A new estimation method for COVID-19 time-varying reproduction number using active cases.

Area of Science:

• Epidemiology
• Mathematical Modeling
• Infectious Disease Dynamics

Background:

• Accurate estimation of the effective reproduction number (Rt) is crucial for managing infectious disease outbreaks like COVID-19.
• Existing methods for Rt estimation can be complex and may require detailed data, such as serial intervals.
• The need for robust and simplified methods to track virus transmission dynamics persists.

Purpose of the Study:

• To propose and validate a novel, simplified method for estimating the time-varying effective reproduction number (Rt) of COVID-19.
• To demonstrate the method's efficacy using a discrete-time stochastic augmented compartmental model.
• To compare the proposed method against established Rt estimation techniques.

Main Methods:

• Utilized a discrete-time stochastic augmented compartmental model for virus transmission.
• Employed a two-stage estimation process combining the Extended Kalman Filter (EKF) and a low-pass filter.
• Assumed Gaussian distribution for case uncertainties and did not require serial interval data.

Main Results:

• The proposed method provides reliable estimates of the effective reproduction number for COVID-19.
• The method demonstrated comparable performance to existing sequential Bayesian models.
• Applied successfully to COVID-19 data from Denmark, Sweden, and Norway with low positive rates.

Conclusions:

• The novel estimation method offers a simpler yet effective approach to calculating the effective reproduction number for COVID-19.
• Eliminating the need for serial interval data enhances the practicality of Rt estimation.
• The method's successful application in Scandinavian countries highlights its potential for real-world epidemiological surveillance.