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Learning Epidemiology by Doing: The Empirical Implications of a Spatial-SIR Model with Behavioral Responses

Author

Listed:
  • Alberto Bisin
  • Andrea Moro

Abstract

We simulate a spatial behavioral model of the diffusion of an infection to understand the role of geographic characteristics: the number and distribution of outbreaks, population size, density, and agents' movements. We show that several invariance properties of the SIR model concerning these variables do not hold when agents interact with neighbors in a (two dimensional) geographical space. Indeed, the spatial model's local interactions generate matching frictions and local herd immunity effects, which play a fundamental role in the infection dynamics. We also show that geographical factors affect how behavioral responses affect the epidemics. We derive relevant implications for estimating the effects of the epidemics and policy interventions that use panel data from several geographical units.

Suggested Citation

  • Alberto Bisin & Andrea Moro, 2021. "Learning Epidemiology by Doing: The Empirical Implications of a Spatial-SIR Model with Behavioral Responses," Papers 2102.10145, arXiv.org, revised Jun 2021.
  • Handle: RePEc:arx:papers:2102.10145
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    Cited by:

    1. Giannone, Elisa & Paixão, Nuno & Pang, Xinle, 2022. "JUE Insight: The geography of pandemic containment," Journal of Urban Economics, Elsevier, vol. 127(C).
    2. Dirk Niepelt & Mart n Gonzalez-Eiras, 2020. "Optimally Controlling an Epidemic," Diskussionsschriften dp2019, Universitaet Bern, Departement Volkswirtschaft.
    3. Edward L. Glaeser & Caitlin S. Gorback & Stephen J. Redding, 2020. "How Much Does COVID-19 Increase with Mobility? Evidence from New York and Four Other U.S. Cities," Working Papers 2020-22, Princeton University. Economics Department..
    4. Bello, Piera & Rocco, Lorenzo, 2022. "Education and COVID-19 excess mortality," Economics & Human Biology, Elsevier, vol. 47(C).
    5. Mario J. Crucini & Oscar O'Flaherty, 2020. "Stay-at-Home Orders in a Fiscal Union," NBER Working Papers 28182, National Bureau of Economic Research, Inc.
    6. Pol Antràs & Stephen J. Redding & Esteban Rossi-Hansberg, 2023. "Globalization and Pandemics," American Economic Review, American Economic Association, vol. 113(4), pages 939-981, April.
    7. Glaeser, Edward L. & Gorback, Caitlin & Redding, Stephen J., 2022. "JUE Insight: How much does COVID-19 increase with mobility? Evidence from New York and four other U.S. cities," Journal of Urban Economics, Elsevier, vol. 127(C).
    8. Mattia Borsati & Silvio Nocera & Marco Percoco, 2020. "Questioning the spatial association between the spread of COVID-19 and transit usage in Italy," GREEN Working Papers 11, GREEN, Centre for Research on Geography, Resources, Environment, Energy & Networks, Universita' Bocconi, Milano, Italy.
    9. Nicola Borri & Francesco Drago & Chiara Santantonio & Francesco Sobbrio, 2021. "The “Great Lockdown”: Inactive workers and mortality by Covid‐19," Health Economics, John Wiley & Sons, Ltd., vol. 30(10), pages 2367-2382, September.
    10. Joshua S. Gans, 2022. "The Economic Consequences of R= 1: Towards a Workable Behavioural Epidemiological Model of Pandemics," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 14(1), pages 3-25, April.
    11. Jacek Rothert & Ryan Brady & Michael Insler, 2020. "The Fragmented United States of America: The impact of scattered lock-down policies on country-wide infections," Departmental Working Papers 65, United States Naval Academy Department of Economics.
    12. Andrew G. Atkeson & Karen A. Kopecky & Tao Zha, 2024. "Four Stylized Facts About Covid‐19," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 65(1), pages 3-42, February.
    13. Badi H. Baltagi & Ying Deng & Jing Li & Zhenlin Yang, 2023. "Cities in a pandemic: Evidence from China," Journal of Regional Science, Wiley Blackwell, vol. 63(2), pages 379-408, March.
    14. Wood, Aaron D. & Berry, Kevin, 2024. "COVID-19 transmission in a resource dependent community with heterogeneous populations: An agent-based modeling approach," Economics & Human Biology, Elsevier, vol. 52(C).
    15. Tomoo INOUE & Tatsuyoshi OKIMOTO, 2022. "Exploring the Dynamic Relationship between Mobility and the Spread of COVID-19, and the Role of Vaccines," Discussion papers 22011, Research Institute of Economy, Trade and Industry (RIETI).
    16. Jacek Rothert & Ryan Brady & Michael Insler, 2020. "Local containment policies and country-wide spread of Covid-19 in the United States: an epidemiological analysis," GRAPE Working Papers 48, GRAPE Group for Research in Applied Economics.
    17. Borsati, Mattia & Nocera, Silvio & Percoco, Marco, 2022. "Questioning the spatial association between the initial spread of COVID-19 and transit usage in Italy," Research in Transportation Economics, Elsevier, vol. 95(C).
    18. Bisin, Alberto & Gottardi, Piero, 2021. "Efficient policy interventions in an epidemic," Journal of Public Economics, Elsevier, vol. 200(C).
    19. Mattia Borsati & Michele Cascarano & Marco Percoco, 2023. "Resilience to health shocks and the spatial extent of local labour markets: evidence from the Covid-19 outbreak in Italy," Regional Studies, Taylor & Francis Journals, vol. 57(12), pages 2503-2520, December.
    20. Desmet, Klaus & Wacziarg, Romain, 2022. "JUE Insight: Understanding spatial variation in COVID-19 across the United States," Journal of Urban Economics, Elsevier, vol. 127(C).
    21. repec:osf:socarx:yxdc5_v1 is not listed on IDEAS
    22. Richard Breen & John Ermisch, 2021. "The distributional impact of COVID-19: Geographic variation in mortality in England," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 44(17), pages 397-414.

    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • I1 - Health, Education, and Welfare - - Health

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