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Tang CY, Gao C, Prasai K, Li T, Dash S, McElroy JA, Hang J, Wan XF. Prediction models for COVID-19 disease outcomes. Emerg Microbes Infect 2024; 13:2361791. [PMID: 38828796 PMCID: PMC11182058 DOI: 10.1080/22221751.2024.2361791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Accepted: 05/26/2024] [Indexed: 06/05/2024]
Abstract
SARS-CoV-2 has caused over 6.9 million deaths and continues to produce lasting health consequences. COVID-19 manifests broadly from no symptoms to death. In a retrospective cross-sectional study, we developed personalized risk assessment models that predict clinical outcomes for individuals with COVID-19 and inform targeted interventions. We sequenced viruses from SARS-CoV-2-positive nasopharyngeal swab samples between July 2020 and July 2022 from 4450 individuals in Missouri and retrieved associated disease courses, clinical history, and urban-rural classification. We integrated this data to develop machine learning-based predictive models to predict hospitalization, ICU admission, and long COVID.The mean age was 38.3 years (standard deviation = 21.4) with 55.2% (N = 2453) females and 44.8% (N = 1994) males (not reported, N = 4). Our analyses revealed a comprehensive set of predictors for each outcome, encompassing human, environment, and virus genome-wide genetic markers. Immunosuppression, cardiovascular disease, older age, cardiac, gastrointestinal, and constitutional symptoms, rural residence, and specific amino acid substitutions were associated with hospitalization. ICU admission was associated with acute respiratory distress syndrome, ventilation, bacterial co-infection, rural residence, and non-wild type SARS-CoV-2 variants. Finally, long COVID was associated with hospital admission, ventilation, and female sex.Overall, we developed risk assessment models that offer the capability to identify patients with COVID-19 necessitating enhanced monitoring or early interventions. Of importance, we demonstrate the value of including key elements of virus, host, and environmental factors to predict patient outcomes, serving as a valuable platform in the field of personalized medicine with the potential for adaptation to other infectious diseases.
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Affiliation(s)
- Cynthia Y. Tang
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, USA
- Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
| | - Cheng Gao
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, USA
- Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, Missouri, USA
| | - Kritika Prasai
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, USA
- Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, Missouri, USA
| | - Tao Li
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
| | - Shreya Dash
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, USA
- Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
| | - Jane A. McElroy
- Family and Community Medicine, University of Missouri, Columbia, Missouri, USA
| | - Jun Hang
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
| | - Xiu-Feng Wan
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, USA
- Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, Missouri, USA
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Tang CY, Li T, Haynes TA, McElroy JA, Ritter D, Hammer RD, Sampson C, Webby R, Hang J, Wan XF. Rural populations facilitated early SARS-CoV-2 evolution and transmission in Missouri, USA. NPJ VIRUSES 2023; 1:7. [PMID: 38186942 PMCID: PMC10769004 DOI: 10.1038/s44298-023-00005-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/20/2023] [Indexed: 01/09/2024]
Abstract
In the United States, rural populations comprise 60 million individuals and suffered from high COVID-19 disease burdens. Despite this, surveillance efforts are biased toward urban centers. Consequently, how rurally circulating SARS-CoV-2 viruses contribute toward emerging variants remains poorly understood. In this study, we aim to investigate the role of rural communities in the evolution and transmission of SARS-CoV-2 during the early pandemic. We collected 544 urban and 435 rural COVID-19-positive respiratory specimens from an overall vaccine-naïve population in Southwest Missouri between July and December 2020. Genomic analyses revealed 53 SARS-CoV-2 Pango lineages in our study samples, with 14 of these lineages identified only in rural samples. Phylodynamic analyses showed that frequent bi-directional diffusions occurred between rural and urban communities in Southwest Missouri, and that four out of seven Missouri rural-origin lineages spread globally. Further analyses revealed that the nucleocapsid protein (N):R203K/G204R paired substitutions, which were detected disproportionately across multiple Pango lineages, were more associated with urban than rural sequences. Positive selection was detected at N:204 among rural samples but was not evident in urban samples, suggesting that viruses may encounter distinct selection pressures in rural versus urban communities. This study demonstrates that rural communities may be a crucial source of SARS-CoV-2 evolution and transmission, highlighting the need to expand surveillance and resources to rural populations for COVID-19 mitigation.
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Affiliation(s)
- Cynthia Y. Tang
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, MO, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
- These authors contributed equally: Cynthia Y. Tang, Tao Li
| | - Tao Li
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA
- These authors contributed equally: Cynthia Y. Tang, Tao Li
| | - Tricia A. Haynes
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, MO, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
| | - Jane A. McElroy
- Family and Community Medicine, University of Missouriś, Columbia, MO, USA
| | - Detlef Ritter
- Anatomic Pathology & Clinical Pathology, University of Missouri, Columbia, MO, USA
| | - Richard D. Hammer
- Anatomic Pathology & Clinical Pathology, University of Missouri, Columbia, MO, USA
| | | | - Richard Webby
- Infectious Diseases, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Jun Hang
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Xiu-Feng Wan
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, MO, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA
- Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, MO, USA
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Murphy C, Lim WW, Mills C, Wong JY, Chen D, Xie Y, Li M, Gould S, Xin H, Cheung JK, Bhatt S, Cowling BJ, Donnelly CA. Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20230132. [PMID: 37611629 PMCID: PMC10446910 DOI: 10.1098/rsta.2023.0132] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 05/23/2023] [Indexed: 08/25/2023]
Abstract
Social distancing measures (SDMs) are community-level interventions that aim to reduce person-to-person contacts in the community. SDMs were a major part of the responses first to contain, then to mitigate, the spread of SARS-CoV-2 in the community. Common SDMs included limiting the size of gatherings, closing schools and/or workplaces, implementing work-from-home arrangements, or more stringent restrictions such as lockdowns. This systematic review summarized the evidence for the effectiveness of nine SDMs. Almost all of the studies included were observational in nature, which meant that there were intrinsic risks of bias that could have been avoided were conditions randomly assigned to study participants. There were no instances where only one form of SDM had been in place in a particular setting during the study period, making it challenging to estimate the separate effect of each intervention. The more stringent SDMs such as stay-at-home orders, restrictions on mass gatherings and closures were estimated to be most effective at reducing SARS-CoV-2 transmission. Most studies included in this review suggested that combinations of SDMs successfully slowed or even stopped SARS-CoV-2 transmission in the community. However, individual effects and optimal combinations of interventions, as well as the optimal timing for particular measures, require further investigation. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.
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Affiliation(s)
- Caitriona Murphy
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Wey Wen Lim
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Cathal Mills
- Department of Statistics, University of Oxford, Oxford, UK
| | - Jessica Y. Wong
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Dongxuan Chen
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Yanmy Xie
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Mingwei Li
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Susan Gould
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
- Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Hualei Xin
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Justin K. Cheung
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Samir Bhatt
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Kobenhavn, Denmark
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Benjamin J. Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Christl A. Donnelly
- Department of Statistics, University of Oxford, Oxford, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
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Dahal S, Mamelund SE, Luo R, Sattenspiel L, Self-Brown S, Chowell G. Investigating COVID-19 transmission and mortality differences between indigenous and non-indigenous populations in Mexico. Int J Infect Dis 2022; 122:910-920. [PMID: 35905949 PMCID: PMC9357430 DOI: 10.1016/j.ijid.2022.07.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/19/2022] [Accepted: 07/21/2022] [Indexed: 01/25/2023] Open
Abstract
OBJECTIVES Indigenous populations have been disproportionately affected during pandemics. We investigated COVID-19 mortality estimates among indigenous and non-indigenous populations at national and sub-national levels in Mexico. METHODS We obtained data from the Ministry of Health, Mexico, on 2,173,036 laboratory-confirmed RT-PCR positive COVID-19 cases and 238,803 deaths. We estimated mortality per 1000 person-weeks, mortality rate ratio (RR) among indigenous vs. non-indigenous groups, and hazard ratio (HR) for COVID-19 deaths across four waves of the pandemic, from February 2020 to March 2022. We also assessed differences in the reproduction number (Rt). RESULTS The mortality rate among indigenous populations of Mexico was 68% higher than that of non-indigenous groups. Out of 32 federal entities, 23 exhibited higher mortality rates among indigenous groups (P < 0.05 in 13 entities). The fourth wave showed the highest RR (2.40). The crude HR was 1.67 (95% CI: 1.62, 1.72), which decreased to 1.08 (95% CI: 1.04, 1.11) after controlling for other covariates. During the intense fourth wave, the Rt among the two groups was comparable. CONCLUSION Indigenous status is a significant risk factor for COVID-19 mortality in Mexico. Our findings may reflect disparities in non-pharmaceutical (e.g., handwashing and using facemasks), and COVID-19 vaccination interventions among indigenous and non-indigenous populations in Mexico.
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Affiliation(s)
- Sushma Dahal
- School of Public Health, Georgia State University, Atlanta, USA,Correspondence to: Sushma Dahal, School of Public Health, Georgia State University, P.O. Box 3995, Atlanta, Georgia, 30302-3995
| | - Svenn-Erik Mamelund
- Centre for Research on Pandemics & Society, Oslo Metropolitan University, Oslo, Norway
| | - Ruiyan Luo
- School of Public Health, Georgia State University, Atlanta, USA
| | - Lisa Sattenspiel
- College of Arts and Science, University of Missouri, Columbia, USA
| | | | - Gerardo Chowell
- School of Public Health, Georgia State University, Atlanta, USA
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