ReviewFrontiers in public health2023
The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management.
Review in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 10 papers.
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.
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.
A Prevalence Study of Persistent COVID-19 in Punta Arenas, Magallanes and Chilean Antarctic Region
Evaluation and implementation of a multimodal intervention to reduce symptoms of long covid/prolonged covid in the adult population of punta arenas, chile.
Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- Explainable convolutional neural network model provides an alternative genome-wide association perspective on mutations in SARS-CoV-2.Scientific reports · 2026Article
- Article
- Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review.Bioengineering (Basel, Switzerland) · 2025Review
- Machine learning and public health policy evaluation: research dynamics and prospects for challenges.Frontiers in public health · 2025Review
- NeurostimML: a machine learning model for predicting neurostimulation-induced tissue damage.Journal of neural engineering · 2024Article
- Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review.Advances in therapy · 2024Review
- Assessing the impact of vaccines on COVID-19 efficacy in survival rates: a survival analysis approach for clinical decision support.Frontiers in public health · 2024Article
- Precision Epidemiology: A Computational Analysis of the Impact of Algorithmic Prediction on the Relationship Between Population Epidemiology and Clinical Epidemiology.International journal of public health · 2024Article
- NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage.bioRxiv : the preprint server for biology · 2023Article
- Reply to: Microvascular hypertensive disease, long COVID, and end-organ pathology.Hypertension research : official journal of the Japanese Society of Hypertension · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ongoing COVID-19 pandemic is arguably one of the most challenging health crises in modern times. The development of effective strategies to control the spread of SARS-CoV-2 were major goals for governments and policy makers. Mathematical modeling and machine learning emerged as potent tools to guide and optimize the different control measures. This review briefly summarizes the SARS-CoV-2 pandemic evolution during the first 3 years. It details the main public health challenges focusing on the contribution of mathematical modeling to design and guide government action plans and spread mitigation interventions of SARS-CoV-2. Next describes the application of machine learning methods in a series of study cases, including COVID-19 clinical diagnosis, the analysis of epidemiological variables, and drug discovery by protein engineering techniques. Lastly, it explores the use of machine learning tools for investigating long COVID, by identifying patterns and relationships of symptoms, predicting risk indicators, and enabling early evaluation of COVID-19 sequelae.
Indexed as
Identifiers
What OpenQuestion holds
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.