The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID19 Pandemic
Autor:
James Fong, Simon
; Lobo Marques, João Alexandre
; Li, G.
; Dey, Nilanjan
; González-Crespo, Rubén
; Herrera-Viedma, Enrique
; Bernardo Gois, F. Nauber
; Xavier Neto, José
Fecha:
2022Palabra clave:
Revista / editorial:
Epidemic Analytics for Decision Supports in COVID19 CrisisCitación:
Fong, S.J. et al. (2022). The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID19 Pandemic. In: Marques, J.A.L., Fong, S.J. (eds) Epidemic Analytics for Decision Supports in COVID19 Crisis. Springer, Cham. https://doi.org/10.1007/978-3-030-95281-5_5Tipo de Ítem:
bookPartResumen:
The application of different tools for predicting COVID19 cases spreading has been widely considered during the pandemic. Comparing different approaches is essential to analyze performance and the practical support they can provide for the current pandemic management. This work proposes using the susceptible-exposed-asymptomatic but infectious-symptomatic and infectious-recovered-deceased (SEAIRD) model for different learning models. The first analysis considers an unsupervised prediction, based directly on the epidemiologic compartmental model. After that, two supervised learning models are considered integrating computational intelligence techniques and control engineering: the fuzzy-PID and the wavelet-ANN-PID models. The purpose is to compare different predictor strategies to validate a viable predictive control system for the COVID19 relevant epidemiologic time series. For each model, after setting the initial conditions for each parameter, the prediction performance is calculated based on the presented data. The use of PID controllers is justified to avoid divergence in the system when the learning process is conducted. The wavelet neural network solution is considered here because of its rapid convergence rate. The proposed solutions are dynamic and can be adjusted and corrected in real time, according to the output error. The results are presented in each subsection of the chapter.
Este ítem aparece en la(s) siguiente(s) colección(es)
Estadísticas de uso
Año |
2012 |
2013 |
2014 |
2015 |
2016 |
2017 |
2018 |
2019 |
2020 |
2021 |
2022 |
2023 |
2024 |
Vistas |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
18 |
73 |
Descargas |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
Ítems relacionados
Mostrando ítems relacionados por Título, autor o materia.
-
Analysis of the COVID19 Pandemic Behaviour Based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models
James Fong, Simon; Lobo Marques, João Alexandre; Li, G.; Dey, Nilanjan; González-Crespo, Rubén; Herrera-Viedma, Enrique; Bernardo Gois, F. Nauber; Xavier Neto, José (Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)A significant number of people infected by COVID19 do not get sick immediately but become carriers of the disease. These patients might have a certain incubation period. However, the classical compartmental model, SEIR, ... -
The Comparison of Different Linear and Nonlinear Models Using Preliminary Data to Efficiently Analyze the COVID-19 Outbreak
James Fong, Simon; Lobo Marques, João Alexandre; Li, G.; Dey, Nilanjan; González-Crespo, Rubén; Herrera-Viedma, Enrique; Bernardo Gois, F. Nauber; Xavier Neto, José (Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)The COVID-19 pandemic spread generated an urgent need for computational systems to model its behavior and support governments and healthcare teams to make proper decisions. There are not many cases of global pandemics in ... -
Probabilistic Forecasting Model for the COVID-19 Pandemic Based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System
James Fong, Simon; Lobo Marques, João Alexandre; Li, G.; Dey, Nilanjan; González-Crespo, Rubén; Herrera-Viedma, Enrique; Bernardo Gois, F. Nauber; Xavier Neto, José (Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)There are several techniques to support simulation of time series behavior. In this chapter, the approach will be based on the Composite Monte Carlo (CMC) simulation method. This method is able to model future outcomes of ...