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We conducted a rapid, mixed-methods assessment to understand how COVID-19 affected Latinx sexual minority men (LSMM) and transgender women (LTGW). Using a computer-assisted telephone interviewing software, one interviewer called 52 participants (randomly sampled from a larger HIV prevention pilot study aiming to increase HIV knowledge and testing frequency; n = 36 LSMM and n = 16 LTGW) between 04/27/20-05/18/20. We quantified core domains using the Epidemic-Pandemic Impacts Inventory scale and provided important context through open-ended qualitative questions assessing: 1) COVID-19 infection history and experiences with quarantine; 2) Health and healthcare access; 3) Employment and economic impact of COVID-19. Participants reported increases in physical conflict or verbal arguments with a partner (13.5%) or other adult(s) (19.2%) due to stressors associated with the safer-at-home order. Participants also reported increased alcohol consumption (23.1%), problems with sleep (67.3%) and mental health (78.4%). Further, disruptions in access to Pre-Exposure Prophylaxis or PrEP–a daily pill to prevent HIV–occurred (33.3% of 18 participants who reported being on PrEP). Many said they received less medical attention than usual (34.6%), and LTGW reported delays in critical gender-affirming hormones/procedures. Half of the participants lost their jobs (50.0%); many undocumented participants relayed additional financial concerns because they did not qualify for financial assistance. Though no COVID-19 infections were noted, COVID-19 dramatically impacted other aspects of health and overall wellbeing of LSMM and LTGW. Public health responses should address the stressors faced by LSMM and LTGW during the COVID-19 pandemic and the impact on wellbeing.

Historic Sitka AL, a town of 9,000 residents, is located on an island southwest of the state capital of Juneau, in the heart of the Tongass National Forest. The Pacific Ocean borders one side of the town, with steep mountains rising above the other. The community has long recognized the threat of tsunamis, but heavy rains in recent years have now caused landslides to be a concern as well. Here, Busch et al provide story of how Sitka came to build its own innovative landslide warning system, as members of the team working to develop it.

Several behavioral, social, and public health interventions, such as suicide/HIV prevention or community preparedness against natural disasters, leverage social network information to maximize outreach. Algorithmic influence maximization techniques have been proposed to aid with the choice of \"peer leaders\" or \"influencers\" in such interventions. Yet, traditional algorithms for influence maximization have not been designed with these interventions in mind. As a result, they may disproportionately exclude minority communities from the benefits of the intervention. This has motivated research on fair influence maximization. Existing techniques come with two major drawbacks. First, they require committing to a single fairness measure. Second, these measures are typically imposed as strict constraints leading to undesirable properties such as wastage of resources. To address these shortcomings, we provide a principled characterization of the properties that a fair influence maximization algorithm should satisfy. In particular, we propose a framework based on social welfare theory, wherein the cardinal utilities derived by each community are aggregated using the isoelastic social welfare functions. Under this framework, the trade-off between fairness and efficiency can be controlled by a single inequality aversion design parameter. We then show under what circumstances our proposed principles can be satisfied by a welfare function. The resulting optimization problem is monotone and submodular and can be solved efficiently with optimality guarantees. Our framework encompasses as special cases leximin and proportional fairness. Extensive experiments on synthetic and real world datasets including a case study on landslide risk management demonstrate the efficacy of the proposed framework.