905
Views
0
CrossRef citations to date
0
Altmetric
Emerging and Re-Emerging Coronaviruses

Prediction models for COVID-19 disease outcomes

, , , , , , & show all
Article: 2361791 | Received 11 Feb 2024, Accepted 26 May 2024, Published online: 14 Jun 2024

References

  • WHO Coronavirus (COVID-19) Dashboard. Updated June 28, 2023. [accessed July 4, 2023]. https://covid19.who.int/.
  • COVID Data Tracker. Updated June 24, 2023. [accessed 2023 July 4]. https://covid.cdc.gov/covid-data-tracker/#datatracker-home.
  • Hu B, Guo H, Zhou P, et al. Characteristics of SARS-CoV-2 and COVID-19. Nat Rev Microbiol. 2021;19(3):141–154. doi:10.1038/s41579-020-00459-7.
  • da Rosa Mesquita R, Francelino Silva Junior LC, Santos Santana FM, et al. Clinical manifestations of COVID-19 in the general population: systematic review. Wien Klin Wochenschr. 2021;133(7-8):377–382. doi:10.1007/s00508-020-01760-4.
  • Long COVID. Updated May 17, 2023. [accessed 2023 July 4]. https://www.cdc.gov/nchs/covid19/pulse/long-covid.htm.
  • Sullivan DJ, Gebo KA, Shoham S, et al. Early outpatient treatment for COVID-19 with convalescent plasma. N Engl J Med. 2022;386(18):1700–1711. doi:10.1056/NEJMoa2119657.
  • Yek C, Warner S, Wiltz JL, et al. Risk factors for severe COVID-19 outcomes Among persons aged ≥18 years Who completed a primary COVID-19 vaccination series — 465 health care facilities, United States, December 2020–October 2021. MMWR Morb Mortal Wkly Rep. 2022;71(1):19–25. doi:10.15585/mmwr.mm7101a4.
  • Shah MM, Joyce B, Plumb ID, et al. Paxlovid associated with decreased hospitalization rate among adults with COVID-19 — United States, April–September 2022. American Journal of Transplantation: Official Journal of the American Society of Transplantation and the American Society of Transplant Surgeons. 2023;23(1):150–155. doi:10.1016/j.ajt.2022.12.004.
  • Crotty BH, Dong Y, Laud P, et al. Hospitalization outcomes among patients with COVID-19 undergoing remote monitoring. JAMA Network Open. 2022;5(7):e2221050, doi:10.1001/jamanetworkopen.2022.21050.
  • Goyal DK, Mansab F, Iqbal A, et al. Early intervention likely improves mortality in COVID-19 infection. Clin Med (Lond). 2020;20(3):248–250. doi:10.7861/clinmed.2020-0214.
  • Russo A, Pisaturo M, Zollo V, et al. Obesity as a risk factor of severe outcome of COVID-19: a pair-matched 1:2 case-control study. J Clin Med. 2023;12(12):4055, doi:10.3390/jcm12124055.
  • Jacob J, Tesch F, Wende D, et al. Development of a risk score to identify patients at high risk for a severe course of COVID-19. Z Gesundh Wiss. 2024: 1–10. doi:10.1007/s10389-023-01884-7.
  • Ko JY, Danielson ML, Town M, et al. Risk factors for coronavirus disease 2019 (COVID-19)-associated hospitalization: COVID-19-associated hospitalization surveillance network and behavioral risk factor surveillance system. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America. 2021;72(11):e695–e703. doi:10.1093/cid/ciaa1419.
  • Rathod D, Kargirwar K, Patel M, et al. Risk factors associated with COVID-19 patients in India: a single center retrospective cohort study. J Assoc Physicians India. Jun 2023;71(6):11–12. doi:10.5005/japi-11001-0263.
  • Gina Turrini DKB, Chen L, Conmy AB, et al. Access to affordable care in rural America: Current trends and key challenges (Research ReportNo. HP-2021-16). July 2021. [accessed 2022 November 1]. https://aspe.hhs.gov/sites/default/files/2021-07/rural-health-rr.pdf.
  • Kadri SS, Simpson SQ. Potential implications of SARS-CoV-2 delta variant surges for rural areas and hospitals. JAMA. 2021;326(11):1003–1004. doi:10.1001/jama.2021.13941.
  • Cuadros DF, Moreno CM, Musuka G, et al. Association between vaccination coverage disparity and the dynamics of the COVID-19 delta and omicron waves in the US. Front Med (Lausanne). 2022;9:898101, doi:10.3389/fmed.2022.898101.
  • Cuadros DF, Branscum AJ, Mukandavire Z, et al. Dynamics of the COVID-19 epidemic in urban and rural areas in the United States. Ann Epidemiol. 2021;59:16–20. doi:10.1016/j.annepidem.2021.04.007.
  • Mueller JT, McConnell K, Burow PB, et al. Impacts of the COVID-19 pandemic on rural America. Proc Natl Acad Sci USA. 2021;118(1):2019378118, doi:10.1073/pnas.2019378118.
  • Huang Q, Jackson S, Derakhshan S, et al. Urban-rural differences in COVID-19 exposures and outcomes in the South: a preliminary analysis of South Carolina. PLoS One. 2021;16(2):e0246548, doi:10.1371/journal.pone.0246548.
  • Melvin SC, Wiggins C, Burse N, et al. The role of public health in COVID-19 emergency response efforts from a rural health perspective. Prev Chronic Dis. 2020: 17, doi:10.5888/pcd17.200256.
  • Dunne EM, Maxwell T, Dawson-Skuza C, et al. Investigation and public health response to a COVID-19 outbreak in a rural resort community—Blaine County, Idaho, 2020. PLoS One. 2021;16(4):e0250322, doi:10.1371/journal.pone.0250322.
  • Ramírez IJ, Lee J. COVID-19 emergence and social and health determinants in Colorado: a rapid spatial analysis. Int J Environ Res Public Health. 2020;17(11):3856, doi:10.3390/ijerph17113856.
  • Sylvia KO, Chigozie AO, Seoyon K, et al. SARS-CoV-2 transmission potential and rural-urban disease burden disparities across Alabama, Louisiana, and Mississippi, March 2020–May 2021. Ann Epidemiol. 2022;71:1–8. doi:10.1016/j.annepidem.2022.04.006.
  • SARS-CoV-2 variants of concern as of 4 August 2022. European Centre for Disease Prevention and Control. Updated August 5, 2022. [accessed 2022 August 9]. https://www.ecdc.europa.eu/en/covid-19/variants-concern.
  • Lin L, Liu Y, Tang X, et al. The disease severity and clinical outcomes of the SARS-CoV-2 variants of concern. Front Public Health. 2021;9:775224, doi:10.3389/fpubh.2021.775224.
  • Xiong Y, Ma Y, Ruan L, et al. Comparing different machine learning techniques for predicting COVID-19 severity. Infect Dis Poverty. 2022;11(1):19, doi:10.1186/s40249-022-00946-4.
  • Iaccarino G, Grassi G, Borghi C, et al. Age and multimorbidity predict death among COVID-19 patients. Hypertension. 2020;76(2):366–372. doi:10.1161/HYPERTENSIONAHA.120.15324.
  • Chieregato M, Frangiamore F, Morassi M, et al. A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data. Sci Rep. 2022;12(1):4329, doi:10.1038/s41598-022-07890-1.
  • Nagy Á, Ligeti B, Szebeni J, et al. COVIDOUTCOME-estimating COVID severity based on mutation signatures in the SARS-CoV-2 genome. Database: The Journal of Biological Databases and Curation. 2021: 2021, doi:10.1093/database/baab020.
  • Nagy Á, Pongor S, Győrffy B. Different mutations in SARS-CoV-2 associate with severe and mild outcome. Int J Antimicrob Agents. 2021;57(2):106272, doi:10.1016/j.ijantimicag.2020.106272.
  • Huang F, Chen L, Guo W, et al. Identifying COVID-19 severity-related SARS-CoV-2 mutation using a machine learning method. Life (Basel, Switzerland). 2022;12(6):806, doi:10.3390/life12060806.
  • Beguir K, Skwark MJ, Fu Y, et al. Early computational detection of potential high-risk SARS-CoV-2 variants. Comput Biol Med. 2023;155:106618, doi:10.1016/j.compbiomed.2023.106618.
  • Rinette B, Kierste M, Chris P, et al. Challenges in reported COVID-19 data: best practices and recommendations for future epidemics. BMJ Global Health. 2021;6(5):e005542, doi:10.1136/bmjgh-2021-005542.
  • Gozashti L, Corbett-Detig R. Shortcomings of SARS-CoV-2 genomic metadata. BMC Res Notes. 2021;14(1):189), doi:10.1186/s13104-021-05605-9.
  • RUCA Data [accessed June 12, 2023]. https://depts.washington.edu/uwruca/ruca-uses.php
  • Long COVID or Post-COVID Conditions. Updated December 16, 2022. [accessed July 4, 2023]. https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects/index.html.
  • Li T, Chung HK, Pireku PK, et al. Rapid high-throughput whole-genome sequencing of SARS-CoV-2 by using one-step reverse transcription-PCR amplification with an integrated microfluidic system and next-generation sequencing. 2021;59(5):e02784–20. doi:10.1128/JCM.02784-20
  • BBtools. Version 39.01. Joint Genome Institute [accessed 2023 February 21]. sourceforge.net/projects/bbmap/.
  • Shepard SS, Meno S, Bahl J, et al. Viral deep sequencing needs an adaptive approach: IRMA, the iterative refinement meta-assembler. BMC Genomics. 2016;17(1):708, doi:10.1186/s12864-016-3030-6.
  • Hadfield J, Megill C, Bell SM, et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics. 2018;34(23):4121–4123. doi:10.1093/bioinformatics/bty407.
  • Rural-Urban Commuting Area Codes. Updated March 22, 2023. [accessed 2023 July 4]. https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/.
  • Magazine N, Zhang T, Wu Y, et al. Mutations and evolution of the SARS-CoV-2 spike protein. Viruses. 2022;14(3):640, doi:10.3390/v14030640.
  • Cowley LE, Farewell DM, Maguire S, et al. Methodological standards for the development and evaluation of clinical prediction rules: a review of the literature. Diagnostic and Prognostic Research. 2019;3(1):16, doi:10.1186/s41512-019-0060-y.
  • Sterne JA, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ (Clinical Research ed). 2009;338:b2393, doi:10.1136/bmj.b2393.
  • Azur MJ, Stuart EA, Frangakis C, et al. Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res. 2011;20(1):40–49. doi:10.1002/mpr.329.
  • Mera-Gaona M, Neumann U, Vargas-Canas R, et al. Evaluating the impact of multivariate imputation by MICE in feature selection. PLoS One. 2021;16(7):e0254720, doi:10.1371/journal.pone.0254720.
  • Santos MS, Soares JP, Abreu PH, et al. Cross-validation for imbalanced datasets: avoiding overoptimistic and overfitting approaches [research frontier]. IEEE Comput Intell Mag. 2018;13(4):59–76. doi:10.1109/MCI.2018.2866730.
  • Batista GE, Prati RC, Monard MC. A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter. 2004;6(1):20–29. doi:10.1145/1007730.1007735.
  • He H, Garcia EA. Learning from imbalanced data. IEEE Trans Knowl Data Eng. 2009;21(9):1263–1284. doi:10.1109/TKDE.2008.239.
  • Chawla NV, Bowyer KW, Hall LO, et al. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–357. doi:10.1613/jair.953.
  • Tibshirani R. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society Series B (Methodological). 1996;58(1):267–288. doi:10.1111/j.2517-6161.1996.tb02080.x.
  • Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. Presented at: Proceedings of the 31st International Conference on Neural Information Processing Systems; 2017; {Red Hook, NY, USA}.
  • Mavrogiorgou A, Kiourtis A, Kleftakis S, et al. A catalogue of machine learning algorithms for healthcare risk predictions. Sensors. 2022;22(22):8615, doi:10.3390/s22228615.
  • Keser SB, Keskin K. A gradient boosting-based mortality prediction model for COVID-19 patients. Neural Computing and Applications. 2023;35(33):23997–24013. doi:10.1007/s00521-023-08997-w.
  • Uddin S, Khan A, Hossain ME, et al. Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak. 2019;19(1):281, doi:10.1186/s12911-019-1004-8.
  • Zapata RD, Huang S, Morris E, et al. Machine learning-based prediction models for home discharge in patients with COVID-19: development and evaluation using electronic health records. PLoS One. 2023;18(10):e0292888, doi:10.1371/journal.pone.0292888.
  • Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in Python. The Journal of Machine Learning Research. 2011;12:2825–2830.
  • Fau LC, Lyu T, Weissman S, et al. Early prediction of COVID-19 associated hospitalization at the time of CDC contact tracing using machine learning: towards pandemic preparedness. LID - rs.3.rs-3213502 [pii]. doi:10.21203/rs.3.rs-3213502/v1
  • Fernandes FT, de Oliveira TA, Teixeira CE, et al. A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil. (2045-2322 (Electronic)).
  • Cheng FY, Joshi HA-O, Tandon P, et al. Using machine learning to predict ICU transfer in hospitalized COVID-19 patients. LID - 1668. (2077-0383 (Print)). doi:10.3390/jcm9061668
  • Huang HF, Liu Y, Li JX, et al. Validated tool for early prediction of intensive care unit admission in COVID-19 patients. (2307-8960 (Print)).
  • Breiman L. Random forests. Mach Learn. 2001;45(1):5–32. doi:10.1023/A:1010933404324.
  • Breiman L, Jerome HF, Richard AO, et al. Classification and regression trees. Biometrics. 1984;40:874.
  • Subudhi S, Verma A, Patel AB, et al. Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19. NPJ Digit Med. 2021;4(1):87, doi:10.1038/s41746-021-00456-x.
  • Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001: 1189–1232.
  • Kim SJ, Koh K, Lustig M, et al. An interior-point method for large-scale $\ell_1$-regularized least squares. IEEE J Sel Top Signal Process. 2007;1(4):606–617. doi:10.1109/JSTSP.2007.910971.
  • Hoerl AE, Kennard RW. Ridge regression: biased estimation for nonorthogonal problems. Technometrics. 1970;12(1):55–67. doi:10.1080/00401706.1970.10488634.
  • Fu Y, Zhong W, Liu T, et al. Early Prediction Model for Critical Illness of Hospitalized COVID-19 Patients Based on Machine Learning Techniques. (2296-2565 (Electronic)).
  • Zhang S, Huang S, Liu J, et al. Identification and validation of prognostic factors in patients with COVID-19: a retrospective study based on artificial intelligence algorithms. Journal of Intensive Medicine. 2021;1(2):103–109. doi:10.1016/j.jointm.2021.04.001.
  • Koutroulos MV, Bakola SA, Kalpakidis S, et al. The MaD-CLINYC score: an easy tool for the prediction of the outcome of hospitalized COVID-19 patients. Hippokratia. 2021;25(3):119–125.
  • Harry Z. The optimality of naive bayes. 2004; https://api.semanticscholar.org/CorpusID:8891634.
  • Chang C-C, Lin C-J. LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol. 2011;2(3):1–27. doi:10.1145/1961189.1961199.
  • Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inf Theory. 1967;13(1):21–27. doi:10.1109/TIT.1967.1053964.
  • David ER, Geoffrey EH, Ronald JW. Learning representations by back-propagating errors. Nature. 1986;323:533–536. doi:10.1038/323533a0.
  • Jayawant NM. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315–1316. doi:10.1097/JTO.0b013e3181ec173d.
  • QuickFacts Missouri. [accessed July 15, 2023]. https://www.census.gov/quickfacts/fact/table/MO/PST045222
  • Data from: COVID-19 Hospitalization Metrics, by Day. Weekly COVID-19 Activity Report: Data for Download.
  • Gangavarapu K, Latif AA, Mullen JL, et al. Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations. Nat Methods. 2023;20(4):512–522. doi:10.1038/s41592-023-01769-3.
  • Data from: Missouri, United States Variant Report.
  • Equils O, Bakaj A, Wilson-Mifsud B, et al. Restoring trust: the need for precision medicine in infectious diseases, public health and vaccines. Hum Vaccin Immunother. 2023;19(2):2234787, doi:10.1080/21645515.2023.2234787.
  • Patone M, Thomas K, Hatch R, et al. Mortality and critical care unit admission associated with the SARS-CoV-2 lineage B.1.1.7 in England: an observational cohort study. Lancet Infect Dis. 2021;21(11):1518–1528. doi:10.1016/s1473-3099(21)00318-2.
  • Iacobucci G. COVID-19: New UK variant may be linked to increased death rate, early data indicate. BMJ. 2021;372:n230. doi:10.1136/bmj.n230.
  • Volz E, Mishra S, Chand M, et al. Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England. Nature. 2021;593(7858):266–269. doi:10.1038/s41586-021-03470-x.
  • Dey L, Chakraborty S, Mukhopadhyay A. Machine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human proteins. Biomed J. 2020;43(5):438–450. doi:10.1016/j.bj.2020.08.003.
  • Notarte KI, de Oliveira MHS, Peligro PJ, et al. Age, sex and previous comorbidities as risk factors not associated with SARS-CoV-2 infection for long COVID-19: a systematic review and meta-analysis. J Clin Med. 2022;11(24):7314), doi:10.3390/jcm11247314.
  • Gentilotti E, Górska A, Tami A, et al. Clinical phenotypes and quality of life to define post-COVID-19 syndrome: a cluster analysis of the multinational, prospective ORCHESTRA cohort. (2589-5370 (Electronic))
  • Planas D, Veyer D, Baidaliuk A, et al. Reduced sensitivity of SARS-CoV-2 variant Delta to antibody neutralization. Nature. 2021;596(7871):276–280. doi:10.1038/s41586-021-03777-9.
  • Anzalone AJ, Horswell R, Hendricks BM, et al. Higher hospitalization and mortality rates among SARS-CoV-2-infected persons in rural America. J Rural Health. 2023;39(1):39–54. doi:10.1111/jrh.12689.
  • Dixon BE, Grannis SJ, Lembcke LR, et al. The synchronicity of COVID-19 disparities: statewide epidemiologic trends in SARS-CoV-2 morbidity, hospitalization, and mortality among racial minorities and in rural America. PLoS One. 2021;16(7):e0255063, doi:10.1371/journal.pone.0255063.
  • Peters DJ. Community susceptibility and resiliency to COVID-19 across the rural-urban continuum in the United States. J Rural Health. 2020;36(3):446–456. doi:10.1111/jrh.12477.
  • Kleynhans J, Tempia S, Wolter N, et al. SARS-CoV-2 seroprevalence in a rural and urban household cohort during first and second waves of infections, South Africa, July 2020–March 2021. Emerg Infect Dis. 2021;27(12):3020–3029. doi:10.3201/eid2712.211465.
  • Tang CY, Li T, Haynes TA, et al. Rural populations facilitated early SARS-CoV-2 evolution and transmission in Missouri, USA. Npj Viruses. 2023;1(1):7, doi:10.1038/s44298-023-00005-1.
  • Gianfrancesco MA, Tamang S, Yazdany J, et al. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med. 2018;178(11):1544–1547. doi:10.1001/jamainternmed.2018.3763.