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

Research areas:

• Uncategorized

Year:

2022

Type of Publication:

Article

Keywords:

COVID-19 contagion forecasting, Curve decomposition, Evolutionary artificial neural networks, Time series

Authors:

• Díaz-Lozano, Miguel
• Guijo-Rubio, David
• Gutiérrez, Pedro Antonio
• Gómez-Orellana, Antonio Manuel
• Túñez, Isaac
• Ortigosa-Moreno, Luis
• Romanos-Rodríguez, Armando
• Padillo-Ruiz, Javier
• Hervás-Martínez, César

Journal:

Expert Systems with Applications

Volume:

207

Pages:

117977

Month:

June

ISSN:

0957-4174

BibTex:

@article{COVID-19-forecasting-framework, author = "Miguel D{\'i}az-Lozano and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Antonio Manuel G{\'o}mez-Orellana and Isaac T{\'u}{\~n}ez and Luis Ortigosa-Moreno and Armando Romanos-Rodr{\'i}guez and Javier Padillo-Ruiz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "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.", awards = "JCR(2022): 8.5, Position: 23/275 (Q1) Category: ENGINEERING, ELECTRICAL {\&}amp; ELECTRONIC", comments = "JCR(2022): 8.5, Position: 23/275 (Q1) Category: ENGINEERING, ELECTRICAL {\&}amp; ELECTRONIC", doi = "10.1016/j.eswa.2022.117977", issn = "0957-4174", journal = "Expert Systems with Applications", keywords = "COVID-19 contagion forecasting, Curve decomposition, Evolutionary artificial neural networks, Time series", month = "June", note = "JCR(2022): 8.5, Position: 23/275 (Q1) Category: ENGINEERING, ELECTRICAL {\&}amp; ELECTRONIC", pages = "117977", title = "{COVID}-19 contagion forecasting framework based on curve decomposition and evolutionary artificial neural networks: {A} case study in {A}ndalusia, {S}pain", url = "doi.org/10.1016/j.eswa.2022.117977", volume = "207", year = "2022", }

Note:

JCR(2022): 8.5, Position: 23/275 (Q1) Category: ENGINEERING, ELECTRICAL & ELECTRONIC

Abstract:

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.

Comments:

JCR(2022): 8.5, Position: 23/275 (Q1) Category: ENGINEERING, ELECTRICAL & ELECTRONIC