tetano
Editor, Senior Moderator
Sci Rep
. 2022 Apr 23;12(1):6675.
doi: 10.1038/s41598-022-10723-w.
A new estimation method for COVID-19 time-varying reproduction number using active cases
Agus Hasan[SUP] 1 [/SUP], Hadi Susanto[SUP] 2 3 [/SUP], Venansius Tjahjono[SUP] 4 [/SUP], Rudy Kusdiantara[SUP] 5 [/SUP], Endah Putri[SUP] 4 [/SUP], Nuning Nuraini[SUP] 5 [/SUP], Panji Hadisoemarto[SUP] 6 [/SUP]
Affiliations
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.
. 2022 Apr 23;12(1):6675.
doi: 10.1038/s41598-022-10723-w.
A new estimation method for COVID-19 time-varying reproduction number using active cases
Agus Hasan[SUP] 1 [/SUP], Hadi Susanto[SUP] 2 3 [/SUP], Venansius Tjahjono[SUP] 4 [/SUP], Rudy Kusdiantara[SUP] 5 [/SUP], Endah Putri[SUP] 4 [/SUP], Nuning Nuraini[SUP] 5 [/SUP], Panji Hadisoemarto[SUP] 6 [/SUP]
Affiliations
- PMID: 35461352
- DOI: 10.1038/s41598-022-10723-w
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.