ArticlePloS one2020
Prediction of COVID-19 spreading profiles in South Korea, Italy and Iran by data-driven coding.
Article in PloS one, 2020. 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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Who cites it
22 citing papers in PubMed.
- Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic.World journal of clinical cases · 2023Review
- Using simulation modelling and systems science to help contain COVID-19: A systematic review.Systems research and behavioral science · 2022Article
- Estimating unconfirmed COVID-19 infection cases and multiple waves of pandemic progression with consideration of testing capacity and non-pharmaceutical interventions: A dynamic spreading model.Information sciences · 2022Article
- Forecasting of a complex phenomenon using stochastic data-based techniques under non-conventional schemes: The SARS-CoV-2 virus spread case.Chaos, solitons, and fractals · 2022Article
- Time series analysis and predicting COVID-19 affected patients by ARIMA model using machine learning.Journal of virological methods · 2022Article
- The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases.Infectious Disease Modelling · 2022Article
- How do the smart travel ban policy and intercity travel pattern affect COVID-19 trends? Lessons learned from Iran.PloS one · 2022Article
- Epidemiologic Parameters for COVID-19: A Systematic Review and Meta-Analysis.Medical journal of the Islamic Republic of Iran · 2022Review
- Big data analysis of the impact of COVID-19 on digital game industrial sustainability in South Korea.PloS one · 2022Article
- Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic.IEEE internet of things journal · 2021Article
- Rapid review of COVID-19 epidemic estimation studies for Iran.BMC public health · 2021Review
- A comparative study of SIR Model, Linear Regression, Logistic Function and ARIMA Model for forecasting COVID-19 cases.AIMS public health · 2021Article
- Assessing the future progression of COVID-19 in Iran and its neighbors using Bayesian models.Infectious Disease Modelling · 2021Article
- Assessing effects of reopening policies on COVID-19 pandemic in Texas with a data-driven transmission model.Infectious Disease Modelling · 2021Article
- Article
- Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19.PloS one · 2021Article
- Article
- Exponentially Increasing Trend of Infected Patients with COVID-19 in Iran: A Comparison of Neural Network and ARIMA Forecasting Models.Iranian journal of public health · 2020Article
- The relationship between trends in COVID-19 prevalence and traffic levels in South Korea.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2020Article
- Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation.Chaos (Woodbury, N.Y.) · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This work applies a data-driven coding method for prediction of the COVID-19 spreading profile in any given population that shows an initial phase of epidemic progression. Based on the historical data collected for COVID-19 spreading in 367 cities in China and the set of parameters of the augmented Susceptible-Exposed-Infected-Removed (SEIR) model obtained for each city, a set of profile codes representing a variety of transmission mechanisms and contact topologies is formed. By comparing the data of an early outbreak of a given population with the complete set of historical profiles, the best fit profiles are selected and the corresponding sets of profile codes are used for prediction of the future progression of the epidemic in that population. Application of the method to the data collected for South Korea, Italy and Iran shows that peaks of infection cases are expected to occur before mid April, the end of March and the end of May 2020, and that the percentage of population infected in each city or region will be less than 0.01%, 0.5% and 0.5%, for South Korea, Italy and Iran, respectively.
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