Predictive modeling of COVID-19 case growth highlights evolving racial and ethnic risk factors in Tennessee and Georgia.

Autor:	Gray JD; Decode Health, Inc. and IQuity Labs, Inc, Nashville, Tennessee, USA., Harris CR; Decode Health, Inc. and IQuity Labs, Inc, Nashville, Tennessee, USA.; Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA., Wylezinski LS; Decode Health, Inc. and IQuity Labs, Inc, Nashville, Tennessee, USA.; Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA., Spurlock Iii CF; Decode Health, Inc. and IQuity Labs, Inc, Nashville, Tennessee, USA chase.spurlock@vanderbilt.edu.; Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
Jazyk:	angličtina
Zdroj:	BMJ health & care informatics [BMJ Health Care Inform] 2021 Aug; Vol. 28 (1).
DOI:	10.1136/bmjhci-2021-100349
Abstrakt:	Introduction: The SARS-CoV-2 (COVID-19) pandemic has exposed the need to understand the risk drivers that contribute to uneven morbidity and mortality in US communities. Addressing the community-specific social determinants of health (SDOH) that correlate with spread of SARS-CoV-2 provides an opportunity for targeted public health intervention to promote greater resilience to viral respiratory infections. Methods: Our work combined publicly available COVID-19 statistics with county-level SDOH information. Machine learning models were trained to predict COVID-19 case growth and understand the social, physical and environmental risk factors associated with higher rates of SARS-CoV-2 infection in Tennessee and Georgia counties. Model accuracy was assessed comparing predicted case counts to actual positive case counts in each county. Results: The predictive models achieved a mean R 2 of 0.998 in both states with accuracy above 90% for all time points examined. Using these models, we tracked the importance of SDOH data features over time to uncover the specific racial demographic characteristics strongly associated with COVID-19 incidence in Tennessee and Georgia counties. Our results point to dynamic racial trends in both states over time and varying, localized patterns of risk among counties within the same state. For example, we find that African American and Asian racial demographics present comparable, and contrasting, patterns of risk depending on locality. Conclusion: The dichotomy of demographic trends presented here emphasizes the importance of understanding the unique factors that influence COVID-19 incidence. Identifying these specific risk factors tied to COVID-19 case growth can help stakeholders target regional interventions to mitigate the burden of future outbreaks. Competing Interests: Competing interests: JDG, LSW and CFS are shareholders in IQuity Labs, Inc. (Nashville, Tennessee) and Decode Health, Inc. (Nashville, Tennessee). IQuity Labs, Inc. develops blood-based RNA tools to aid in the diagnosis and treatment of human disease. Decode Health, Inc. develops artificial intelligence approaches to predict chronic and infectious disease risk in patient populations. (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
Databáze:	MEDLINE
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