Evidence map›Paper›PMID 33922693›Full record

SynthesisInternational journal of environmental research and public health2021

Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review.

Anil Babu Payedimarri, Diego Concina, Luigi Portinale, Massimo Canonico, Deborah Seys, Kris Vanhaecht, Massimiliano Panella

Abstract readSystematic Review
In one paragraph

Synthesis in International journal of environmental research and public health, 2021. 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. AI Quality Standards in Health Care: Rapid Umbrella Review.Journal of medical Internet research · 2024
    Pooled it
  3. Trial
  4. Article
  5. Review
  6. Article
  7. Advances in methods for characterizing dietary patterns: A scoping review.medRxiv : the preprint server for health sciences · 2024
    Article
  8. Applying Machine Learning Techniques to Implementation Science.Online journal of public health informatics · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Review
  15. Article
  16. Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia.International journal of environmental research and public health · 2022
    Article
  17. Article
  18. Article
  19. Article
  20. Review
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

7 authors.

Anil Babu PayedimarriDepartment of Translational Medicine (DIMET), Università del Piemonte Orientale, 28100 Novara, Italy.ORCID 0000-0003-4298-1305
Diego ConcinaDepartment of Translational Medicine (DIMET), Università del Piemonte Orientale, 28100 Novara, Italy.
Luigi PortinaleDepartment of Science and Technological Innovation (DISIT) Università del Piemonte Orientale, 15121 Alessandria, Italy.
Massimo CanonicoDepartment of Science and Technological Innovation (DISIT) Università del Piemonte Orientale, 15121 Alessandria, Italy.
Deborah SeysLeuven Institute for Healthcare Policy, Department of Public Health and Primary Care, KU Leuven, 3000 Leuven, Belgium.ORCID 0000-0003-4966-3879
Kris VanhaechtLeuven Institute for Healthcare Policy, Department of Public Health and Primary Care, KU Leuven, 3000 Leuven, Belgium.
Massimiliano PanellaDepartment of Translational Medicine (DIMET), Università del Piemonte Orientale, 28100 Novara, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

Indexed as

COVID-19PandemicsArtificial IntelligenceCommunicable Disease ControlHumansMachine LearningPublic HealthQuarantineSARS-CoV-2artificial intelligenceCOVID-19epidemicmachine learningpandemicprediction modelspublic health interventionssevere acute respiratory syndrome coronavirus-2

Identifiers

PMID33922693
PMCPMC8123005

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.