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10.1016/j.scitotenv.2020.141401

http://scihub22266oqcxt.onion/10.1016/j.scitotenv.2020.141401
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32798876!7836816!32798876
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suck abstract from ncbi


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pmid32798876      Sci+Total+Environ 2020 ; 748 (ä): 141401
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  • Socioeconomic restrictions slowdown COVID-19 far more effectively than favorable weather-evidence from the satellite #MMPMID32798876
  • Shen X; Cai C; Li H
  • Sci Total Environ 2020[Dec]; 748 (ä): 141401 PMID32798876show ga
  • We model the impact of restricting socioeconomic activities (SA) on the transmission of COVID-19 globally. Countries initiate public health measures to slow virus transmission, ranging from stringent quarantines including city lockdown to simpler social distancing recommendations. We use satellite readings of NO(2), a pollutant emitted from socioeconomic activities, as a proxy for the level of social-economic restrictions, and discuss the implications under the influences of weather. We found that restricting SA has a leading contribution to lowering the reproductive number of COVID-19 by 18.3% +/- 3.5%, while air temperature, the highest contributor among all weather-related variables only contributes 8.0% +/- 2.6%. The reduction effects by restricting SA becomes more pronounced (23% +/- 3.0%) when we limited the data to China and developed countries where the indoor climate is mostly controlled. We computed the spared infectees by restricting SA until mid-April. Among all polities, China spared 40,964 (95% CI 31,463-51,470) infectees with 37,727 (95% CI, 28,925-47,488) in the Hubei Province, the epicenter of the outbreak. Europe spared 174,494 (95% CI 139,202-210,841) infectees, and the United States (US) spared 180,336 (95% CI 142,860-219,445) with 79,813 (95% CI 62,887-97,653) in New York State. In the same period, many regions except for China, Australia, and South Korea see a steep upward trend of spared infectees due to restricting SA with the US and Europe far steeper, signaling a greater risk of reopening the economy too soon. Latin America and Africa show less reduction of transmissivity through the region-by-time fixed effects than other regions, indicating a higher chance of becoming an epicenter soon.
  • |*Coronavirus Infections[MESH]
  • |*Pandemics[MESH]
  • |*Pneumonia, Viral[MESH]
  • |*Weather[MESH]
  • |Africa[MESH]
  • |Australia[MESH]
  • |Betacoronavirus[MESH]
  • |COVID-19[MESH]
  • |China[MESH]
  • |Europe[MESH]
  • |Humans[MESH]
  • |New York[MESH]
  • |Republic of Korea[MESH]
  • |SARS-CoV-2[MESH]
  • |Socioeconomic Factors[MESH]


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