Modeling outbreaks of COVID-19 in China: The impact of vaccination and other control measures on curbing the epidemic

ABSTRACT

This study aims to examine the development trend of COVID-19 in China and propose a model to assess the impacts of various prevention and control measures in combating the COVID-19 pandemic. Using COVID-19 cases reported by the National Health Commission of China from January 2, 2020, to January 2, 2022, we established a Susceptible-Exposed-Infected-Asymptomatic-Quarantined-Vaccinated-Hospitalized-Removed (SEIAQVHR) model to calculate the COVID-19 transmission rate and R_t effective reproduction number, and assess prevention and control measures. Additionally, we built a stochastic model to explore the development of the COVID-19 epidemic. We modeled the incidence trends in five outbreaks between 2020 and 2022. Some important features of the COVID-19 epidemic are mirrored in the estimates based on our SEIAQVHR model. Our model indicates that an infected index case entering the community has a 50%–60% chance to cause a COVID-19 outbreak. Wearing masks and getting vaccinated were the most effective measures among all the prevention and control measures. Specifically targeting asymptomatic individuals had no significant impact on the spread of COVID-19. By adjusting prevention and control parameters, we suggest that increasing the rates of effective vaccination and mask-wearing can significantly reduce COVID-19 cases in China. Our stochastic model analysis provides a useful tool for understanding the COVID-19 epidemic in China.

KEYWORDS:

• COVID-19
• public health
• compartmental model
• stochastic

Authors’ contribution

Conceptualization and Writing – original draft: Wenting Zha and Han Ni; Formal analysis and Software: Wenting Zha, Wentao Kuang and Yuxi He; Investigation and Data curation: Xuewen Yang and Jin Zhao; Validation and Methodology: Nan Ni, Nan Zhou and Yuxi He; Visualization and Project administration: Wentao Kuang, Liuyi Fu and Haoyun Dai; Writing – review & editing: Yuan Lv, Nan Zhou and Xuewen Yang; Funding acquisition: Yuan Lv and Wenting Zha. All authors have read and agreed to the published version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data sources for this study: https://ourworldindata.org/; https://coronavirus.jhu.edu/data.

Code

The research involves a significant amount of computational code, and we will only provide some of the key code snippets here:

s=randn(1,44);

for idx=1:length(T)-1

S(idx+1)=S(idx)-S(idx)*(1-(b+c+ds))*(a1*E(idx)+a2*I(idx)

 +a3*A(idx))-b*S(idx)-ds*S(idx)+0.00025*s(idx)*S(idx);

E(idx+1)=E(idx)+S(idx)*(1-(b+c+ds))*(a1*E(idx)+a2*I(idx)

 +a3*A(idx))-de*E(idx)-f*E(idx)+0.025*s(idx)*E(idx);

I(idx +1)=I(idx)+f*g*E(idx)-h*I(idx)-j*I(idx)+0.035*s(idx)*I(idx);

A(idx+1)=A(idx)+f*(1-g)*E(idx)-i*A(idx)-k*A(idx)+0.025*s(idx)

 *A(idx);

Qs(idx +1)=Qs(idx)+ds*S(idx)+0.025*s(idx)*Qs(idx);

Qe(idx +1)=Qe(idx)+de*E(idx)+0.035*s(idx)*Qe(idx);

V(idx +1)=V(idx)+b*S(idx)+0.025*s(idx)*V(idx);

H(idx +1)=H(idx)+h*I(idx)+i*A(idx)-l*H(idx)+0.025*s(idx)*H(idx);

R(idx +1)=R(idx)+j*I(idx)+k*A(idx)+l*H(idx)+0.025*s(idx)*R(idx);

Q(idx +1)=Qs(idx)+Qe(idx);

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website at https://doi.org/10.1080/21645515.2024.2338953.

Additional information

Funding

This research was funded by [Hunan Provincial Department of Education] grant number [JG2018B041], [Hunan Natural Fund] grant number [2020JJ4059], [Changsha City Science and Technology Plan Project] grant number [kq2001025] and [Research project of Hunan Provincial Health Commission] grant number [202212054651].