COVID-19 contagion forecasting framework based on curve decomposition and evolutionary artificial neural networks: A case study in Andalusia, Spain

Many types of research have been carried out with the aim of combating the COVID-19 pandemic since the first outbreak was detected in Wuhan, China. Anticipating the evolution of an outbreak helps to devise suitable economic, social and health care strategies to mitigate the effects of the virus. For...

Full description

Bibliographic Details
Main Authors: Díaz-Lozano, M. (Author), Gómez-Orellana, A.M (Author), Guijo-Rubio, D. (Author), Gutiérrez, P.A (Author), Hervás-Martínez, C. (Author), Ortigosa-Moreno, L. (Author), Padillo-Ruiz, J. (Author), Romanos-Rodríguez, A. (Author), Túñez, I. (Author)
Format: Article
Language:English
Published: Elsevier Ltd 2022
Subjects:
Online Access:View Fulltext in Publisher
LEADER 02874nam a2200433Ia 4500
001 10.1016-j.eswa.2022.117977
008 220718s2022 CNT 000 0 und d
020 |a 09574174 (ISSN) 
245 1 0 |a COVID-19 contagion forecasting framework based on curve decomposition and evolutionary artificial neural networks: A case study in Andalusia, Spain 
260 0 |b Elsevier Ltd  |c 2022 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1016/j.eswa.2022.117977 
520 3 |a Many types of research have been carried out with the aim of combating the COVID-19 pandemic since the first outbreak was detected in Wuhan, China. Anticipating the evolution of an outbreak helps to devise suitable economic, social and health care strategies to mitigate the effects of the virus. For this reason, predicting the SARS-CoV-2 transmission rate has become one of the most important and challenging problems of the past months. In this paper, we apply a two-stage mid and long-term forecasting framework to the epidemic situation in eight districts of Andalusia, Spain. First, an analytical procedure is performed iteratively to fit polynomial curves to the cumulative curve of contagions. Then, the extracted information is used for estimating the parameters and structure of an evolutionary artificial neural network with hybrid architectures (i.e., with different basis functions for the hidden nodes) while considering single and simultaneous time horizon estimations. The results obtained demonstrate that including polynomial information extracted during the training stage significantly improves the mid- and long-term estimations in seven of the eight considered districts. The increase in average accuracy (for the joint mid- and long-term horizon forecasts) is 37.61% and 35.53% when considering the single and simultaneous forecast approaches, respectively. © 2022 Elsevier Ltd 
650 0 4 |a Analytical procedure 
650 0 4 |a Andalusia 
650 0 4 |a Case-studies 
650 0 4 |a COVID-19 
650 0 4 |a COVID-19 contagion forecasting 
650 0 4 |a Curve decomposition 
650 0 4 |a Curves decomposition 
650 0 4 |a Evolutionary artificial neural networks 
650 0 4 |a Forecasting 
650 0 4 |a Long-term forecasting 
650 0 4 |a Neural networks 
650 0 4 |a Polynomial approximation 
650 0 4 |a Polynomial curve 
650 0 4 |a Time series 
650 0 4 |a Times series 
650 0 4 |a Transmission rates 
650 0 4 |a Viruses 
700 1 |a Díaz-Lozano, M.  |e author 
700 1 |a Gómez-Orellana, A.M.  |e author 
700 1 |a Guijo-Rubio, D.  |e author 
700 1 |a Gutiérrez, P.A.  |e author 
700 1 |a Hervás-Martínez, C.  |e author 
700 1 |a Ortigosa-Moreno, L.  |e author 
700 1 |a Padillo-Ruiz, J.  |e author 
700 1 |a Romanos-Rodríguez, A.  |e author 
700 1 |a Túñez, I.  |e author 
773 |t Expert Systems with Applications