ArticleJournal of virological methods2022
Time series analysis and predicting COVID-19 affected patients by ARIMA model using machine learning.
Article in Journal of virological methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review.International journal of environmental research and public health · 2022Pooled it
- Wavelet analysis and time series forecasting of malaria incidence across different transmission settings in Nigeria.BMC infectious diseases · 2026Article
- Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis.BMC public health · 2025Article
- Spectral study of COVID-19 pandemic in Japan: The dependence of spectral gradient on the population size of the community.PloS one · 2025Article
- Impact and Effectiveness of COVID-19 Vaccines Based on Machine Learning Analysis of a Time Series: A Population-Based Study.Journal of clinical medicine · 2024Article
- Development of a novel dynamic nosocomial infection risk management method for COVID-19 in outpatient settings.BMC infectious diseases · 2024Article
- Spatial-temporal analysis of hepatitis E in Hainan Province, China (2013-2022): insights from four major hospitals.Frontiers in public health · 2024Article
- Insight into vaccination and meteorological factors on daily COVID-19 cases and mortality in Bangladesh.Groundwater for sustainable development · 2023Article
- Article
- Nesting the SIRV model with NAR, LSTM and statistical methods to fit and predict COVID-19 epidemic trend in Africa.BMC public health · 2023Article
- Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases.Frontiers in artificial intelligence · 2023Article
- Time series analysis of daily reported number of new positive cases of COVID-19 in Japan from January 2020 to February 2023.PloS one · 2023Article
- Investigating Topic Modeling Techniques to Extract Meaningful Insights in Italian Long COVID Narration.Biotech (Basel (Switzerland)) · 2022Article
- Modeling Global COVID-19 Dissemination Data After the Emergence of Omicron Variant Using Multipronged Approaches.Current microbiology · 2022Article
- Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data.Journal of healthcare engineering · 2022Article
- Comparative Evaluation of the Multilayer Perceptron Approach with Conventional ARIMA in Modeling and Prediction of COVID-19 Daily Death Cases.Journal of healthcare engineering · 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
- Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories.AIMS public health · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The spread of a respiratory syndrome known as Coronavirus Disease 2019 (COVID-19) quickly took on pandemic proportions, affecting over 192 countries. An emergency of the health system was obligated for the response to this epidemic. Although containment measures in China reduced new cases by more than 90 %, the levels of reduction were not the same in other countries. So, the question that arises is: what the world will see this pandemic, and how many patients can be affected? The response would be helpful and supportive of the authority and the community to prepare for the coming days. In this study, the Autoregressive Integrated Moving Average (ARIMA) model was employed to analyze the temporal dynamics of the worldwide spread of COVID-19 in the time window from January 22, 2020 to April 7, 2020. The cumulative number of confirmed Covid-19-affected patients forecasted over the three months was between 9,189,262 - 14,906,483 worldwide. This prediction value of Covid 19-affected patients will be valid only if the situation remains unchanged, and the epidemic spreads according to the previous nature worldwide in these three months.
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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.