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Adoption and impact of non-pharmaceutical interventions for COVID-19.
Imai, Natsuko; Gaythorpe, Katy A M; Abbott, Sam; Bhatia, Sangeeta; van Elsland, Sabine; Prem, Kiesha; Liu, Yang; Ferguson, Neil M.
  • Imai N; MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
  • Gaythorpe KAM; MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
  • Abbott S; Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.
  • Bhatia S; MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
  • van Elsland S; MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
  • Prem K; Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.
  • Liu Y; Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.
  • Ferguson NM; MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
Wellcome Open Res ; 5: 59, 2020.
Article in English | MEDLINE | ID: covidwho-1068024
ABSTRACT

Background:

Several non-pharmaceutical interventions (NPIs) have been implemented across the world to control the coronavirus disease (COVID-19) pandemic. Social distancing (SD) interventions applied so far have included school closures, remote working and quarantine. These measures have been shown to have large impacts on pandemic influenza transmission. However, there has been comparatively little examination of such measures for COVID-19.

Methods:

We examined the existing literature, and collated data, on implementation of NPIs to examine their effects on the COVID-19 pandemic so far. Data on NPIs were collected from official government websites as well as from media sources.

Results:

Measures such as travel restrictions have been implemented in multiple countries and appears to have slowed the geographic spread of COVID-19 and reduced initial case numbers. We find that, due to the relatively sparse information on the differences with and without interventions, it is difficult to quantitatively assess the efficacy of many interventions. Similarly, whilst the comparison to other pandemic diseases such as influenza can be helpful, there are key differences that could affect the efficacy of similar NPIs.

Conclusions:

The timely implementation of control measures is key to their success and must strike a balance between early enough application to reduce the peak of the epidemic and ensuring that they can be feasibly maintained for an appropriate duration. Such measures can have large societal impacts and they need to be appropriately justified to the population. As the pandemic of COVID-19 progresses, quantifying the impact of interventions will be a vital consideration for the appropriate use of mitigation strategies.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study Language: English Journal: Wellcome Open Res Year: 2020 Document Type: Article Affiliation country: Wellcomeopenres.15808.1

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study Language: English Journal: Wellcome Open Res Year: 2020 Document Type: Article Affiliation country: Wellcomeopenres.15808.1