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Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea.
Int J Environ Res Public Health. 2020 05 18; 17(10)IJ

Abstract

The current pandemic of the new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, has received wide attention by scholars and researchers. The vast increase in infected people is a significant challenge for each country and the international community in general. The prediction and forecasting of the number of infected people (so-called confirmed cases) is a critical issue that helps in understanding the fast spread of COVID-19. Therefore, in this article, we present an improved version of the ANFIS (adaptive neuro-fuzzy inference system) model to forecast the number of infected people in four countries, Italy, Iran, Korea, and the USA. The improved version of ANFIS is based on a new nature-inspired optimizer, called the marine predators algorithm (MPA). The MPA is utilized to optimize the ANFIS parameters, enhancing its forecasting performance. Official datasets of the four countries are used to evaluate the proposed MPA-ANFIS. Moreover, we compare MPA-ANFIS to several previous methods to evaluate its forecasting performance. Overall, the outcomes show that MPA-ANFIS outperforms all compared methods in almost all performance measures, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), and Coefficient of Determination(R 2). For instance, according to the results of the testing set, the R 2 of the proposed model is 96.48%, 98.59%, 98.74%, and 95.95% for Korea, Italy, Iran, and the USA, respectively. More so, the MAE is 60.31, 3951.94, 217.27, and 12,979, for Korea, Italy, Iran, and the USA, respectively.

Authors+Show Affiliations

State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.Department of e-Systems, University of Bisha, Bisha 61922, Saudi Arabia. Department of Computer, Damietta University, Damietta 34517, Egypt.State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan.Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

32443476

Citation

Al-Qaness, Mohammed A A., et al. "Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea." International Journal of Environmental Research and Public Health, vol. 17, no. 10, 2020.
Al-Qaness MAA, Ewees AA, Fan H, et al. Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea. Int J Environ Res Public Health. 2020;17(10).
Al-Qaness, M. A. A., Ewees, A. A., Fan, H., Abualigah, L., & Abd Elaziz, M. (2020). Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea. International Journal of Environmental Research and Public Health, 17(10). https://doi.org/10.3390/ijerph17103520
Al-Qaness MAA, et al. Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea. Int J Environ Res Public Health. 2020 05 18;17(10) PubMed PMID: 32443476.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea. AU - Al-Qaness,Mohammed A A, AU - Ewees,Ahmed A, AU - Fan,Hong, AU - Abualigah,Laith, AU - Abd Elaziz,Mohamed, Y1 - 2020/05/18/ PY - 2020/04/09/received PY - 2020/05/06/revised PY - 2020/05/12/accepted PY - 2020/5/24/entrez PY - 2020/5/24/pubmed PY - 2020/6/3/medline KW - ANFIS KW - COVID-19 KW - SARS-CoV-2 KW - forecasting KW - marine predators algorithm (MPA) JF - International journal of environmental research and public health JO - Int J Environ Res Public Health VL - 17 IS - 10 N2 - The current pandemic of the new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, has received wide attention by scholars and researchers. The vast increase in infected people is a significant challenge for each country and the international community in general. The prediction and forecasting of the number of infected people (so-called confirmed cases) is a critical issue that helps in understanding the fast spread of COVID-19. Therefore, in this article, we present an improved version of the ANFIS (adaptive neuro-fuzzy inference system) model to forecast the number of infected people in four countries, Italy, Iran, Korea, and the USA. The improved version of ANFIS is based on a new nature-inspired optimizer, called the marine predators algorithm (MPA). The MPA is utilized to optimize the ANFIS parameters, enhancing its forecasting performance. Official datasets of the four countries are used to evaluate the proposed MPA-ANFIS. Moreover, we compare MPA-ANFIS to several previous methods to evaluate its forecasting performance. Overall, the outcomes show that MPA-ANFIS outperforms all compared methods in almost all performance measures, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), and Coefficient of Determination(R 2). For instance, according to the results of the testing set, the R 2 of the proposed model is 96.48%, 98.59%, 98.74%, and 95.95% for Korea, Italy, Iran, and the USA, respectively. More so, the MAE is 60.31, 3951.94, 217.27, and 12,979, for Korea, Italy, Iran, and the USA, respectively. SN - 1660-4601 UR - https://www.unboundmedicine.com/medline/citation/32443476/Marine_Predators_Algorithm_for_Forecasting_Confirmed_Cases_of_COVID_19_in_Italy_USA_Iran_and_Korea_ L2 - https://www.mdpi.com/resolver?pii=ijerph17103520 DB - PRIME DP - Unbound Medicine ER -