ArticleEmerging microbes & infections2024
Prediction models for COVID-19 disease outcomes.
Article in Emerging microbes & infections, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Post-COVID-19 Condition Diagnosis among Older People: Findings from Swedish National Register Data.Gerontology · 2026Article
- Machine learning and probabilistic approaches for forecasting infectious disease transmission and cases.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2026Article
- A predictive model for PICC-related thrombosis in sepsis patients using XGBoost algorithm.Scientific reports · 2026Article
- Integrating Host Genetics and Clinical Setting in Machine Learning Models: Predicting COVID-19 Prognosis for Healthcare Decision-Making (The FeMiNa Study).Diagnostics (Basel, Switzerland) · 2026Article
- Development of a machine learning-based mortality prediction model for patients with mental disorders and COVID-19.Frontiers in cellular and infection microbiology · 2026Article
- Tracing Emergence of SARS-CoV-2 Variants: Insights from Comprehensive Assessment Using Reverse Transcription Polymerase Chain Reaction and Whole Genome Sequencing.Microorganisms · 2025Article
- Clinical characteristics and predictive models for hospitalized patients with COVID-19 combined with bacterial pneumonia.PloS one · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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% (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.