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 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.

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

10.31219/osf.io/rtbym ◽

2020 ◽

Author(s):

Amir Mosavi

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Future Research ◽

Complex Nature ◽

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 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

10.20944/preprints202005.0031.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

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.

Download Full-text

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.

Download Full-text

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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Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

10.31234/osf.io/eqcdx ◽

2019 ◽

Author(s):

Oskar Flygare ◽

Jesper Enander ◽

Erik Andersson ◽

Brjánn Ljótsson ◽

Volen Z Ivanov ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forests ◽

Clinical Utility ◽

Body Dysmorphic Disorder ◽

Prediction Models ◽

Behavioral Therapy ◽

Learning Approach ◽

Learning Approaches ◽

Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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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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