Epidemiological feature analysis of SVEIR model with control strategy and variant evolution

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• COVID-19 outbreaks, particularly Omicron variants, present complex challenges for epidemic control.
• Understanding the interplay of non-pharmaceutical interventions, vaccination, and host factors is crucial for managing infectious disease spread.
• Small-scale epidemic modeling, like that of Jinzhou City, offers unique insights often missed in broader studies.

Purpose of the Study:

• To analyze the dynamical and epidemiological features of two Omicron variant-led COVID-19 epidemics in Jinzhou City.
• To evaluate the effectiveness of non-pharmaceutical interventions and vaccination strategies.
• To estimate the basic and effective reproduction numbers for different Omicron subvariants.

Main Methods:

• Utilized the Susceptible-Vaccinated-Exposed-Infected-Recovered (SVEIR) model with control parameters.
• Employed surveillance data alongside the SVEIR model for analysis.
• Applied three methods (maximum likelihood, exponential growth rate, next generation matrix) to estimate the basic reproduction number.

Main Results:

• Observed an increasing trend in the basic reproduction number from Omicron BA.5.2 to BA.2.12.1.
• Jinzhou wave 1 peaked on November 1 and was controlled in 7 days; wave 2 peaked on November 28 and was controlled in 5 days.
• Assessed the impact of non-pharmaceutical interventions (awareness delay, peak delay, control intensity) on infection scales for Omicron and EG.5 variants.

Conclusions:

• The SVEIR model effectively captured epidemic dynamics and the impact of control measures in Jinzhou City.
• Findings provide insights into variant evolution and inform future public health strategies for epidemic preparedness.
• The study highlights the importance of timely and intensive non-pharmaceutical interventions for controlling COVID-19 waves.