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Modelling COVID-19 contagion: risk assessment and targeted mitigation policies

Author

Listed:
  • Rama Cont

    (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique, MI - Mathematical Institute [Oxford] - University of Oxford)

  • Artur Kotlicki

    (MI - Mathematical Institute [Oxford] - University of Oxford)

  • Renyuan Xu

    (MI - Mathematical Institute [Oxford] - University of Oxford)

Abstract

We use a spatial epidemic model with demographic and geographic heterogeneity to study the regional dynamics of COVID-19 across 133 regions in England. Our model emphasises the role of variability of regional outcomes and hetero-geneity across age groups and geographic locations, and provides a framework for assessing the impact of policies targeted towards sub-populations or regions. We define a concept of efficiency for comparative analysis of epidemic control policies and show targeted mitigation policies based on local monitoring to be more efficient than country-level or non-targeted measures. In particular, our results emphasise the importance of shielding vulnerable subpopulations and show that targeted policies based on local monitoring can considerably lower fatality forecasts and, in many cases, prevent the emergence of second waves which may occur under centralised policies.

Suggested Citation

  • Rama Cont & Artur Kotlicki & Renyuan Xu, 2020. "Modelling COVID-19 contagion: risk assessment and targeted mitigation policies," Working Papers hal-02923033, HAL.
    • Handle: RePEc:hal:wpaper:hal-02923033
    Note: View the original document on HAL open archive server: https://hal.science/hal-02923033v3
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    References listed on IDEAS

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    1. Daron Acemoglu & Victor Chernozhukov & Ivàn Werning & Michael D. Whinston, 2020. "A Multi-Risk SIR Model with Optimally Targeted Lockdown," CeMMAP working papers CWP14/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Robert S. Pindyck, 2020. "COVID-19 and the Welfare Effects of Reducing Contagion," NBER Working Papers 27121, National Bureau of Economic Research, Inc.
    3. 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.
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    Cited by:

    1. Ana Duarte & Simon Walker & Andrew Metry & Ruth Wong & Jasmina Panovska-Griffiths & Mark Sculpher, 2021. "Jointly Modelling Economics and Epidemiology to Support Public Policy Decisions for the COVID-19 Response: A Review of UK Studies," PharmacoEconomics, Springer, vol. 39(8), pages 879-887, August.
    2. Mourao, Paulo & Bento, Ricardo, 2021. "Explaining COVID-19 contagion in Portuguese municipalities using spatial autocorrelation models," Revista Galega de Economía, University of Santiago de Compostela. Faculty of Economics and Business., vol. 30(1), pages 1-12.
    3. Febrero Paños, Eladio & Bermejo Patón, Fernando, 2021. "Estimating the impact of COVID-19 on the Spanish economy with input-output analysis," Revista Galega de Economía, University of Santiago de Compostela. Faculty of Economics and Business., vol. 30(1), pages 1-19.
    4. 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.
    5. Đorđević, J. & Papić, I. & Šuvak, N., 2021. "A two diffusion stochastic model for the spread of the new corona virus SARS-CoV-2," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).

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