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Time series modeling of epidemics: leading indicators, control groups and policy assessment

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  • Harvey, A. C.

Abstract

This article shows how new time series models can used to track the progress of an epidemic, forecast key variables and evaluate the effects of policies. The univariate framework of Harvey and Kattuman (2020) is extended to model the relationship between two or more series, and the role of common trends is discussed. Data on daily deaths from Covid-19 in Italy and the UK provides an example of leading indicators when there is balanced growth. When growth is not balanced, the model can be extended by including a nonstationary component in the leading series. The viability of this model is investigated by examining the relationship between new cases and deaths in the Florida second wave of summer 2020. The balanced growth framework is then used as the basis for policy evaluation by showing how some variables can serve as control groups for a target variable. This approach is used to investigate the consequences of Sweden’s soft lockdown coronavirus policy.

Suggested Citation

  • Harvey, A. C., 2021. "Time series modeling of epidemics: leading indicators, control groups and policy assessment," Cambridge Working Papers in Economics 2114, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:2114
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    References listed on IDEAS

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    1. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    2. Sang-Wook (Stanley) Cho, 2020. "Quantifying the impact of nonpharmaceutical interventions during the COVID-19 outbreak: The case of Sweden," The Econometrics Journal, Royal Economic Society, vol. 23(3), pages 323-344.
    3. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    4. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    5. Alberto Abadie & Alexis Diamond & Jens Hainmueller, 2015. "Comparative Politics and the Synthetic Control Method," American Journal of Political Science, John Wiley & Sons, vol. 59(2), pages 495-510, February.
    6. Benjamin Born & Alexander M Dietrich & Gernot J Müller, 2021. "The lockdown effect: A counterfactual for Sweden," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-13, April.
    7. Sang-Wook (Stanley) Cho, 0. "Quantifying the impact of nonpharmaceutical interventions during the COVID-19 outbreak: The case of Sweden," Econometrics Journal, Royal Economic Society, vol. 23(3), pages 323-344.
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    More about this item

    Keywords

    Balanced growth; Co-integration; Covid-19; Gompertz curve; Kalman filter; Stochastic trend;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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