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IntroductionRecent efforts including the U.S. Department of Education’sRaise the Bar: STEM Excellence for All Students, designed to strengthen Science, Technology, Engineering and Mathematics (STEM) education, typify the development of effective outreach programs implemented in high school settings to increase STEM achievement and literacy and to promote future careers in STEM. Specifically, artificial intelligence (AI) and machine learning (ML) are topics of great importance and interest but are often reserved for higher-level education. Introductions of complex subjects in high school promotes student efficacy, enthusiasm, and skill-development for STEM careers. Establishing strong partnerships between universities and high schools is mutually beneficial for the professional development of students, teachers, and professors. In this paper, we detail immersive outreach efforts and their effectiveness in a high school setting. MethodsFrom Spring 2021 to Spring 2024, we conducted eight data-science and analysis-coding style workshops along with two data science units, with 302 students participating in the data science workshops and 82 students in the data science units. All students who participated in the data science lessons completed a comprehensive final project. Surveys measuring knowledge and appeal to data science and coding were conducted both retrospectively and prospectively, before and after each workshop and the data science units. A 1 year follow up survey was conducted for students in the 2023 data science lessons (n= 23). ResultsOverall, average student interest significantly increased from 2.72 ± 1.08/5.0 (n= 205) to 3.15 ± 1.18/5.0 (n= 181,p= 0.001) during the data science workshops, while 70% of students expressed desire to continue with coding. Interest modestly increased in the data science lessons from 3.15 ± 0.65/4.0 to 3.17 ± 0.77/4.0 (n= 82,p= 0.8571), while knowledge significantly increased from 64.16% to 88.5% (% correct out of six questions) in the 2023 data science lessons and from 52.62% to 60.79% (% correct out of 29 questions) in the 2024 data science lessons. DiscussionIncreasing STEM exposure through outreach programs and a modified curriculum can positively alter students’ career trajectory and prepare them for the evolving technologically advanced world and the careers within it. 

BackgroundCOVID-19 is constantly evolving, and highly populated communities consist of many different characteristics that may contribute to COVID-19 health outcomes. Therefore, we aimed to (1) quantify the relationships between county characteristics and severe and non-severe county-level health outcomes related to COVID-19. We also aimed to (2) compare these relationships across time periods where the Delta (B.1.617.2) and Omicron (B.1.1.529 and BA.1.1) variants were dominant in the U.S. MethodsWe used multiple regression to measure the strength of relationships between healthcare outcomes and county characteristics in the 50 most populous U.S. counties. ResultsWe found many different significant predictors including the proportion of a population vaccinated, median household income, population density, and the proportion of residents aged 65+, but mainly found that socioeconomic factors and the proportion of a population vaccinated play a large role in the dynamics of the spread and severity of COVID-19 in communities with high populations. DiscussionThe present study shines light on the associations between public health outcomes and county characteristics and how these relationships change throughout Delta and Omicron’s dominance. It is important to understand factors underlying COVID-19 health outcomes to prepare for future health crises.