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. 2020 Jun;92(6):645-659.
doi: 10.1002/jmv.25750. Epub 2020 Mar 16.

Transmission dynamics of the COVID-19 outbreak and effectiveness of government interventions: A data-driven analysis

Yaqing Fang  1 , Yiting Nie  2 , Marshare Penny  3
Affiliations

Transmission dynamics of the COVID-19 outbreak and effectiveness of government interventions: A data-driven analysis

Yaqing Fang et al. J Med Virol. 2020 Jun.

Abstract

Using the parameterized susceptible-exposed-infectious-recovered model, we simulated the spread dynamics of coronavirus disease 2019 (COVID-19) outbreak and impact of different control measures, conducted the sensitivity analysis to identify the key factor, plotted the trend curve of effective reproductive number (R), and performed data fitting after the simulation. By simulation and data fitting, the model showed the peak existing confirmed cases of 59 769 arriving on 15 February 2020, with the coefficient of determination close to 1 and the fitting bias 3.02%, suggesting high precision of the data-fitting results. More rigorous government control policies were associated with a slower increase in the infected population. Isolation and protective procedures would be less effective as more cases accrue, so the optimization of the treatment plan and the development of specific drugs would be of more importance. There was an upward trend of R in the beginning, followed by a downward trend, a temporary rebound, and another continuous decline. The feature of high infectiousness for severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) led to an upward trend, and government measures contributed to the temporary rebound and declines. The declines of R could be exploited as strong evidence for the effectiveness of the interventions. Evidence from the four-phase stringent measures showed that it was significant to ensure early detection, early isolation, early treatment, adequate medical supplies, patients' being admitted to designated hospitals, and comprehensive therapeutic strategy. Collaborative efforts are required to combat the novel coronavirus, focusing on both persistent strict domestic interventions and vigilance against exogenous imported cases.

Keywords: COVID-19; SARS-CoV-2; SEIR; basic reproductive number; data fitting; data simulation; effective reproductive number; effectiveness; intervention; sensitivity analysis; transmission.

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Conflict of interest statement

The authors declare that there are no conflict of interests.

Figures

Figure 1
Figure 1
The distribution of existing confirmed COVID‐19 cases in Hubei, China, and worldwide. COVID‐19, coronavirus disease 2019
Figure 2
Figure 2
The trend of existing confirmed cases in China and overseas
Figure 3
Figure 3
Dynamics of COVID‐19 based on the SEIR model. COVID‐19, coronavirus disease 2019; SEIR, susceptible‐exposed‐infectious‐recovered
Figure 4
Figure 4
S value simulation curve of susceptible population
Figure 5
Figure 5
I value the simulation curve of the infected population
Figure 6
Figure 6
Daily number of new recovered cases and new confirmed cases in China
Figure 7
Figure 7
Sensitivity analysis of different parameters
Figure 8
Figure 8
R values from 20 January to 29 February 2020
Figure 9
Figure 9
The trend of R values overtime
Figure 10
Figure 10
Results of Dickey‐Fuller test
Figure 11
Figure 11
Data decomposed into time series
Figure 12
Figure 12
Data fitting by polyregression
Figure 13
Figure 13
Result of grid search No. 1
Figure 14
Figure 14
The top five parameters. MSE, mean square error
Figure 15
Figure 15
Predict linear
Figure 16
Figure 16
The SEIR model simulation of COVID‐19 after data fitting. COVID‐19, coronavirus disease 2019; SEIR, susceptible‐exposed‐infectious‐recovered
See this image and copyright information in PMC

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