A switching dynamic model based on phased COVID-19 data in Chongqing and its evaluation

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• Despite controlling COVID-19 domestically, China faces ongoing risks from imported cases.
• Effective public health policies are crucial for managing imported COVID-19.
• Understanding the impact of emergency response measures is vital for pandemic control.

Purpose of the Study:

• To analyze the influence of imported COVID-19 cases on local outbreaks.
• To evaluate the effectiveness of first-level emergency response (FLER) policies.
• To provide data-driven insights for public health policy and resource allocation.

Main Methods:

• Developed and validated a SCQIHR switching model using 2020 COVID-19 data from Chongqing.
• Investigated the impact of varying numbers of imported cases and FLER implementation timing.
• Assessed the model's performance using three real-world scenarios from Chongqing in 2021.

Main Results:

• The model accurately fits multidimensional time-series data.
• FLER implementation significantly decreased the effective reproduction number, contact rate, and misdetection rate.
• FLER significantly increased quarantine rates for close contacts and isolation rates for non-hospitalized cases.
• Peaks in quarantined and hospitalized cases correlated linearly with the number of imported cases and FLER delays.

Conclusions:

• The study provides valuable guidance for local public health policy and medical resource management.
• Implementing FLER effectively mitigates the local impact of imported COVID-19 cases.
• Timely FLER is critical to prevent surges in cases and healthcare burden.