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

TL;DR

This paper introduces a novel estimation method for the COVID-19 reproduction number that utilizes a stochastic compartmental model and Kalman filtering, eliminating the need for serial interval data and providing reliable estimates.

Contribution

The proposed method simplifies COVID-19 reproduction number estimation by removing the requirement for serial interval data and combining EKF with low pass filtering for improved accuracy.

Findings

Comparable to existing Bayesian approaches in accuracy

Effective in Scandinavian countries with low positive rates

Does not require serial interval information

Abstract

We propose a new method to estimate the time-varying effective (or instantaneous) reproduction number of the novel coronavirus disease (COVID-19). The method is based on a discrete-time stochastic augmented compartmental model that describes the virus transmission. A two-stage estimation method, which combines the Extended Kalman Filter (EKF) to estimate the reported state variables (active and removed cases) and a low pass filter based on a rational transfer function to remove short term fluctuations of the reported cases, is used with case uncertainties that are assumed to follow a Gaussian distribution. Our method does not require information regarding serial intervals, which makes the estimation procedure simpler without reducing the quality of the estimate. We show that the proposed method is comparable to common approaches, e.g., age-structured and new cases based sequential Bayesian models. We also apply it to COVID-19 cases in the Scandinavian countries: Denmark, Sweden, and Norway, where the positive rates were below 5\% recommended by WHO.