Evidence map›Paper›PMID 32618918›Full record

ArticleThe Journal of bone and joint surgery. American volume2020

Using Machine Learning to Estimate Unobserved COVID-19 Infections in North America.

Shashank Vaid, Caglar Cakan, Mohit Bhandari

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. A Review of the Machine Learning Algorithms for Covid-19 Case Analysis.IEEE transactions on artificial intelligence · 2023
    Article
  6. Article
  7. Article
  8. Robust trend estimation for COVID-19 in Brazil.Spatial and spatio-temporal epidemiology · 2021
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  10. Review
  11. Machine Learning for Analyzing Non-Countermeasure Factors Affecting Early Spread of COVID-19.International journal of environmental research and public health · 2021
    Article
  12. Estimation of Undetected Asymptomatic COVID-19 Cases in South Korea Using a Probabilistic Model.International journal of environmental research and public health · 2021
    Article
  13. Article
  14. Intelligent system for COVID-19 prognosis: a state-of-the-art survey.Applied intelligence (Dordrecht, Netherlands) · 2021
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  17. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Shashank VaidDeGroote School of Business, McMaster University, Hamilton, Ontario, Canada.ORCID 0000-0003-0658-2016
Caglar CakanDepartment of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany.ORCID 0000-0002-1902-5393
Mohit BhandariDivision of Orthopaedic Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada.ORCID 0000-0003-3556-9179

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Machine LearningBayes TheoremBetacoronavirusCanadaComputer SimulationCoronavirus InfectionsCOVID-19ForecastingHumansNorth AmericaPandemicsPneumonia, ViralSARS-CoV-2United States

Identifiers

PMID32618918
PMCPMC7396213

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.