ArticleChaos, solitons, and fractals2020
Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil.
Article in Chaos, solitons, and fractals, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 164 papers, 3 of them syntheses that pooled it.
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Who cites it
164 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic.Frontiers in genetics · 2022Pooled it
- Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Artificial Intelligence for COVID-19: A Systematic Review.Frontiers in medicine · 2021Pooled it
- TEAM: A time-enhanced attention-based model for virus mutation prediction.Quantitative biology (Beijing, China) · 2026Article
- Enhancing COVID-19 Forecasting Accuracy in Malaysia Using a Hybrid ARIMA-LSTM Model With Exogenous Variables: A Time-Series Predictive Study.Health science reports · 2026Article
- Impact of the first booster vaccine against SARS-CoV-2 in Chile.Infectious Disease Modelling · 2026Article
- Real-time dynamic prediction of HFMD transmission using SEIRQ-ARIMA hybrid model optimized by multi-stage ABC-GWO algorithm.Scientific reports · 2026Article
- Article
- A Comparative Study on COVID-19 Dynamics: Mathematical Modeling, Predictions, and Resource Allocation Strategies in Romania, Italy, and Switzerland.Bioengineering (Basel, Switzerland) · 2025Article
- Comparison between simulated scenarios and Swedish COVID-19 cases throughout the pandemic.Scientific reports · 2025Article
- Neural networks to model COVID-19 dynamics and allocate healthcare resources.Scientific reports · 2025Article
- Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree-Based Machine Learning Model.Health science reports · 2025Article
- An investigation into the impact of temporality on COVID-19 infection and mortality predictions: new perspective based on Shapley Values.BMC medical research methodology · 2025Article
- A machine learning approach for diagnostic and prognostic predictions, key risk factors and interactions.Health services & outcomes research methodology · 2025Article
- The role of artificial intelligence in pandemic responses: from epidemiological modeling to vaccine development.Molecular biomedicine · 2025Review
- A data-driven approach to study temporal characteristics of COVID-19 infection and death Time Series for twelve countries across six continents.BMC medical research methodology · 2025Article
- Article
- 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 Narrative Review of High Throughput Wastewater Sample Processing for Infectious Disease Surveillance: Challenges, Progress, and Future Opportunities.International journal of environmental research and public health · 2024Review
- Automated robot and artificial intelligence-powered wastewater surveillance for proactive mpox outbreak prediction.Biosafety and health · 2024Article
104 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The new Coronavirus (COVID-19) is an emerging disease responsible for infecting millions of people since the first notification until nowadays. Developing efficient short-term forecasting models allow forecasting the number of future cases. In this context, it is possible to develop strategic planning in the public health system to avoid deaths. In this paper, autoregressive integrated moving average (ARIMA), cubist regression (CUBIST), random forest (RF), ridge regression (RIDGE), support vector regression (SVR), and stacking-ensemble learning are evaluated in the task of time series forecasting with one, three, and six-days ahead the COVID-19 cumulative confirmed cases in ten Brazilian states with a high daily incidence. In the stacking-ensemble learning approach, the CUBIST regression, RF, RIDGE, and SVR models are adopted as base-learners and Gaussian process (GP) as meta-learner. The models' effectiveness is evaluated based on the improvement index, mean absolute error, and symmetric mean absolute percentage error criteria. In most of the cases, the SVR and stacking-ensemble learning reach a better performance regarding adopted criteria than compared models. In general, the developed models can generate accurate forecasting, achieving errors in a range of 0.87%-3.51%, 1.02%-5.63%, and 0.95%-6.90% in one, three, and six-days-ahead, respectively. The ranking of models, from the best to the worst regarding accuracy, in all scenarios is SVR, stacking-ensemble learning, ARIMA, CUBIST, RIDGE, and RF models. The use of evaluated models is recommended to forecasting and monitor the ongoing growth of COVID-19 cases, once these models can assist the managers in the decision-making support systems.
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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.