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Matching Theory and Evidence on Covid-19 using a Stochastic Network SIR Model

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  • M. Hashem Pesaran
  • Cynthia Fan Yang

Abstract

This paper develops an individual-based stochastic network SIR model for the empirical analysis of the Covid-19 pandemic. It derives moment conditions for the number of infected and active cases for single as well as multigroup epidemic models. These moment conditions are used to investigate the identification and estimation of the transmission rates. The paper then proposes a method that jointly estimates the transmission rate and the magnitude of under-reporting of infected cases. Empirical evidence on six European countries matches the simulated outcomes once the under-reporting of infected cases is addressed. It is estimated that the number of actual cases could be between 4 to 10 times higher than the reported numbers in October 2020 and declined to 2 to 3 times in April 2021. The calibrated models are used in the counterfactual analyses of the impact of social distancing and vaccination on the epidemic evolution, and the timing of early interventions in the UK and Germany.

Suggested Citation

  • M. Hashem Pesaran & Cynthia Fan Yang, 2021. "Matching Theory and Evidence on Covid-19 using a Stochastic Network SIR Model," Papers 2109.00321, arXiv.org, revised Jan 2022.
    • Handle: RePEc:arx:papers:2109.00321
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    1. Alexander Chudik & M. Hashem Pesaran & Alessandro Rebucci, 2021. "COVID-19 Time-Varying Reproduction Numbers Worldwide: An Empirical Analysis of Mandatory and Voluntary Social Distancing," Globalization Institute Working Papers 407, Federal Reserve Bank of Dallas.
    2. Alexander Chudik & M. Hashem Pesaran & Alessandro Rebucci, 2020. "Voluntary and Mandatory Social Distancing: Evidence on COVID-19 Exposure Rates from Chinese Provinces and Selected Countries," NBER Working Papers 27039, National Bureau of Economic Research, Inc.
    3. Mohammad Akbarpour & Cody Cook & Aude Marzuoli & Simon Mongey & Abhishek Nagaraj & Matteo Saccarola & Pietro Tebaldi & Shoshana Vasserman & Hanbin Yang, 2020. "Socioeconomic Network Heterogeneity and Pandemic Policy Response," NBER Working Papers 27374, National Bureau of Economic Research, Inc.
    4. Alexis Akira Toda, 2020. "Susceptible-Infected-Recovered (SIR) Dynamics of COVID-19 and Economic Impact," Papers 2003.11221, arXiv.org, revised Mar 2020.
    5. Korolev, Ivan, 2021. "Identification and estimation of the SEIRD epidemic model for COVID-19," Journal of Econometrics, Elsevier, vol. 220(1), pages 63-85.
    6. Daron Acemoglu & Victor Chernozhukov & Iván Werning & Michael D. Whinston, 2021. "Optimal Targeted Lockdowns in a Multigroup SIR Model," American Economic Review: Insights, American Economic Association, vol. 3(4), pages 487-502, December.
    7. Fernández-Villaverde, Jesús & Jones, Charles I., 2022. "Estimating and simulating a SIRD Model of COVID-19 for many countries, states, and cities," Journal of Economic Dynamics and Control, Elsevier, vol. 140(C).
    8. Christian Gourieroux & Joann Jasiak, 2020. "Analysis of Virus Transmission: A Stochastic Transition Model Representation of Epidemiological Models," Annals of Economics and Statistics, GENES, issue 140, pages 1-26.
    9. Sean Elliott & Christian Gourieroux, 2020. "Uncertainty on the Reproduction Ratio in the SIR Model," Papers 2012.11542, arXiv.org.
    10. Andrew Atkeson & Karen Kopecky & Tao Zha, 2020. "Estimating and Forecasting Disease Scenarios for COVID-19 with an SIR Model," NBER Working Papers 27335, National Bureau of Economic Research, Inc.
    11. Joël Mossong & Niel Hens & Mark Jit & Philippe Beutels & Kari Auranen & Rafael Mikolajczyk & Marco Massari & Stefania Salmaso & Gianpaolo Scalia Tomba & Jacco Wallinga & Janneke Heijne & Malgorzata Sa, 2008. "Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 5(3), pages 1-1, March.
    12. Sean ELLIOTT & Christian GOURIEROUX, 2020. "Uncertainty on the Reproduction Ratio in the SIR Model," Working Papers 2020-31, Center for Research in Economics and Statistics.
    13. Ji, Chunyan & Jiang, Daqing & Shi, Ningzhong, 2011. "Multigroup SIR epidemic model with stochastic perturbation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(10), pages 1747-1762.
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    Citations

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    Cited by:

    1. Toru Kitagawa & Guanyi Wang, 2021. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," CeMMAP working papers CWP28/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Pongou, Roland & Tchuente, Guy & Tondji, Jean-Baptiste, 2021. "Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States," GLO Discussion Paper Series 957, Global Labor Organization (GLO).
    3. Ida Johnsson & M. Hashem Pesaran & Cynthia Fan Yang, 2023. "Structural Econometric Estimation of the Basic Reproduction Number for Covid-19 across U.S. States and Selected Countries," CESifo Working Paper Series 10659, CESifo.
    4. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," CESifo Working Paper Series 8977, CESifo.
    5. Alexander Chudik & M. Hashem Pesaran & Alessandro Rebucci, 2023. "Social Distancing, Vaccination and Evolution of COVID-19 Transmission Rates in Europe," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(2), pages 474-508, June.
    6. Alexander Chudik & M. Hashem Pesaran & Alessandro Rebucci, 2021. "COVID-19 Time-Varying Reproduction Numbers Worldwide: An Empirical Analysis of Mandatory and Voluntary Social Distancing," Globalization Institute Working Papers 407, Federal Reserve Bank of Dallas.
    7. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," Working Papers 21-18, Federal Reserve Bank of Philadelphia.
    8. Toru Kitagawa & Guanyi Wang, 2020. "Who Should Get Vaccinated? Individualized Allocation of Vaccines Over SIR Network," Papers 2012.04055, arXiv.org, revised Jul 2021.
    9. Jonas E. Arias & Jesús Fernández-Villaverde & Juan Rubio Ramírez & Minchul Shin, 2021. "The Causal Effects of Lockdown Policies on Health and Macroeconomic Outcomes," NBER Working Papers 28617, National Bureau of Economic Research, Inc.
    10. Roland Pongou & Guy Tchuente & Jean-Baptiste Tondji, 2021. "Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States," Papers 2110.10230, arXiv.org.
    11. Toru Kitagawa & Guanyi Wang, 2020. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," CeMMAP working papers CWP59/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • J18 - Labor and Demographic Economics - - Demographic Economics - - - Public Policy

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