preprints_ui: rw5cv_v1
Data license: ODbL (database) & original licenses (content) · Data source: Open Science Framework
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| rw5cv_v1 | COVID-19 Outbreak Prediction with Machine Learning | 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. | 2020-10-06T08:19:46.046026 | 2020-10-08T12:23:01.474334 | 2020-10-08T12:22:33.594553 | arabixiv | 1 | accepted | 1 | 1 | https://doi.org/10.31221/osf.io/rw5cv | GNU Lesser General Public License (LGPL) 3.0 | COVID-19; Coronavirus; Model; Prediction; Time-series; disease; huge data; time series data | ["COVID-19", "Coronavirus", "Model", "Prediction", "Time-series", "disease", "huge data", "time series data"] | Sina Faizollahzadeh Ardabili; Amir Mosavi; Pedram Ghamisi; Filip Ferdinand; Annamaria R. Varkonyi-Koczy; Uwe Reuter; Timon Rabczuk; Peter M. Atkinson | [{"id": "vmtuw", "name": "Sina Faizollahzadeh Ardabili", "index": 0, "orcid": null, "bibliographic": true}, {"id": "rx2k7", "name": "Amir Mosavi", "index": 1, "orcid": "0000-0003-4842-0613", "bibliographic": true}, {"id": "c7kaq", "name": "Pedram Ghamisi", "index": 2, "orcid": null, "bibliographic": true}, {"id": "jzuvh", "name": "Filip Ferdinand", "index": 3, "orcid": null, "bibliographic": true}, {"id": "gyrpk", "name": "Annamaria R. Varkonyi-Koczy", "index": 4, "orcid": null, "bibliographic": true}, {"id": "64z9v", "name": "Uwe Reuter", "index": 5, "orcid": null, "bibliographic": true}, {"id": "tjqm8", "name": "Timon Rabczuk", "index": 6, "orcid": null, "bibliographic": true}, {"id": "ydv5n", "name": "Peter M. Atkinson", "index": 7, "orcid": null, "bibliographic": true}] | Sina Faizollahzadeh Ardabili | Medicine and Health Sciences; Engineering; Health Information Technology; Computational Engineering; Other Medicine and Health Sciences; Diseases; Public Health | [{"id": "5a57d9d0076808000d81526c", "text": "Medicine and Health Sciences"}, {"id": "5a57d9d4076808000d8152e3", "text": "Engineering"}, {"id": "5a57d9d6076808000d81531f", "text": "Health Information Technology"}, {"id": "5a57d9db076808000d815426", "text": "Computational Engineering"}, {"id": "5a57d9dc076808000d81545e", "text": "Other Medicine and Health Sciences"}, {"id": "5a57d9dd076808000d81546f", "text": "Diseases"}, {"id": "5a57d9de076808000d8154a1", "text": "Public Health"}] | https://osf.io/download/5f7c28aecc246f006274c95d | 0 | no | no | [] | 2025-04-09T20:04:14.500628 |