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Background and Objectives: Although at-home coronavirus disease-2019 (COVID-19) testing offers several benefits in a relatively cost-effective and less risky manner, evidence suggests that at-home COVID-19 test kits have a high rate of false negatives. One way to improve the accuracy and acceptance of COVID-19 screening is to combine existing at-home physical test kits with an easily accessible, electronic, self-diagnostic tool. The objective of the current study was to test the acceptability and usability of an artificial intelligence (AI)-enabled COVID-19 testing tool that combines a web-based symptom diagnostic screening survey and a physical at-home test kit to test differences across adults from varying races, ages, genders, educational, and income levels in the United States. Methods: A total of 822 people from Richmond, Virginia, were included in the study. Data were collected from employees and patients of Virginia Commonwealth University Health Center as well as the surrounding community in June through October 2021. Data were weighted to reflect the demographic distribution of patients in United States. Descriptive statistics and repeated independent t tests were run to evaluate the differences in the acceptability and usability of an AI-enabled COVID-19 testing tool. Results: Across all participants, there was a reasonable degree of acceptability and usability of the AI-enabled COVID-19 testing tool that included a physical test kit and symptom screening website. The AI-enabled COVID-19 testing tool demonstrated overall good acceptability and usability across race, age, gender, and educational background. Notably, participants preferred both components of the AI-enabled COVID-19 testing tool to the in-clinic testing. Conclusion: Overall, these findings suggest that our AI-enabled COVID-19 testing approach has great potential to improve the quality of remote COVID testing at low cost and high accessibility for diverse demographic populations in the United States. [ABSTRACT FROM AUTHOR]

Mycobacterium bovis isolates carry restricted allelic variation yet exhibit a range of disease phenotypes and host preferences. Conventional genotyping methods target small hypervariable regions of the M. bovis genome and provide anonymous biallelic information that is insufficient to develop phylogeny. To resolve phylogeny and establish trait-allele associations, we interrogated 75 M. bovis and 61 M. tuberculosis genomes for single nucleotide polymorphisms (SNPs), using iPLEX MassArray (Sequenom Inc., CA) technology. We indexed nucleotide variations in 306 genic and 44 intergenic loci among isolates derived from outbreaks in the United States from 1991 to 2010 and isolated from a variety of mammalian hosts. Two hundred six variant SNPs classified the 136 isolates and 4 previously sequenced strains (AF2122/97, BCG Pasteur, H37Rv, and CDC1551) into 5 major "SNP cluster groups." M. bovis isolates clustered into three major lineages based on 118 variant SNPs, while 84 SNPs differentiated the M. bovis BCG lineage from the virulent isolates. Forty-nine of the 51 human M. tuberculosis isolates were identical at all 350 loci studied. Thus, SNP-based analyses resolved the genotypic differences within M. bovis strains and differentiated these strains from M. tuberculosis strains representing diversity in time and space, providing population genetic frameworks that may aid in identifying factors responsible for the wide host range and disease phenotypes of M. bovis.