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. 2021 Jan 13;16(1):e0244474.
doi: 10.1371/journal.pone.0244474. eCollection 2021.

Tracking R of COVID-19: A new real-time estimation using the Kalman filter

Francisco Arroyo-Marioli  1 , Francisco Bullano  1 , Simas Kucinskas  2 , Carlos Rondón-Moreno  1
Affiliations

Tracking R of COVID-19: A new real-time estimation using the Kalman filter

Francisco Arroyo-Marioli et al. PLoS One. .

Abstract

We develop a new method for estimating the effective reproduction number of an infectious disease ( R ) and apply it to track the dynamics of COVID-19. The method is based on the fact that in the SIR model, R is linearly related to the growth rate of the number of infected individuals. This time-varying growth rate is estimated using the Kalman filter from data on new cases. The method is easy to implement in standard statistical software, and it performs well even when the number of infected individuals is imperfectly measured, or the infection does not follow the SIR model. Our estimates of R for COVID-19 for 124 countries across the world are provided in an interactive online dashboard, and they are used to assess the effectiveness of non-pharmaceutical interventions in a sample of 14 European countries.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Rt of COVID-19: China, Italy, and the US.
Estimates of the effective reproduction rate (Rt) of COVID-19 for selected countries. The sample consists of all dates after the total number of reported cases in the country has reached 100. 65% credible bounds shown by the shaded areas.
Fig 2
Fig 2. Rt of COVID-19: Brazil, India, and Germany.
Estimates of the effective reproduction rate (Rt) of COVID-19 for selected countries. The sample consists of all dates after the total number of reported cases in the country has reached 100. 65% credible bounds shown by the shaded areas.
Fig 3
Fig 3. R and policy interventions: Lockdowns.
Estimated effective reproduction number (Rt) one week before and three weeks after a lockdown is introduced in a country. The original sample consists of 14 European countries studied by Flaxman et al [25]. Heteroskedasticity-robust confidence bounds are shown by the shaded areas.
Fig 4
Fig 4. Mobility around introduction of lockdowns.
Mobility index (constructed from Google’s “COVID-19 Community Mobility Reports” [24]) one week before and three weeks after a lockdown is introduced in a country. See S1 Appendix (Section A.8) for details on the construction of the mobility index. The original sample consists of 14 European countries studied by Flaxman et al [25]. Heteroskedasticity-robust confidence bounds are shown by the shaded areas.
See this image and copyright information in PMC

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