The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions
Open accessAbstract
Keeping the lid on infection spread: 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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Cited by
Ranking the effectiveness of worldwide COVID-19 government interventions
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Differential effects of intervention timing on COVID-19 spread in the United States
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Scientific consensus on the COVID-19 pandemic: we need to act now
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Metadata
- Authors
- Dehning, J., Zierenberg, J., Spitzner, F. P., Wibral, M., Neto, J. P., Wilczek, M., & Priesemann, V.
- Venue
- Science
- Published
- 2020-07-10
- Volume
- 369
- Issue
- 6500
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W3023190093
- Cited by
- 819
- References
- 59
- FWCI
- 26.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Dehning, J., Zierenberg, J., Spitzner, F. P., Wibral, M., Neto, J. P., Wilczek, M., & Priesemann, V. (2020). Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions. Science. https://doi.org/10.1126/science.abb9789
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.