wuhan) and China (except Hubei) based on the different transmission pattern. A testing capacity limitation factor for medical resources was imposed to model the number of infected but not quarantined individuals. Baidu migration data were used to assess the number of infected individuals who migrated from Wuhan to other areas.

Results: Basic reproduction number, R0, was 3.6 before the city was lockdown on Jan 23, 2020. The actual infected number the model predicted was 4508 in Wuhan before Jan 23, 2020. By January 22 2020, it was estimated that 1764 infected cases migrated from Wuhan to other cities in Hubei province. Effective reproductive number, R, gradually decreased from 3.6 (Wuhan), 3.4 (Hubei except Wuhan,) and 3.3 (China except Hubei) in stage 1 (from Dec 08, 2019 to Jan 22, 2020) to 0.67 (Wuhan), 0.59 (Hubei except Wuhan) and 0.63 (China except Hubei) respectively. Especially after January 23, 2020 when Wuhan City was closed, the infected number showed a turning point in Wuhan. By early April, there would be 42073 (95% confidence interval, 41673 to 42475), 21342 (95% confidence interval, 21057 to 21629) and 13384 (95% confidence interval, 13158 to 13612) infected cases in Wuhan, Hubei (except Wuhan) and China (except Hubei), respectively.

Conclusion: A series of control measures in China have effectively prevented the spread of COVID-19, and the epidemic should be under control in early April with very few new cases occasionally reported.

PROVIDER: S-EPMC7709801 | BioStudies |

REPOSITORIES: biostudies

Similar Datasets

2020-10-05 | BIOMD0000000962 | BioModels
| S-EPMC7095099 | BioStudies
| S-EPMC7416814 | BioStudies
| S-EPMC7273499 | BioStudies
| S-EPMC7251287 | BioStudies
| S-EPMC7181913 | BioStudies
| S-EPMC7591076 | BioStudies
| S-EPMC7928662 | BioStudies
| S-EPMC7159271 | BioStudies
| S-EPMC7269887 | BioStudies