A Multi-SCALE Community Network-Based SEIQR Model to Evaluate the Dynamic NPIs of COVID-19

Area of Science:

• Epidemiology
• Network Science
• Public Health

Background:

• Traditional infectious disease models struggle with urban-scale healthcare cost reduction via community-level interventions.
• Existing models often fail to meet simulation requirements across different spatial scales.

Purpose of the Study:

• To develop and validate a multi-scale network model for studying infectious disease dynamics in urban environments.
• To evaluate the effectiveness of dynamic non-pharmaceutical interventions (NPIs) for COVID-19 control in Shanghai.

Main Methods:

• Proposed a three-layer, multi-scale contact network to differentiate social interactions by area size and contact intensity.
• Constructed a susceptible-exposure-infection-quarantine-recovery (SEIQR) epidemic model based on the multi-scale network.
• Calculated the initial reproduction number (Rt) and assessed NPI effectiveness during the Shanghai COVID-19 outbreak (March-July 2022).

Main Results:

• The multi-scale network model successfully simulated epidemic dynamics at different scales.
• The model demonstrated the feasibility of assessing small-scale control measures like community quarantine and mobility restrictions.
• Experimental results confirmed the model's ability to meet diverse simulation needs.

Conclusions:

• A strict, long-term dynamic NPI strategy is effective in controlling epidemic spread, as shown by the Shanghai COVID-19 outbreak analysis.
• The proposed SEIQR model on a multi-scale network provides a robust framework for urban epidemic management and cost reduction.
• This approach supports urban managers in implementing targeted, scalable control measures for infectious diseases.