ArticleTransboundary and emerging diseases2021
Exploring the growth of COVID-19 cases using exponential modelling across 42 countries and predicting signs of early containment using machine learning.
Article in Transboundary and emerging diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- An Entropy-Based Scale-Free L-Statistic for Exponentiality Testing in Lifetime Data with DMRL Aging.Entropy (Basel, Switzerland) · 2026Article
- Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework.JMIR public health and surveillance · 2025Article
- Review
- Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study.Journal of medical Internet research · 2024Article
- A novel bidirectional LSTM deep learning approach for COVID-19 forecasting.Scientific reports · 2023Article
- Effect of sojourn time distributions on the early dynamics of COVID-19 outbreak.Nonlinear dynamics · 2023Article
- The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19.Social science & medicine (1982) · 2022Review
- Effects of vaccination and non-pharmaceutical interventions and their lag times on the COVID-19 pandemic: Comparison of eight countries.PLoS neglected tropical diseases · 2022Article
- Setting up a temporary isolation tent site for asymptomatic COVID-19 positive male migrant workers in Qatar.Qatar medical journal · 2022Article
- Some new findings on the survival Rényi entropy and application of COVID-19 data.Results in physics · 2021Article
- An intelligent framework to predict socioeconomic impacts of COVID-19 and public sentiments.Computers & electrical engineering : an international journal · 2021Article
- Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review.Heliyon · 2021Article
- COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review.Interdisciplinary sciences, computational life sciences · 2021Review
- Exploring the growth of COVID-19 cases using exponential modelling across 42 countries and predicting signs of early containment using machine learning.Transboundary and emerging diseases · 2021Article
- Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence.Environmental science and pollution research international · 2021Article
- On Pilot Massive COVID-19 Testing by Antigen Tests in Europe. Case Study: Slovakia.Infectious disease reports · 2021Article
- A systematic review on AI/ML approaches against COVID-19 outbreak.Complex & intelligent systems · 2021Article
- The prediction of the lifetime of the new coronavirus in the USA using mathematical models.Soft computing · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The coronavirus disease 2019 (COVID-19) pandemic spread by the single-stranded RNA severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to the seventh generation of the coronavirus family. Following an unusual replication mechanism, its extreme ease of transmissivity has put many countries under lockdown. With the uncertainty of developing a cure/vaccine for the infection in the near future, the onus currently lies on healthcare infrastructure, policies, government activities, and behaviour of the people to contain the virus. This research uses exponential growth modelling studies to understand the spreading patterns of SARS-CoV-2 and identifies countries that showed early signs of containment until March 26, 2020. Predictive supervised machine learning models are built using infrastructure, environment, policies, and infection-related independent variables to predict early containment. COVID-19 infection data across 42 countries are used. Logistic regression results show a positive significant relationship between healthcare infrastructure and lockdown policies, and signs of early containment. Machine learning models based on logistic regression, decision tree, random forest, and support vector machines are developed and show accuracies between 76.2% and 92.9% to predict early signs of infection containment. Other policies and the decisions taken by countries to contain the infection are also discussed.
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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.