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10.1038/s41562-020-01009-0

http://scihub22266oqcxt.onion/10.1038/s41562-020-01009-0
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33199859!ä!33199859

suck abstract from ncbi


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pmid33199859      Nat+Hum+Behav 2020 ; 4 (12): 1303-1312
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  • Ranking the effectiveness of worldwide COVID-19 government interventions #MMPMID33199859
  • Haug N; Geyrhofer L; Londei A; Dervic E; Desvars-Larrive A; Loreto V; Pinior B; Thurner S; Klimek P
  • Nat Hum Behav 2020[Dec]; 4 (12): 1303-1312 PMID33199859show ga
  • Assessing the effectiveness of non-pharmaceutical interventions (NPIs) to mitigate the spread of SARS-CoV-2 is critical to inform future preparedness response plans. Here we quantify the impact of 6,068 hierarchically coded NPIs implemented in 79 territories on the effective reproduction number, R(t), of COVID-19. We propose a modelling approach that combines four computational techniques merging statistical, inference and artificial intelligence tools. We validate our findings with two external datasets recording 42,151 additional NPIs from 226 countries. Our results indicate that a suitable combination of NPIs is necessary to curb the spread of the virus. Less disruptive and costly NPIs can be as effective as more intrusive, drastic, ones (for example, a national lockdown). Using country-specific 'what-if' scenarios, we assess how the effectiveness of NPIs depends on the local context such as timing of their adoption, opening the way for forecasting the effectiveness of future interventions.
  • |*Government[MESH]
  • |Artificial Intelligence[MESH]
  • |Basic Reproduction Number/*statistics & numerical data[MESH]
  • |COVID-19/*prevention & control[MESH]
  • |Datasets as Topic[MESH]
  • |Global Health/*statistics & numerical data[MESH]
  • |Humans[MESH]


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