Forecast for peak infections in the second wave of the Omicron after the adjustment of zero-COVID policy in the mainland of China

Abstract

On December 7, 2022, the Chinese government optimized the current epidemic prevention and control policy, and no longer adopted the zero-COVID policy and mandatory quarantine measures. Based on the above policy changes, this paper establishes a compartment dynamics model considering age distribution, home isolation and vaccinations. Parameter estimation was performed using improved least squares and Nelder-Mead simplex algorithms combined with modified case data. Then, using the estimated parameter values to predict a second wave of the outbreak, the peak of severe cases will reach on 8 May 2023, the number of severe cases will reach 206,000. Next, it is proposed that with the extension of the effective time of antibodies obtained after infection, the peak of severe cases in the second wave of the epidemic will be delayed, and the final scale of the disease will be reduced. When the effectiveness of antibodies is 6 months, the severe cases of the second wave will peak on July 5, 2023, the number of severe cases is 194,000. Finally, the importance of vaccination rates is demonstrated, when the vaccination rate of susceptible people under 60 years old reaches 98%, and the vaccination rate of susceptible people over 60 years old reaches 96%, the peak of severe cases in the second wave of the epidemic will be reached on 13 July 2023, when the number of severe cases is 166,000.

Keywords: Nelder-Mead Simplex direct search algorithm; Omicron; Parameter estimation; The severe cases; The zero-COVID policy.

Fig. 1

The chart of the interpolation results and data corrections. (a)Interpolation results and data correction for severe cases. (b)Interpolation results for cumulative deaths.

Fig. 2

Flow chart of compartments of the model.

Fig. 3

The chart of the fitted result and prediction curve. (a) Fitting results and prediction for severe cases. (b) Fitting results and prediction for cumulative deaths.

Fig. 4

The change chart of the ${\mathcal{R}}_0$. (a) Change curve of ${\mathcal{R}}_0$ in susceptible people under 60 years old. (b) Change curve of ${\mathcal{R}}_0$ in susceptible people over 60 years old.

Fig. 5

Simulation result plots of different antibody decay times. (a) Simulation results for severe cases. (b) Simulation results for cumulative deaths.

Fig. 6

Simulation result plots of different vaccination rate. (a) Simulation results for severe cases. (b) Simulation results for cumulative deaths.