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"I like this piece by Kate Aronoff looking back on WPA "boondoggles" in the context of a suddenly much discussed job guarantee. A lot of people deserve congratulations for the suddenly much-discussedness of job guarantee proposals. People like William Darity, Darrick Hamilton, and Mark Paul, Pavlina Tcherneva, Randy Wray, and others have worked doggedly through years of winter to keep this (by no means new) idea alive while no major political faction in the United States was willing to give it the time of day. Now, all of a sudden, Democratic Party(ish) bigwigs including Kristen Gillibrand, Cory Booker, and Bernie Sanders are racing onto the bandwagon. Persistence pays (although perhaps not quite a living wage). Let's get this part out of the way. I'm for it, if it's well implemented. What about a UBI? I'm for that too, if it's well implemented. Do we need both? Well, they do complement each other: Pairing a job guarantee with a UBI would mitigate the risk that the "guarantee" would transmogrify under political pressure into a punitive workfare program. Pairing a UBI with a job guarantee would mitigate the risk that we neglect the broader project of integrating one another into a vibrant society, that we let a check in the mail substitute for human engagement. If we could get both a UBI and a JG, that'd be great. (Of course, if we did get both, we'd want the numbers to be different than either as a standalone.)"

Numerical results show that school closure alone would have limited benefit in reducing the peak incidence (less than 10% reduction with 8-week school closure for regions in the early phase of the epidemic). When coupled with 25% adults teleworking, 8-week school closure would be enough to delay the peak by almost 2 months with an approximately 40% reduction of the case incidence at the peak. This is critical to reduce the burden on the healthcare system in the weeks of highest demand. Moderate overall reduction of the final attack rate (15%) would also be achieved. Results across regions are qualitatively similar, with differences

Actions taken to mitigate the threats of coronavirus disease 2019 (COVID-19) to human life and welfare have inadvertently resulted in a natural experiment offering unanticipated insight into how human behavior affects animal behavior (1). Worldwide, elective quarantine and stay-at-home orders have reduced use of public spaces and transportation networks, especially in cities. Anecdotal media accounts suggest that restricted movement has elicited rarely observed behaviors in commensal and peri-urban animals (2). Though not all of the reports have proven to be accurate (3), widely publicized observations like coyotes crossing the normally heavily trafficked Golden Gate Bridge in the San Francisco (SF) Bay Area (California, USA) have provoked widespread fascination with the prospect that animals rapidly move back into landscapes recently vacated by humans.

Goal: The purpose of this article is to introduce a new strategy to identify areas with high human density and mobility, which are at risk for spreading COVID-19. Crowded regions with actively moving people (called at-risk regions) are susceptible to spreading the disease, especially if they contain asymptomatic infected people together with healthy people. Methods: Our scheme identifies at-risk regions using existing cellular network functionalities-handover and cell (re)selection-used to maintain seamless coverage for mobile end-user equipment (UE). The frequency of handover and cell (re)selection events is highly reflective of the density of mobile people in the area because virtually everyone carries UEs. Results: These measurements, which are accumulated over very many UEs, allow us to identify the at-risk regions without compromising the privacy and anonymity of individuals. Conclusions: The inferred at-risk regions can then be subjected to further monitoring and risk mitigation.