ArticleGlobal health research and policy2020
Application of artificial neural networks to predict the COVID-19 outbreak.
Article in Global health research and policy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 109 citations in OpenAlex.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- How Will AI Shape the Future of Pandemic Response? Early Clues From Data Analytics.Risk analysis : an official publication of the Society for Risk Analysis · 2025Review
- Artificial intelligence in biology and medicine.Die Naturwissenschaften · 2025Review
- Predictive Modeling for Pandemic Forecasting: A COVID-19 Study in New Zealand and Partner Countries.International journal of environmental research and public health · 2025Article
- Article
- Modeling health outcomes of air pollution in the Middle East by using support vector machines and neural networks.Scientific reports · 2024Article
- Using meta-learning to recommend an appropriate time-series forecasting model.BMC public health · 2024Article
- The Importance of Pseudo Thrombocytopenia Due to Platelet Cold Agglutination before Surgery, What Should We Do? A Case Report.International journal of hematology-oncology and stem cell research · 2024Article
- Article
- Article
- A Hybridized Machine Learning Approach for Predicting COVID-19 Using Adaptive Neuro-Fuzzy Inference System and Reptile Search Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- Estimate the incubation period of coronavirus 2019 (COVID-19).Computers in biology and medicine · 2023Article
- Machine Learning to Predict the Adsorption Capacity of Microplastics.Nanomaterials (Basel, Switzerland) · 2023Article
- Multi-region machine learning-based novel ensemble approaches for predicting COVID-19 pandemic in Africa.Environmental science and pollution research international · 2023Article
- Artificial Neural Networks for the Prediction of Monkeypox Outbreak.Tropical medicine and infectious disease · 2022Article
- Multi-outputs Gaussian process for predicting Burkina Faso COVID-19 spread using correlations from the weather parameters.Infectious Disease Modelling · 2022Review
- Artificial neural networks for prediction of COVID-19 in India by using backpropagation.Expert systems · 2022Article
- Applying Benford's law to COVID-19 data: the case of the European Union.Journal of public health (Oxford, England) · 2022Article
- Explanation of COVID-19 Mortality Using Artificial Neural Network Based on Underlying and Laboratory Risk Factors in Ilam, Iran.Archives of Razi Institute · 2022Article
- Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks.Journal of cellular and molecular medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMillions of people have been infected worldwide in the COVID-19 pandemic. In this study, we aim to propose fourteen prediction models based on artificial neural networks (ANN) to predict the COVID-19 outbreak for policy makers.
methodsThe ANN-based models were utilized to estimate the confirmed cases of COVID-19 in China, Japan, Singapore, Iran, Italy, South Africa and United States of America. These models exploit historical records of confirmed cases, while their main difference is the number of days that they assume to have impact on the estimation process. The COVID-19 data were divided into a train part and a test part. The former was used to train the ANN models, while the latter was utilized to compare the purposes. The data analysis shows not only significant fluctuations in the daily confirmed cases but also different ranges of total confirmed cases observed in the time interval considered.
resultsBased on the obtained results, the ANN-based model that takes into account the previous 14 days outperforms the other ones. This comparison reveals the importance of considering the maximum incubation period in predicting the COVID-19 outbreak. Comparing the ranges of determination coefficients indicates that the estimated results for Italy are the best one. Moreover, the predicted results for Iran achieved the ranges of [0.09, 0.15] and [0.21, 0.36] for the mean absolute relative errors and normalized root mean square errors, respectively, which were the best ranges obtained for these criteria among different countries.
conclusionBased on the achieved results, the ANN-based model that takes into account the previous fourteen days for prediction is suggested to predict daily confirmed cases, particularly in countries that have experienced the first peak of the COVID-19 outbreak. This study has not only proved the applicability of ANN-based model for prediction of the COVID-19 outbreak, but also showed that considering incubation period of SARS-COV-2 in prediction models may generate more accurate estimations.
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.