COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

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COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

10.1101/2020.05.02.20088427 ◽

2020 ◽

Author(s):

Gergo Pinter ◽

Imre Felde ◽

Amir Mosavi ◽

Pedram Ghamisi ◽

Richard Gloaguen

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Future Research ◽

Complex Nature ◽

Learning Approach ◽

Epidemiological Models ◽

Inference System ◽

Proper Actions ◽

Machine Learning Approach ◽

Hybrid Machine

AbstractSeveral epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

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COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

10.21203/rs.3.rs-27132/v1 ◽

2020 ◽

Author(s):

Gergo Pinter ◽

Imre Felde ◽

Amir Mosavi ◽

Pedram Ghamisi ◽

Richard Gloaguen

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Future Research ◽

Complex Nature ◽

Learning Approach ◽

Epidemiological Models ◽

Inference System ◽

Proper Actions ◽

Machine Learning Approach ◽

Hybrid Machine

Abstract Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

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COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach

Mathematics ◽

10.3390/math8060890 ◽

2020 ◽

Vol 8 (6) ◽

pp. 890 ◽

Cited By ~ 8

Author(s):

Gergo Pinter ◽

Imre Felde ◽

Amir Mosavi ◽

Pedram Ghamisi ◽

Richard Gloaguen

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Imperialist Competitive Algorithm ◽

Future Research ◽

Learning Approach ◽

Epidemiological Models ◽

Inference System ◽

Proper Actions ◽

Machine Learning Approach ◽

Hybrid Machine

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to the lack of essential data and uncertainty, the epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19, and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are proposed to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for 9 days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

Download Full-text

COVID-19 Outbreak Prediction with Machine Learning

10.34055/osf.io/xr4js ◽

2020 ◽

Author(s):

Sina Faizollahzadeh Ardabili ◽

Amir Mosavi ◽

Pedram Ghamisi ◽

Filip Ferdinand ◽

Annamaria R. Varkonyi-Koczy ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Fuzzy Inference ◽

Control Measures ◽

Future Research ◽

Complex Nature ◽

Inference System ◽

Wide Range ◽

Standard Models ◽

High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

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COVID-19 Outbreak Prediction with Machine Learning

10.35542/osf.io/pzhfj ◽

2020 ◽

Author(s):

Sina Faizollahzadeh Ardabili ◽

Amir Mosavi ◽

Pedram Ghamisi ◽

Filip Ferdinand ◽

Annamaria R. Varkonyi-Koczy ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Fuzzy Inference ◽

Control Measures ◽

Future Research ◽

Complex Nature ◽

Inference System ◽

Wide Range ◽

Standard Models ◽

High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Download Full-text

COVID-19 Outbreak Prediction with Machine Learning

10.21203/rs.3.rs-27130/v1 ◽

2020 ◽

Author(s):

Sina F. Ardabili ◽

Amir Mosavi ◽

Pedram Ghamisi ◽

Filip Ferdinand ◽

Annamaria R. Varkonyi-Koczy ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Fuzzy Inference ◽

Control Measures ◽

Future Research ◽

Complex Nature ◽

Inference System ◽

Wide Range ◽

Standard Models ◽

High Level

Abstract Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible-infected-recovered (SIR) and susceptible-exposed-infectious-recovered (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Download Full-text

COVID-19 Outbreak Prediction with Machine Learning

10.31219/osf.io/zat7w ◽

2020 ◽

Author(s):

Sina Faizollahzadeh Ardabili ◽

Amir Mosavi ◽

Pedram Ghamisi ◽

Filip Ferdinand ◽

Annamaria R. Varkonyi-Koczy ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Fuzzy Inference ◽

Control Measures ◽

Future Research ◽

Complex Nature ◽

Inference System ◽

Wide Range ◽

Standard Models ◽

High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Download Full-text

COVID-19 Outbreak Prediction with Machine Learning

10.31224/osf.io/tn3wz ◽

2020 ◽

Author(s):

Sina Faizollahzadeh Ardabili ◽

Amir Mosavi ◽

Pedram Ghamisi ◽

Filip Ferdinand ◽

Annamaria R. Varkonyi-Koczy ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Fuzzy Inference ◽

Control Measures ◽

Future Research ◽

Complex Nature ◽

Inference System ◽

Wide Range ◽

Standard Models ◽

High Level

: Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Download Full-text

COVID-19 Outbreak Prediction with Machine Learning

10.31235/osf.io/37b8f ◽

2020 ◽

Author(s):

Sina Faizollahzadeh Ardabili ◽

Amir Mosavi ◽

Pedram Ghamisi ◽

Filip Ferdinand ◽

Annamaria R. Varkonyi-Koczy ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Fuzzy Inference ◽

Control Measures ◽

Future Research ◽

Complex Nature ◽

Inference System ◽

Wide Range ◽

Standard Models ◽

High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Download Full-text