ArticleJournal of the Royal Society, Interface2020
Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2.
Article in Journal of the Royal Society, Interface, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Mechanistic Understanding of Pandemic Dynamics: A Multiscale Algorithmic Framework.Life (Basel, Switzerland) · 2026Article
- Predictive healthcare modeling for early pandemic assessment leveraging deep auto regressor neural prophet.Scientific reports · 2024Article
- Data-Driven Deep Learning Neural Networks for Predicting the Number of Individuals Infected by COVID-19 Omicron Variant.Epidemiologia (Basel, Switzerland) · 2023Article
- Fitting Early Phases of the COVID-19 Outbreak: A Comparison of the Performances of Used Models.Healthcare (Basel, Switzerland) · 2023Article
- An algebraic formula, deep learning and a novel SEIR-type model for the COVID-19 pandemic.Royal Society open science · 2023Article
- Machine Learning Algorithms Associate Case Numbers with SARS-CoV-2 Variants Rather Than with Impactful Mutations.Viruses · 2023Article
- Compartmental mathematical modelling of dynamic transmission of COVID-19 in Rwanda.IJID regions · 2023Article
- Computational Models for Biomarker Discovery.Advances in experimental medicine and biology · 2023Article
- Biserial targeted feature projection based radial kernel regressive deep belief neural learning for covid-19 prediction.Soft computing · 2023Article
- Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Susceptibility to Resurgent COVID-19 Outbreaks Following Vaccine Rollouts: A Modeling Study.Viruses · 2022Article
- Spatial heterogeneity affects predictions from early-curve fitting of pandemic outbreaks: a case study using population data from Denmark.Royal Society open science · 2022Article
- Deming least square regressed feature selection and Gaussian neuro-fuzzy multi-layered data classifier for early COVID prediction.Expert systems · 2022Article
- Could COVID-19's Aftermath on Children's Health Be Felt into the 22nd Century?Children (Basel, Switzerland) · 2022Article
- Influence of state reopening policies in COVID-19 mortality.Scientific reports · 2022Article
- Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach.Frontiers in public health · 2022Article
- Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD.Computers in biology and medicine · 2021Article
- Investigating the first stage of the COVID-19 pandemic in Ukraine using epidemiological and genomic data.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2021Article
- Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review.Heliyon · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We introduce a novel methodology for predicting the time evolution of the number of individuals in a given country reported to be infected with SARS-CoV-2. This methodology, which is based on the synergy of explicit mathematical formulae and deep learning networks, yields algorithms whose input is only the existing data in the given country of the accumulative number of individuals who are reported to be infected. The analytical formulae involve several constant parameters that were determined from the available data using an error-minimizing algorithm. The same data were also used for the training of a bidirectional long short-term memory network. We applied the above methodology to the epidemics in Italy, Spain, France, Germany, USA and Sweden. The significance of these results for evaluating the impact of easing the lockdown measures is discussed.
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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.