All-People-Test-Based Methods for COVID-19 Infectious Disease Dynamics Simulation Model: Towards Citywide COVID...

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• The conversion rate between asymptomatic and symptomatic COVID-19 infections is critical for pandemic response.
• Effective COVID-19 management requires robust screening, prediction, and tracing strategies.
• Simulation modeling aids decision-making in public health crises.

Purpose of the Study:

• To develop a dynamic infectious disease model incorporating all-people testing (APT).
• To evaluate the effectiveness of APT strategies in controlling COVID-19 transmission.
• To improve the accuracy of epidemic prediction and inform public health interventions.

Main Methods:

• Developed the SETPG (A + I) RD + APT dynamic infectious disease model.
• Simulated COVID-19 case data from Hong Kong and the United States (Jan 2020 - Nov 2020).
• Compared screening strategies based on detection capability and tracking rates to assess time to R0=1.

Main Results:

• The SETPG (A + I) RD + APT model provided more realistic simulations and accurate epidemic predictions.
• Evaluated screening performance indicators, including the number of screenings and timespan required.
• Demonstrated that APT strategies can effectively reduce epidemic transmission and are adaptable to densely populated areas.

Conclusions:

• The developed model effectively simulates the impact of all-people testing on infectious disease dynamics.
• APT is a viable and effective strategy for controlling COVID-19, particularly in metropolises.
• The model aids in optimizing screening strategies for better epidemic control and prediction.