COVID-19 has generated a huge mortality toll in the United States, with a disproportionate number of deaths occurring among the Black and Latino populations. Measures of life expectancy quantify these disparities in an easily interpretable way. We project that COVID-19 will reduce US life expectancy in 2020 by 1.13 y. Estimated reductions for the Black and Latino populations are 3 to 4 times that for Whites. Consequently, COVID-19 is expected to reverse over 10 y of progress made in closing the Black−White gap in life expectancy and reduce the previous Latino mortality advantage by over 70%. Some reduction in life expectancy may persist beyond 2020 because of continued COVID-19 mortality and long-term health, social, and economic impacts of the pandemic. All study data are included in [Dataset S1]. : https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2014746118/-/DCSupplemental
Wearing face masks and maintaining social distance are familiar to many people around the world during the ongoing SARS-CoV-2 pandemic. Evidence suggests that these are effective ways to reduce the risk of SARS-CoV-2 infection. However, it is not clear how exactly the risk of infection is affected by wearing a mask during close personal encounters or by social distancing without a mask. Our results show that face masks significantly reduce the risk of SARS-CoV-2 infection compared to social distancing. We find a very low risk of infection when everyone wears a face mask, even if it doesn’t fit perfectly on the face. Previously published data were used for this work (<https://aerosol.ds.mpg.de/>). All other study data are included in the article and/or [ SI Appendix ]. : https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2110117118/-/DCSupplemental
Shared by 261, including Uta Frith, Paul Graham, Paul Kedrosky
The role of social media in political discourse has been the topic of intense scholarly and public debate. Politicians and commentators from all sides allege that Twitter’s algorithms amplify their opponents’ voices, or silence theirs. Policy makers and researchers have thus called for increased transparency on how algorithms influence exposure to political content on the platform. Based on a massive-scale experiment involving millions of Twitter users, a fine-grained analysis of political parties in seven countries, and 6.2 million news articles shared in the United States, this study carries out the most comprehensive audit of an algorithmic recommender system and its effects on political content. Results unveil that the political right enjoys higher amplification compared to the political left. Aggregated study data are available upon request from the corresponding authors following the protocol outlined in [ SI Appendix, section 3 ]. : https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2025334119/-/DCSupplemental
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