ArticleStochastic environmental research and risk assessment : research journal2023
Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries.
Article in Stochastic environmental research and risk assessment : research journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed.
- Global Spatiotemporal Dynamics of African Swine Fever: An Integrated Multi-Scale Spatial and Time-Series Analysis.Viruses · 2026Article
- Comparative analysis of supervised and ensemble models with unsupervised exploration for alzheimer's disease prediction.Scientific reports · 2026Article
- Integrating machine learning and time-to-event models to explain and predict risk of hospitalization due to dengue in Colombia.Scientific reports · 2025Article
- Asthma and severe acute respiratory infections: a stratified analysis of mortality patterns in Brazil.BMC public health · 2025Article
- Comparison of ARIMA and FB-Prophet time series models for the prediction of national and regional malaria incidence in Uganda.Malaria journal · 2025Article
- Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review.Bioengineering (Basel, Switzerland) · 2025Review
- Multiagent AI Systems in Health Care: Envisioning Next-Generation Intelligence.Federal practitioner : for the health care professionals of the VA, DoD, and PHS · 2025Article
- Infectious disease prediction model based on optimized deep learning algorithm.Frontiers in public health · 2025Article
- A novel ensemble ARIMA-LSTM approach for evaluating COVID-19 cases and future outbreak preparedness.Health care science · 2024Article
- A six-compartment model for COVID-19 with transmission dynamics and public health strategies.Scientific reports · 2024Article
- Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants.Scientific reports · 2024Article
- Evaluating water-related health risks in East and Central Asian Islamic Nations using predictive models (2020-2030).Scientific reports · 2024Article
- Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models.BMC public health · 2024Article
- Article
- Global, regional, and national mortality of tuberculosis attributable to alcohol and tobacco from 1990 to 2019: A modelling study based on the Global Burden of Disease study 2019.Journal of global health · 2024Article
- Tuberculosis disease burden in China: a spatio-temporal clustering and prediction study.Frontiers in public health · 2024Article
- On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders.Biomedicines · 2023Article
- An Epidemiological Analysis for Assessing and Evaluating COVID-19 Based on Data Analytics in Latin American Countries.Biology · 2023Article
- An analytical approach to evaluate the impact of age demographics in a pandemic.Stochastic environmental research and risk assessment : research journal · 2023Article
- Article
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5 authors.
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Abstract
Machine learning (ML) has proved to be a prominent study field while solving complex real-world problems. The whole globe has suffered and continues suffering from Coronavirus disease 2019 (COVID-19), and its projections need to be forecasted. In this article, we propose and derive an autoregressive modeling framework based on ML and statistical methods to predict confirmed cases of COVID-19 in the South Asian Association for Regional Cooperation (SAARC) countries. Automatic forecasting models based on autoregressive integrated moving average (ARIMA) and Prophet time series structures, as well as extreme gradient boosting, generalized linear model elastic net (GLMNet), and random forest ML techniques, are introduced and applied to COVID-19 data from the SAARC countries. Different forecasting models are compared by means of selection criteria. By using evaluation metrics, the best and suitable models are selected. Results prove that the ARIMA model is found to be suitable and ideal for forecasting confirmed infected cases of COVID-19 in these countries. For the confirmed cases in Afghanistan, Bangladesh, India, Maldives, and Sri Lanka, the ARIMA model is superior to the other models. In Bhutan, the Prophet time series model is appropriate for predicting such cases. The GLMNet model is more accurate than other time-series models for Nepal and Pakistan. The random forest model is excluded from forecasting because of its poor fit.
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