ArticleSoftware: practice & experience2022
An approach to forecast impact of Covid-19 using supervised machine learning model.
Article in Software: practice & experience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- FCMCPS-COVID: AI propelled fog-cloud inspired scalable medical cyber-physical system, specific to coronavirus disease.Internet of things (Amsterdam, Netherlands) · 2023Article
- An innovative ensemble model based on deep learning for predicting COVID-19 infection.Scientific reports · 2023Article
- Proposing a hybrid technique of feature fusion and convolutional neural network for melanoma skin cancer detection.Journal of pathology informatics · 2023Article
- Deep learning for Covid-19 forecasting: State-of-the-art review.Neurocomputing · 2022Article
- Challenges and Elements Needed for Children with Learning Disabilities in Teaching and Learning the Quran.Children (Basel, Switzerland) · 2022Article
- An approach to forecast impact of Covid-19 using supervised machine learning model.Software: practice & experience · 2022Article
- Advanced data integration in banking, financial, and insurance software in the age of COVID-19.Software: practice & experience · 2022Article
- Article
- Using Machine Learning Methods to Predict Bone Metastases in Breast Infiltrating Ductal Carcinoma Patients.Frontiers in public health · 2022Article
- An artificial intelligence method for comprehensive evaluation of preschool education quality.Frontiers in psychology · 2022Article
- Individual Factors Associated With COVID-19 Infection: A Machine Learning Study.Frontiers in public health · 2022Article
- Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics.Network modeling and analysis in health informatics and bioinformatics · 2022Review
- Blockchain for COVID-19: a comprehensive review.Personal and ubiquitous computing · 2021Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Covid-19 pandemic has emerged as one of the most disquieting worldwide public health emergencies of the 21st century and has thrown into sharp relief, among other factors, the dire need for robust forecasting techniques for disease detection, alleviation as well as prevention. Forecasting has been one of the most powerful statistical methods employed the world over in various disciplines for detecting and analyzing trends and predicting future outcomes based on which timely and mitigating actions can be undertaken. To that end, several statistical methods and machine learning techniques have been harnessed depending upon the analysis desired and the availability of data. Historically speaking, most predictions thus arrived at have been short term and country-specific in nature. In this work, multimodel machine learning technique is called EAMA for forecasting Covid-19 related parameters in the long-term both within India and on a global scale have been proposed. This proposed EAMA hybrid model is well-suited to predictions based on past and present data. For this study, two datasets from the Ministry of Health & Family Welfare of India and Worldometers, respectively, have been exploited. Using these two datasets, long-term data predictions for both India and the world have been outlined, and observed that predicted data being very similar to real-time values. The experiment also conducted for statewise predictions of India and the countrywise predictions across the world and it has been included in the Appendix.
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.