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COVID-19 with uncertain phases: estimation issues with an illustration for Argentina

Author

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  • Ahumada, Hildegart
  • Espina, Santos
  • Navajas, Fernando H.

Abstract

We use an approach to assess COVID-19 performance that starts from what we consider is the most likely set of hypotheses about the uncertain evolution of the pandemic, that envisage a sequence of different cycles with unknown duration and magnitude over 18-24 months. This pattern implies a research strategy where short-term time series forecasting of the evolution of observed cases and deaths play a central role in both detecting transitions from phase to phase of infections and the estimation of necessarily changing structural parameters and indicators of a SIRD model. We illustrate our approach with Buenos Aires City performance, which represents a significant share of the Argentine case with an early introduction of a lockdown followed by a second wave latter on. This approach can be extended to include measures of the intensity and compliance of lockdowns, as well as the heterogeneity across areas. We find that mobility (as a proxy for the effectiveness of the lockdown) has an impact on observed cases in Buenos Aires City with a lag of 8 days and deaths relate with new cases registered 16 to 19 days before. Mobility has a clear impact on the growth rate of cases and by extension deaths. Our approach and results have implications for policy dialogue issues.

Suggested Citation

  • Ahumada, Hildegart & Espina, Santos & Navajas, Fernando H., 2020. "COVID-19 with uncertain phases: estimation issues with an illustration for Argentina," MPRA Paper 101466, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:101466
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    References listed on IDEAS

    as
    1. Christopher F Baum & Miguel Henry, 2020. "Socioeconomic Factors influencing the Spatial Spread of COVID-19 in the United States," London Stata Conference 2020 05, Stata Users Group.
    2. Fernando Alvarez & David Argente, 2020. "A Simple Planning Problem for COVID-19 Lockdown," Working Papers 2020-34, Becker Friedman Institute for Research In Economics.
    3. Andrew Atkeson, 2020. "What Will Be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios," NBER Working Papers 26867, National Bureau of Economic Research, Inc.
    4. Titan Alon & Minki Kim & David Lagakos & Mitchell VanVuren, 2020. "How Should Policy Responses to the COVID-19 Pandemic Differ in the Developing World?," NBER Working Papers 27273, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Fernando Luis Valli & Ayelen Bargados & Christian Haedo, 2021. "Analysis of the economic impact of COVID-19 pandemic on Small and Medium Enterprises," Asociación Argentina de Economía Política: Working Papers 4528, Asociación Argentina de Economía Política.
    2. Chen, Kexin & Pun, Chi Seng & Wong, Hoi Ying, 2023. "Efficient social distancing during the COVID-19 pandemic: Integrating economic and public health considerations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 84-98.
    3. Hildegart Ahumada & Eduardo Cavallo & Santos Espina-Mairal & Fernando Navajas, 2022. "Sectoral Productivity Growth, COVID-19 Shocks, and Infrastructure," Economics of Disasters and Climate Change, Springer, vol. 6(1), pages 1-28, March.
    4. Tsiligianni, Christiana & Tsiligiannis, Aristeides & Tsiliyannis, Christos, 2023. "A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws," European Journal of Operational Research, Elsevier, vol. 304(1), pages 42-56.

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    More about this item

    Keywords

    COVID-19; Forecasting; SIRD; Lockdown; Mobility;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • I10 - Health, Education, and Welfare - - Health - - - General

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