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The Omicron wave was the largest wave of COVID-19 pandemic to date, more than doubling any other in terms of cases and hospitalizations in the United States. In this paper, we present a large-scale agent-based model of policy interventions that could have been implemented to mitigate the Omicron wave. Our model takes into account the behaviors of individuals and their interactions with one another within a nationally representative population, as well as the efficacy of various interventions such as social distancing, mask wearing, testing, tracing, and vaccination. We use the model to simulate the impact of different policy scenarios and evaluate their potential effectiveness in controlling the spread of the virus. Our results suggest the Omicron wave could have been substantially curtailed via a combination of interventions comparable in effectiveness to extreme and unpopular singular measures such as widespread closure of schools and workplaces, and highlight the importance of early and decisive action.

There is currently a great deal of interest in the potential of college subsidy programs to increase equitable access to higher education and to reduce the financial burden on college attendees. In this article, the authors turn to a computational modeling approach (“Agent-based modeling”) that allows them to explicitly simulate individual college and student decisions over time. Their model is grounded in a strong body of evidence about how students and colleges make application, admissions, and enrollment decisions.

There is currently a great deal of interest in the potential of college subsidy programs to increase equitable access to higher education and to reduce the financial burden on college attendees. In this article, the authors turn to a computational modeling approach (“Agent-based modeling”) that allows them to explicitly simulate individual college and student decisions over time. Their model is grounded in a strong body of evidence about how students and colleges make application, admissions, and enrollment decisions.

BACKGROUND: Involving groups of community stakeholders (e.g., steering committees) to lead community-wide health interventions appears to support multiple outcomes ranging from policy and systems change to individual biology. While numerous tools are available to measure stakeholder characteristics, many lack detail on reliability and validity, are not context specific, and may not be sensitive enough to capture change over time. This study describes the development and reliability of a novel survey to measure Stakeholder-driven Community Diffusion via assessment of stakeholders' social networks, knowledge, and engagement about childhood obesity prevention. METHODS: This study was completed in three phases. Phase 1 included conceptualization and online survey development through literature reviews and expert input. Phase 2 included a retrospective study with stakeholders from two completed whole-of-community interventions. Between May-October 2015, 21 stakeholders from the Shape Up Somerville and Romp & Chomp interventions recalled their social networks, knowledge, and engagement pre-post intervention. We also assessed one-week test-retest reliability of knowledge and engagement survey modules among Shape Up Somerville respondents. Phase 3 included survey modifications and a second prospective reliability assessment. Test-retest reliability was assessed in May 2016 among 13 stakeholders involved in ongoing interventions in Victoria, Australia. RESULTS: In Phase 1, we developed a survey with 7, 20 and 50 items for the social networks, knowledge, and engagement survey modules, respectively. In the Phase 2 retrospective study, Shape Up Somerville and Romp & Chomp networks included 99 and 54 individuals. Pre-post Shape Up Somerville and Romp & Chomp mean knowledge scores increased by 3.5 points (95% CI: 0.35-6.72) and (- 0.42-7.42). Engagement scores did not change significantly (Shape Up Somerville: 1.1 points (- 0.55-2

This paper simulates a system of socioeconomic status (SES)−based affirmative action in college admissions and examines the extent to which it can produce racial diversity in selective colleges. Using simulation models, we investigate the potential relative effects of race- and/or SES-based affirmative action policies, alongside targeted, race-based recruitment, on the racial and socioeconomic distribution of students in colleges. These simulations suggest three important patterns: (1) neither SES-based affirmative action nor race recruiting policies on their own can reproduce levels of racial diversity achieved by race-based affirmative action; however, SES-based affirmative action in combination with targeted recruitment, although likely expensive, shows the potential to yield racial diversity levels comparable to race-based affirmative action; (b) the use of affirmative action policies by some colleges reduces the diversity of similar-quality colleges that do not have such policies; (c) overall, the combination of SES-based affirmative action and race recruiting results in slightly fewer Black and Hispanic students that are academically overmatched than under race-based affirmative action, but the schools that use the combination policy also see an overall reduction in the academic achievement of the students they enroll.

This paper simulates a system of socioeconomic status (SES)−based affirmative action in college admissions and examines the extent to which it can produce racial diversity in selective colleges. Using simulation models, we investigate the potential relative effects of race- and/or SES-based affirmative action policies, alongside targeted, race-based recruitment, on the racial and socioeconomic distribution of students in colleges. These simulations suggest three important patterns: (1) neither SES-based affirmative action nor race recruiting policies on their own can reproduce levels of racial diversity achieved by race-based affirmative action; however, SES-based affirmative action in combination with targeted recruitment, although likely expensive, shows the potential to yield racial diversity levels comparable to race-based affirmative action; (b) the use of affirmative action policies by some colleges reduces the diversity of similar-quality colleges that do not have such policies; (c) overall, the combination of SES-based affirmative action and race recruiting results in slightly fewer Black and Hispanic students that are academically overmatched than under race-based affirmative action, but the schools that use the combination policy also see an overall reduction in the academic achievement of the students they enroll.