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

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  • Francisco Arroyo-Marioli
  • Francisco Bullano
  • Simas Kucinskas
  • Carlos Rondón-Moreno

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.

Suggested Citation

  • Francisco Arroyo-Marioli & Francisco Bullano & Simas Kucinskas & Carlos Rondón-Moreno, 2021. "Tracking R of COVID-19: A new real-time estimation using the Kalman filter," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-16, January.
    • Handle: RePEc:plo:pone00:0244474
    DOI: 10.1371/journal.pone.0244474
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    Cited by:

    1. James, Nick & Menzies, Max, 2023. "Collective infectivity of the pandemic over time and association with vaccine coverage and economic development," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Gächter, Martin & Huber, Florian & Meier, Martin, 2022. "A shot for the US economy," Finance Research Letters, Elsevier, vol. 47(PA).
    3. David Turner & Balázs Égert & Yvan Guillemette & Jarmila Botev, 2021. "The tortoise and the hare: The race between vaccine rollout and new COVID variants," OECD Economics Department Working Papers 1672, OECD Publishing.
    4. Campi, Gaetano & Bianconi, Antonio, 2022. "Periodic recurrent waves of Covid-19 epidemics and vaccination campaign," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    5. Hilde C. Bjørnland & Malin C. Jensen & Leif Anders Thorsrud, 2023. "Business Cycle and Health Dynamics during the COVID-19 Pandemic. A Scandinavian Perspective," Working Papers No 15/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    6. Meng, Xueyu & Lin, Jianhong & Fan, Yufei & Gao, Fujuan & Fenoaltea, Enrico Maria & Cai, Zhiqiang & Si, Shubin, 2023. "Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    7. Polyzos, Efstathios & Fotiadis, Anestis & Huan, Tzung-Cheng, 2023. "From Heroes to Scoundrels: Exploring the effects of online campaigns celebrating frontline workers on COVID-19 outcomes," Technology in Society, Elsevier, vol. 72(C).
    8. Ho, Paul & Lubik, Thomas A. & Matthes, Christian, 2023. "How to go viral: A COVID-19 model with endogenously time-varying parameters," Journal of Econometrics, Elsevier, vol. 232(1), pages 70-86.
    9. Daniel L. Millimet & Christopher F. Parmeter, 2022. "COVID‐19 severity: A new approach to quantifying global cases and deaths," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1178-1215, July.
    10. Díaz, Fernando & Henríquez, Pablo A. & Winkelried, Diego, 2022. "Stock market volatility and the COVID-19 reproductive number," Research in International Business and Finance, Elsevier, vol. 59(C).
    11. Candelon, Bertrand & Moura, Rubens, 2023. "Sovereign yield curves and the COVID-19 in emerging markets," Economic Modelling, Elsevier, vol. 127(C).

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