Model-Based Evaluation of Transmissibility and Intervention Measures for a COVID-19 Outbreak in Xiamen City, China

Abstract

Background: In September 2021, there was an outbreak of coronavirus disease 2019 (COVID-19) in Xiamen, China. Various non-pharmacological interventions (NPIs) and pharmacological interventions (PIs) have been implemented to prevent and control the spread of the disease. This study aimed to evaluate the effectiveness of various interventions and to identify priorities for the implementation of prevention and control measures.

Methods: The data of patients with COVID-19 were collected from 8 to 30 September 2021. A Susceptible-Exposed-Infectious-Recovered (SEIR) dynamics model was developed to fit the data and simulate the effectiveness of interventions (medical treatment, isolation, social distancing, masking, and vaccination) under different scenarios. The effective reproductive number (R_eff ) was used to assess the transmissibility and transmission risk.

Results: A total of 236 cases of COVID-19 were reported in Xiamen. The epidemic curve was divided into three phases (R_eff = 6.8, 1.5, and 0). Notably, the cumulative number of cases was reduced by 99.67% due to the preventive and control measures implemented by the local government. In the effective containment stage, the number of cases could be reduced to 115 by intensifying the implementation of interventions. The total number of cases (TN) could be reduced by 29.66-95.34% when patients voluntarily visit fever clinics. When only two or three of these measures are implemented, the simulated TN may be greater than the actual number. As four measures were taken simultaneously, the TN may be <100, which is 57.63% less than the actual number. The simultaneous implementation of five interventions could rapidly control the transmission and reduce the number of cases to fewer than 25.

Conclusion: With the joint efforts of the government and the public, the outbreak was controlled quickly and effectively. Authorities could promptly cut the transmission chain and control the spread of the disease when patients with fever voluntarily went to the hospital. The ultimate effect of controlling the outbreak through only one intervention was not obvious. The combined community control and mask wearing, along with other interventions, could lead to rapid control of the outbreak and ultimately lower the total number of cases. More importantly, this would mitigate the impact of the outbreak on society and socioeconomics.

Keywords: COVID-19; dynamics model; evaluation; intervention; transmissibility.

Figure 1

The framework of SEIRQV model of intervention effect evaluation.

Figure 2

Fitting results of the SEIR model and the data of the actual secondary cases of SARS-CoV-2 infections in Xiamen City, China, 2021. (A) evaluation of COVID-19 transmissibility (Reff=6.8, 1.5, and 0); (B) effect of intervention measures at different stages.

Figure 3

The simulation results of comprehensive intervention measures in Xiamen City, China, 2021. (A) simulates the reduction of transmission capacity during the effective containment stage period of the epidemic; (B) simulates the decrease in the duration of the epidemic effective containment stage; (C) is the scenario simulating the advance of the peak time of the epidemic; and (D) simulates the situation where the peak time of the epidemic is advanced and there is no effective containment stage.

Figure 4

The simulation results of single intervention measures in Xiamen City, China, 2021. (A) Medical treatment; (B) isolation; (C) social distancing; (D) wearing masks; and (E) vaccination.

Figure 5

The simulation results of a mix of two interventions in Xiamen City, China, 2021. (A) Medical treatment and isolation; (B) medical treatment and social distancing; (C) medical treatment and wearing mask; (D) medical treatment and vaccination; (E) isolation and social distancing; (F) isolation and wearing mask; (G) isolation and vaccination; (H) social distancing and wearing mask; (I) social distancing and vaccination; and (J) wearing mask and vaccination.

Figure 6

The simulation results of a mix of three interventions in Xiamen City, China, 2021. (A) Medical treatment and isolation and social distancing; (B) medical treatment and isolation and wearing mask; (C) medical treatment and isolation and vaccination; (D) medical treatment and social distancing and wearing mask; (E) medical treatment and social distancing and vaccination; (F) medical treatment and waring mask and vaccination; (G) social distancing and isolation and wearing mask; (H) social distancing and isolation and vaccination; (I) isolation and wearing mask and vaccination; and (J) social distancing and wearing mask and vaccination.

Figure 7

The simulation results of a mix of four interventions in Xiamen City, China, 2021. (A) Medical treatment and isolation and social distancing and wearing mask; (B) medical treatment and isolation and social distancing and vaccination; (C) medical treatment and isolation and wearing mask and vaccination; (D) medical treatment and vaccination and social distancing and wearing mask; and (E) isolation and vaccination and social distancing and wearing mask.

Figure 8

The simulation results of mix of five interventions in Xiamen City, China, 2021. (A) Medical treatment (1/γ = 2), Isolation (φ = 0,0.1), Social distancing (x = 14,15), Wearing masks (q =100%), Vaccination (v = 0~50%); (B) Medical treatment (1/γ = 3), Isolation (φ = 0–0.3), Social distancing (x = 8–15), Wearing masks (q = 60%–100%), Vaccination (v = 0~50%); (C) Medical treatment (1/γ = 4), Isolation (φ = 0–0.4), Social distancing (x = 6–15), Wearing masks (q = 40%–100%), Vaccination (v = 0–70%); (D) Medical treatment(1/γ = 5), Isolation (φ = 0–0.5), Social distancing (x = 5–15), Wearing masks (q = 30%–100%), Vaccination (v = 0–80%).