Modeling of Covid-19 Transmission Using Machine Learning
2021 International Conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2021
; 20:530-538, 2022.
Article
in English
| Scopus | ID: covidwho-1753331
ABSTRACT
A susceptible-infected-susceptible (SIS) model with a nonlinear infection rate, a forecast model based on autoregressive integrated moving average (ARIMA), and a forecast model based on long short-term memory (LSTM) artificial neural networks were developed using the COVID-19 epidemic data from four countries (China, Italy, the United Kingdom, Germany, France, and Poland) to simulate and forecast the epidemic trends in these countries. The models were compared in terms of forecast errors, and the LSTM model was found to forecast virus transmission very well. © 2022 The authors and IOS Press.
ARIMA; COVID-19; epidemic forecast; LSTM; SIS model; Epidemiology; Long short-term memory; Transmissions; Viruses; Auto-regressive; Autoregressive integrated moving average; Forecast models; Machine-learning; Model-based OPC; Moving averages; Nonlinear infection rates; Susceptible-infected-susceptible model; Forecasting
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2021 International Conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2021
Year:
2022
Document Type:
Article
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