Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions

  1. Jonas Dehning
  2. Johannes Zierenberg
  3. F. Paul Spitzner
  4. Michael Wibral
  5. Joao Pinheiro Neto
  6. Michael Wilczek
  7. Viola Priesemann

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Abstract

From February to April 2020, many countries introduced variations on social distancing measures to slow the ravages of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Publicly available data show that Germany has been particularly successful in minimizing death rates. Dehning et al. quantified three governmental interventions introduced to control the outbreak. The authors predicted that the third governmental intervention—a strict contact ban since 22 March—switched incidence from growth to decay. They emphasize that relaxation of controls must be done carefully, not only because there is a 2-week lag between a measure being enacted and the effect on case reports but also because the three measures used in Germany only just kept virus spread below the growth threshold.

Science , this issue p. eabb9789

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  1. Version published to 10.1126/science.abb9789
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    SciScore for 10.1101/2020.04.02.20050922: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

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    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Our implementation relies on the python package PyMC3 [42] with NUTS (No-U-Turn Sampling) [43] using multiple, independent Markov chains.
    python
    suggested: (IPython, RRID:SCR_001658)
    PyMC3
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  3. Version published to 10.1101/2020.04.02.20050922v3 on medRxiv
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