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TRACKING THE MUTANT: FORECASTING AND NOWCASTING COVID-19 IN THE UK IN 2021

Published online by Cambridge University Press:  23 June 2021

Andrew Harvey ,
Paul Kattuman Open the ORCID record for Paul Kattuman [Opens in a new window]  and
Craig Thamotheram
Andrew Harvey
Affiliation:
Faculty of Economics, University of Cambridge, Cambridge, United Kingdom
Paul Kattuman*
Affiliation:
Cambridge Judge Business School, University of Cambridge, Cambridge, United Kingdom
Craig Thamotheram
Affiliation:
National Institute of Economic and Social Research, London, United Kingdom
*
*Corresponding author. Email: p.kattuman@jbs.cam.ac.uk
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Abstract

A new class of time series models is used to track the progress of the COVID-19 epidemic in the UK in early 2021. Models are fitted to England and the regions, as well as to the UK as a whole. The growth rate of the daily number of cases and the instantaneous reproduction number are computed regularly and compared with those produced by SAGE. The results from figures published each day are compared with results based on figures by specimen date, which may be more accurate but are subject to substantial revisions. It is then shown how data from the two different sources can be combined in bivariate models.

Keywords

data revisionsepidemicKalman filterreproduction number (R)state-space modelC22C32
Type
Research Article
Information
National Institute Economic Review , Volume 256: THE IMPACT OF COVID-19 ON MACROECONOMIC FORECASTING , Spring 2021 , pp. 110 - 126
Copyright
© National Institute Economic Review, 2021

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References

Abbott, S., Hellewell, J., Thompson, R. N. et al. (2020), ‘Estimating the time-varying reproduction number of SARS-CoV-2 using national and subnational case counts’. Wellcome Open Research, 5:112. https://doi.org/10.12688/wellcomeopenres.16006.1.CrossRefGoogle Scholar
Anesti, N., Galvão, A.B. and Miranda-Agrippino, S. (2021), ‘Uncertain kingdom: Nowcasting GDP and its revisions’, Journal of Applied Econometrics, to appear.CrossRefGoogle Scholar
Avery, C., Bossert, W., Clark, A., Ellison, G. and Ellison, S. (2020), ‘An economist’s guide to epidemiology models of infectious disease’, Journal of Economic Perspectives, 34, 4, pp. 79–104.10.1257/jep.34.4.79CrossRefGoogle Scholar
Bettencourt, L.M., and Ribeiro, R.M. (2008), ‘Real time Bayesian estimation of the epidemic potential of emerging infectious diseases’. PLoS One, 3, e2185. https://doi.org/10.1371/journal.pone.0002185.CrossRefGoogle ScholarPubMed
Chowell, G., Nishiura, H. and Bettencourt, L.M.A. (2007), ‘Comparative estimation of the reproduction number for pandemic influenza from daily case notification data’, Journal of the Royal Society Interface, 4, 12, pp. 155–66.CrossRefGoogle ScholarPubMed
Durbin, J. and Koopman, S. (2012), Time Series Analysis by State Space Methods, Oxford: Oxford University Press.CrossRefGoogle Scholar
Harvey, A. (1989), Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge: Cambridge University Press.Google Scholar
Harvey, A. (2020), ‘Time series models for epidemics: Leading indicators, control groups and policy assessment’, Cambridge Working Papers in Economics 2114. https://doi.org/10.17863/CAM.65417.CrossRefGoogle Scholar
Harvey, A. and Chung, C. (2000), ‘Estimating the underlying change in unemployment in the UK, with discussion’, Journal of the Royal Statistical Society: Series A, 163, pp. 303–39.CrossRefGoogle Scholar
Harvey, A. and Kattuman, P. (2020a), ‘Time series models based on growth curves with applications to forecasting coronavirus’, Harvard Data Science Review, Special Issue 1—COVID-19, available online at https://hdsr.mitpress.mit.edu/pub/ozgjx0yn.CrossRefGoogle Scholar
Harvey, A. and Kattuman, P. (2020b), ‘A farewell to R: Time series models for tracking and forecasting epidemics’, CEPR Working paper, Issue 51, 7 October, available online at https://cepr.org/content/covid-economics.Google Scholar
Höhle, M. and an der Heiden, M. (2014), ‘Bayesian nowcasting during the STEC O104:H4 outbreak in Germany’, Biometrics, 70, pp. 993–1002.10.1111/biom.12194CrossRefGoogle ScholarPubMed
Ioannidis, J., Cripps, S. and Tanner, M. (2020), ‘Forecasting for COVID-19 has failed’, International Journal of Forecasting, in press. 10.1016/j.ijforecast.2020.08.004.Google Scholar
Koopman, S., Lit, R. and Harvey, A. (2020), STAMP 8.4: Structural Time Series Analyser, Modeller and Predictor. 5th ed., London: Timberlake Consultants.Google Scholar
Lit, R., Koopman, S.J. and Koopman, A.C. (2020), ‘Time Series Lab - Score edition’. https://timeserieslab.com.Google Scholar
Wallinga, J. and Lipsitch, M. (2007), ‘How generation intervals shape the relationship between growth rates and reproductive numbers’, Proceedings of the Royal Society B, 274, pp. 599–604.10.1098/rspb.2006.3754CrossRefGoogle ScholarPubMed
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