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Regional now- and forecasting for data reported with delay: toward surveillance of COVID-19 infections

Author

Listed:
  • Giacomo De Nicola

    (Ludwig-Maximillians-Universität München)

  • Marc Schneble

    (Ludwig-Maximillians-Universität München)

  • Göran Kauermann

    (Ludwig-Maximillians-Universität München)

  • Ursula Berger

    (Ludwig-Maximillians-Universität München)

Abstract

Governments around the world continue to act to contain and mitigate the spread of COVID-19. The rapidly evolving situation compels officials and executives to continuously adapt policies and social distancing measures depending on the current state of the spread of the disease. In this context, it is crucial for policymakers to have a firm grasp on what the current state of the pandemic is, and to envision how the number of infections is going to evolve over the next days. However, as in many other situations involving compulsory registration of sensitive data, cases are reported with delay to a central register, with this delay deferring an up-to-date view of the state of things. We provide a stable tool for monitoring current infection levels as well as predicting infection numbers in the immediate future at the regional level. We accomplish this through nowcasting of cases that have not yet been reported as well as through predictions of future infections. We apply our model to German data, for which our focus lies in predicting and explain infectious behavior by district.

Suggested Citation

  • Giacomo De Nicola & Marc Schneble & Göran Kauermann & Ursula Berger, 2022. "Regional now- and forecasting for data reported with delay: toward surveillance of COVID-19 infections," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(3), pages 407-426, September.
    • Handle: RePEc:spr:alstar:v:106:y:2022:i:3:d:10.1007_s10182-021-00433-5
    DOI: 10.1007/s10182-021-00433-5
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    References listed on IDEAS

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    1. Jennifer Beam Dowd & Liliana Andriano & David M. Brazel & Valentina Rotondi & Per Block & Xuejie Ding & Yan Liu & Melinda C. Mills, 2020. "Demographic science aids in understanding the spread and fatality rates of COVID-19," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(18), pages 9696-9698, May.
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    Cited by:

    1. Ursula Berger & Göran Kauermann & Helmut Küchenhoff, 2022. "Discussion on On the role of data, statistics and decisions in a pandemic," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(3), pages 387-390, September.
    2. Gert G. Wagner, 2022. "Grenzen und Fortschritte indikatorengestützter Politik am Beispiel der Corona-Pandemie [Limitations and progress of indicator-based policy – The case of the Corona pandemic]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(3), pages 171-187, December.

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