ArticleExpert systems2022
Pandemic coronavirus disease (Covid-19): World effects analysis and prediction using machine-learning techniques.
Article in Expert systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Deep fused explainable neural architecture for the automatic recognition of therapeutic medicinal plants.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2026Article
- A novel lexicon dictionary and CNN-LSTM employed hybrid approach for sentiment detection of COVID-19 vaccines.Scientific reports · 2026Article
- Survey on mathematical modeling of infectious disease dynamics: insights and applications.BMC infectious diseases · 2026Review
- Uncloaking the black-box: the need for explainable artificial intelligence in clinical microbiology and infectious diseases applications.Frontiers in public health · 2026Review
- Research trends and hotspots in viral neglected tropical diseases: a bibliometric analysis.Future microbiology · 2025Article
- A swarm-optimization based fusion model of sentiment analysis for cryptocurrency price prediction.Scientific reports · 2025Article
- Research on the evolution of college online public opinion risk based on improved Grey Wolf Optimizer combined with LSTM model.PloS one · 2025Article
- A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction.Scientific reports · 2024Article
- COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms.BMC bioinformatics · 2024Article
- Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets.PeerJ. Computer science · 2024Article
- Impact of the Pandemic Time on the Mental Health of People with Dementia and Their Family Caregivers in Brazil and Chile: One-Year Follow-Up.Journal of Alzheimer's disease : JAD · 2024Article
- FCMCPS-COVID: AI propelled fog-cloud inspired scalable medical cyber-physical system, specific to coronavirus disease.Internet of things (Amsterdam, Netherlands) · 2023Article
- The coming Omicron waves and factors affecting its spread after China reopening borders.BMC medical informatics and decision making · 2023Article
- Effect of a health education program about COVID-19 on the knowledge, attitude, and practices of paramedical students in Egypt: an interventional study.Journal of public health in Africa · 2023Article
- An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM.Scientific reports · 2023Article
- A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms.Mobile networks and applications : MONET · 2023Article
- Multi-region machine learning-based novel ensemble approaches for predicting COVID-19 pandemic in Africa.Environmental science and pollution research international · 2023Article
- Data Mining Based Techniques for Covid-19 Predictions.Procedia computer science · 2023Article
- Prediction and forecasting of worldwide corona virus (COVID-19) outbreak using time series and machine learning.Concurrency and computation : practice & experience · 2022Article
- Pandemic coronavirus disease (Covid-19): World effects analysis and prediction using machine-learning techniques.Expert systems · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pandemic novel Coronavirus (Covid-19) is an infectious disease that primarily spreads by droplets of nose discharge when sneezing and saliva from the mouth when coughing, that had first been reported in Wuhan, China in December 2019. Covid-19 became a global pandemic, which led to a harmful impact on the world. Many predictive models of Covid-19 are being proposed by academic researchers around the world to take the foremost decisions and enforce the appropriate control measures. Due to the lack of accurate Covid-19 records and uncertainty, the standard techniques are being failed to correctly predict the epidemic global effects. To address this issue, we present an Artificial Intelligence (AI)-based meta-analysis to predict the trend of epidemic Covid-19 over the world. The powerful machine learning algorithms namely Naïve Bayes, Support Vector Machine (SVM) and Linear Regression were applied on real time-series dataset, which holds the global record of confirmed, recovered, deaths and active cases of Covid-19 outbreak. Statistical analysis has also been conducted to present various facts regarding Covid-19 observed symptoms, a list of Top-20 Coronavirus affected countries and a number of coactive cases over the world. Among the three machine learning techniques investigated, Naïve Bayes produced promising results to predict Covid-19 future trends with less Mean Absolute Error (MAE) and Mean Squared Error (MSE). The less value of MAE and MSE strongly represent the effectiveness of the Naïve Bayes regression technique. Although, the global footprint of this pandemic is still uncertain. This study demonstrates the various trends and future growth of the global pandemic for a proactive response from the citizens and governments of countries. This paper sets the initial benchmark to demonstrate the capability of machine learning for outbreak prediction.
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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.