ArticleThe Journal of bone and joint surgery. American volume2020
Using Machine Learning to Estimate Unobserved COVID-19 Infections in North America.
Article in The Journal of bone and joint surgery. American volume, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.BioMed research international · 2021Pooled it
- A machine learning approach for diagnostic and prognostic predictions, key risk factors and interactions.Health services & outcomes research methodology · 2025Article
- Article
- A Review of the Machine Learning Algorithms for Covid-19 Case Analysis.IEEE transactions on artificial intelligence · 2023Article
- Assessing the Transmissibility of the New SARS-CoV-2 Variants: From Delta to Omicron.Vaccines · 2022Article
- Review on COVID-19 diagnosis models based on machine learning and deep learning approaches.Expert systems · 2022Article
- Robust trend estimation for COVID-19 in Brazil.Spatial and spatio-temporal epidemiology · 2021Article
- Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition.Scientific reports · 2021Article
- Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic.Diagnostics (Basel, Switzerland) · 2021Review
- Machine Learning for Analyzing Non-Countermeasure Factors Affecting Early Spread of COVID-19.International journal of environmental research and public health · 2021Article
- Estimation of Undetected Asymptomatic COVID-19 Cases in South Korea Using a Probabilistic Model.International journal of environmental research and public health · 2021Article
- Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19.Journal of critical care · 2021Article
- Intelligent system for COVID-19 prognosis: a state-of-the-art survey.Applied intelligence (Dordrecht, Netherlands) · 2021Article
- Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential.Frontiers in physiology · 2021Article
- COVID-19 detection using federated machine learning.PloS one · 2021Article
- Applications of artificial intelligence in battling against covid-19: A literature review.Chaos, solitons, and fractals · 2021Review
- Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks.PeerJ. Computer science · 2020Article
- The Landscape of Coronavirus Disease 2019 (COVID-19) and Integrated Analysis SARS-CoV-2 Receptors and Potential Inhibitors in Lung Adenocarcinoma Patients.Frontiers in cell and developmental biology · 2020Article
- Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India.Infection and drug resistance · 2020Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe detection of coronavirus disease 2019 (COVID-19) cases remains a huge challenge. As of April 22, 2020, the COVID-19 pandemic continues to take its toll, with >2.6 million confirmed infections and >183,000 deaths. Dire projections are surfacing almost every day, and policymakers worldwide are using projections for critical decisions. Given this background, we modeled unobserved infections to examine the extent to which we might be grossly underestimating COVID-19 infections in North America.
methodsWe developed a machine-learning model to uncover hidden patterns based on reported cases and to predict potential infections. First, our model relied on dimensionality reduction to identify parameters that were key to uncovering hidden patterns. Next, our predictive analysis used an unbiased hierarchical Bayesian estimator approach to infer past infections from current fatalities.
resultsOur analysis indicates that, when we assumed a 13-day lag time from infection to death, the United States, as of April 22, 2020, likely had at least 1.3 million undetected infections. With a longer lag time-for example, 23 days-there could have been at least 1.7 million undetected infections. Given these assumptions, the number of undetected infections in Canada could have ranged from 60,000 to 80,000. Duarte's elegant unbiased estimator approach suggested that, as of April 22, 2020, the United States had up to >1.6 million undetected infections and Canada had at least 60,000 to 86,000 undetected infections. However, the Johns Hopkins University Center for Systems Science and Engineering data feed on April 22, 2020, reported only 840,476 and 41,650 confirmed cases for the United States and Canada, respectively.
conclusionsWe have identified 2 key findings: (1) as of April 22, 2020, the United States may have had 1.5 to 2.029 times the number of reported infections and Canada may have had 1.44 to 2.06 times the number of reported infections and (2) even if we assume that the fatality and growth rates in the unobservable population (undetected infections) are similar to those in the observable population (confirmed infections), the number of undetected infections may be within ranges similar to those described above. In summary, 2 different approaches indicated similar ranges of undetected infections in North America. LEVEL OF EVIDENCE: Prognostic Level V. See Instructions for Authors for a complete description of levels of evidence.
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