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The Spread of the Covid-19 Pandemic in Time and Space

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  • Hafner, Christian

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

Abstract

As the COVID-19 pandemic has had a profound impact on public health and global economies in 2020; it is crucial to understand how it developed and spread in time and space. This paper contributes to the growing literature by considering the dynamics of country-wise growth rates of infection numbers. Low-order serial correlation of growth rates is predominantly negative with cycles of two to four days for most countries. The results of fitted spatial autoregressive models suggest that there is high degree of spillover between countries. Forecast variances of many countries, in particular those with a high absolute number of infections, can to a large extent be explained by structural innovations of other countries. A better understanding of the serial and spatial dynamics of the spread of the pandemic may contribute to an improved containment and risk management.

Suggested Citation

  • Hafner, Christian, 2020. "The Spread of the Covid-19 Pandemic in Time and Space," LIDAM Reprints ISBA 2020031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2020031
    DOI: https://doi.org/10.3390/ijerph17113827
    Note: In: International Journal of Environmental Research and Public Health - Vol. 17, no.11, p. 3827 (2020)
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    Cited by:

    1. Li, Shaoran & Linton, Oliver, 2021. "When will the Covid-19 pandemic peak?," Journal of Econometrics, Elsevier, vol. 220(1), pages 130-157.
    2. Zhangbo Yang & Jiahao Zhang & Shanxing Gao & Hui Wang, 2022. "Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China," IJERPH, MDPI, vol. 19(2), pages 1-17, January.
    3. Sergio A. Chillon & Mikel Millan & Iñigo Aramendia & Unai Fernandez-Gamiz & Ekaitz Zulueta & Xabier Mendaza-Sagastizabal, 2021. "Natural Ventilation Characterization in a Classroom under Different Scenarios," IJERPH, MDPI, vol. 18(10), pages 1-13, May.
    4. Ewen Gallic & Michel Lubrano & Pierre Michel, 2021. "Optimal lockdowns: Analysing the efficiency of sanitary policies in Europe during the first wave," AMSE Working Papers 2111, Aix-Marseille School of Economics, France.
    5. Ștefan Cristian Gherghina & Daniel Ștefan Armeanu & Camelia Cătălina Joldeș, 2020. "Stock Market Reactions to COVID-19 Pandemic Outbreak: Quantitative Evidence from ARDL Bounds Tests and Granger Causality Analysis," IJERPH, MDPI, vol. 17(18), pages 1-35, September.
    6. Monika Małgorzata Wojcieszak-Zbierska & Anna Jęczmyk & Jan Zawadka & Jarosław Uglis, 2020. "Agritourism in the Era of the Coronavirus (COVID-19): A Rapid Assessment from Poland," Agriculture, MDPI, vol. 10(9), pages 1-19, September.
    7. Hou, Li & Jin, Baisuo & Wu, Yuehua, 2024. "Estimation and variable selection for high-dimensional spatial dynamic panel data models," Journal of Econometrics, Elsevier, vol. 238(2).
    8. Amanda M. Y. Chu & Thomas W. C. Chan & Mike K. P. So & Wing-Keung Wong, 2021. "Dynamic Network Analysis of COVID-19 with a Latent Pandemic Space Model," IJERPH, MDPI, vol. 18(6), pages 1-22, March.
    9. Samuel Domínguez-Amarillo & Jesica Fernández-Agüera & Sonia Cesteros-García & Roberto Alonso González-Lezcano, 2020. "Bad Air Can Also Kill: Residential Indoor Air Quality and Pollutant Exposure Risk during the COVID-19 Crisis," IJERPH, MDPI, vol. 17(19), pages 1-33, September.
    10. Anna Gloria Billé & Massimiliano Caporin, 2022. "Impact of COVID-19 on financial returns: a spatial dynamic panel data model with random effects," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-21, December.

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