Optimized Forecasting Method for Weekly Influenza Confirmed Cases
Abstract
:1. Introduction
- We propose an optimized ANFIS model for forecasting weekly influenza confirmed cases using FPA and SCA.
- Data of two different countries, namely, China and the USA, were used to assess the proposed FPASCA-ANFIS.
- Extensive comparisons were implemented to evaluate the performance of the FPASCA-ANFIS. The comparative outcomes approved the efficiency of the proposed method.
2. Preliminaries
2.1. ANFIS
2.2. SCA
2.3. FPA
3. The Proposed FPASCA-ANFIS
4. Experimental Evaluation
4.1. Dataset Description
4.2. Performance Measures
- Mean Absolute Error (MAE):
- Mean Absolute Percentage Error (MAPE):represent the predicted value, and Y represent the real value.
- Root Mean Squared Relative Error (RMSRE):
- Root Mean Square Error (RMSE):
- Coefficient of Determination ():
4.3. Results and Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Radin, J.M.; Wineinger, N.E.; Topol, E.J.; Steinhubl, S.R. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: A population-based study. Lancet Digit. Health 2020, 2, e85–e93. [Google Scholar] [CrossRef] [Green Version]
- Yang, S.; Santillana, M.; Brownstein, J.S.; Gray, J.; Richardson, S.; Kou, S. Using electronic health records and Internet search information for accurate influenza forecasting. BMC Infect. Dis. 2017, 17, 332. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Moss, R.; Fielding, J.; Franklin, L.; Stephens, N.; Mcvernon, J.; Dawson, P.; Mccaw, J. Epidemic forecasts as a tool for public health: Interpretation and (re)calibration. Aust. N. Z. J. Public Health 2017, 42, 69–76. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pei, S.; Kandula, S.; Yang, W.; Shaman, J. Forecasting the spatial transmission of influenza in the United States. Proc. Nat. Acad. Sci. USA 2018, 115, 2752–2757. [Google Scholar] [CrossRef] [Green Version]
- Senanayake, R.; O’Callaghan, S.; Ramos, F. Predicting spatio-temporal propagation of seasonal influenza using variational Gaussian process regression. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA, 12–17 February 2016. [Google Scholar]
- Achrekar, H.; Gandhe, A.; Lazarus, R.; Yu, S.H.; Liu, B. Predicting Flu Trends using Twitter Data. In Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Shanghai, China, 10–15 April 2011; pp. 702–707. [Google Scholar] [CrossRef] [Green Version]
- Alkouz, B.; Al Aghbari, Z.; Abawajy, J.H. Tweetluenza: Predicting flu trends from twitter data. Big Data Min. Anal. 2019, 2, 248–273. [Google Scholar] [CrossRef] [Green Version]
- Wang, Y.; Xu, K.; Kang, Y.; Wang, H.; Wang, F.; Avram, A. Regional Influenza Prediction with Sampling Twitter Data and PDE Model. Int. J. Environ. Res. Public Health 2020, 17, 678. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Morita, H.; Kramer, S.; Heaney, A.; Gil, H.; Shaman, J. Influenza forecast optimization when using different surveillance data types and geographic scale. Influenza Respir. Viruses 2018, 12, 755–764. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Santillana, M.; Nguyen, A.T.; Dredze, M.; Paul, M.J.; Nsoesie, E.O.; Brownstein, J.S. Combining search, social media, and traditional data sources to improve influenza surveillance. PLoS Comput. Biol. 2015, 11, e1004513. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shaman, J.; Karspeck, A. Forecasting seasonal outbreaks of influenza. Proc. Nat. Acad. Sci. USA 2012, 109, 20425–20430. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shaman, J.; Karspeck, A.; Yang, W.; Tamerius, J.; Lipsitch, M. Real-time influenza forecasts during the 2012–2013 season. Nat. Commun. 2013, 4, 2837. [Google Scholar] [CrossRef]
- Yang, W.; Olson, D.R.; Shaman, J. Forecasting influenza outbreaks in boroughs and neighborhoods of New York City. PLoS Comput. Biol. 2016, 12, e1005201. [Google Scholar] [CrossRef] [PubMed]
- Gao, H.; Wong, K.K.; Zheteyeva, Y.; Shi, J.; Uzicanin, A.; Rainey, J.J. Comparing observed with predicted weekly influenza-like illness rates during the winter holiday break, united states, 2004–2013. PLoS ONE 2015, 10, e0143791. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Volkova, S.; Ayton, E.; Porterfield, K.; Corley, C.D. Forecasting influenza-like illness dynamics for military populations using neural networks and social media. PLoS ONE 2017, 12, e0188941. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cao, P.H.; Wang, X.; Fang, S.S.; Cheng, X.W.; Chan, K.P.; Wang, X.L.; Lu, X.; Wu, C.L.; Tang, X.J.; Zhang, R.L.; et al. Forecasting influenza epidemics from multi-stream surveillance data in a subtropical city of China. PLoS ONE 2014, 9, e92945. [Google Scholar] [CrossRef]
- Jang, J.S. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 1993, 23, 665–685. [Google Scholar] [CrossRef]
- Wei, L.Y. A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Appl. Soft Comput. 2016, 42, 368–376. [Google Scholar] [CrossRef]
- Pousinho, H.M.I.; Mendes, V.; Catalão, J.P.D.S. Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach. Int. J. Electr. Power Energy Syst. 2012, 39, 29–35. [Google Scholar] [CrossRef] [Green Version]
- Svalina, I.; Galzina, V.; Lujić, R.; ŠImunović, G. An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices. Expert Syst. Appl. 2013, 40, 6055–6063. [Google Scholar] [CrossRef]
- Ekici, B.B.; Aksoy, U.T. Prediction of building energy needs in early stage of design by using ANFIS. Expert Syst. Appl. 2011, 38, 5352–5358. [Google Scholar] [CrossRef]
- Ho, Y.C.; Tsai, C.T. Comparing ANFIS and SEM in linear and nonlinear forecasting of new product development performance. Expert Syst. Appl. 2011, 38, 6498–6507. [Google Scholar] [CrossRef]
- Kumar, D.T.; Soleimani, H.; Kannan, G. Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS. Int. J. Appl. Math. Comput. Sci. 2014, 24, 669–682. [Google Scholar] [CrossRef] [Green Version]
- Shirmohammadi, B.; Moradi, B.; Moosavi, V.; Taie Semiromi, M.; Zeinali, A. Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: Southeastern part of east Azerbaijan province, Iran). Nat. Hazards 2013, 69, 389–402. [Google Scholar] [CrossRef]
- Barak, S.; Sadegh, S.S. Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm. Int. J. Electr. Power Energy Syst. 2016, 82, 92–104. [Google Scholar] [CrossRef] [Green Version]
- Al-Qaness, M.A.; Elaziz, M.A.; Ewees, A.A. Oil consumption forecasting using optimized adaptive neuro-fuzzy inference system based on sine cosine algorithm. IEEE Access 2018, 6, 68394–68402. [Google Scholar] [CrossRef]
- El Aziz, M.A.; Hemdan, A.M.; Ewees, A.A.; Elhoseny, M.; Shehab, A.; Hassanien, A.E.; Xiong, S. Prediction of biochar yield using adaptive neuro-fuzzy inference system with particle swarm optimization. In Proceedings of the 2017 IEEE PES PowerAfrica, Accra, Ghana, 27–30 June 2017; pp. 115–120. [Google Scholar]
- Catalão, J.P.D.S.; Pousinho, H.M.I.; Mendes, V.M.F. Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting. IEEE Trans. Power Syst. 2010, 26, 137–144. [Google Scholar] [CrossRef]
- Bagheri, A.; Peyhani, H.M.; Akbari, M. Financial forecasting using ANFIS networks with quantum-behaved particle swarm optimization. Expert Syst. Appl. 2014, 41, 6235–6250. [Google Scholar] [CrossRef]
- Al-qaness, M.A.; Abd Elaziz, M.; Ewees, A.A.; Cui, X. A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting. Electronics 2019, 8, 1071. [Google Scholar] [CrossRef] [Green Version]
- Elaziz, M.A.; Ewees, A.A.; Alameer, Z. Improving adaptive neuro-fuzzy inference system based on a modified salp swarm algorithm using genetic algorithm to forecast crude oil price. Nat. Resour. Res. 2019, 1–16. [Google Scholar] [CrossRef]
- Ewees, A.A.; El Aziz, M.A.; Elhoseny, M. Social-spider optimization algorithm for improving ANFIS to predict biochar yield. In Proceedings of the 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, India, 3–5 July 2017; pp. 1–6. [Google Scholar]
- Yang, X.S. Flower pollination algorithm for global optimization. In Proceedings of the International Conference on Unconventional Computing and Natural Computation, Orléans, France, 3–7 September 2012; pp. 240–249. [Google Scholar]
- Salgotra, R.; Singh, U.; Saha, S.; Nagar, A.K. Improved Flower Pollination Algorithm for Linear Antenna Design Problems. In Soft Computing for Problem Solving; Springer: Singapore, 2020; pp. 79–89. [Google Scholar]
- Rodrigues, D.; Yang, X.S.; De Souza, A.N.; Papa, J.P. Binary flower pollination algorithm and its application to feature selection. In Recent Advances in Swarm Intelligence and Evolutionary Computation; Springer: Cham, Switzerland, 2015; pp. 85–100. [Google Scholar]
- Alam, D.; Yousri, D.; Eteiba, M. Flower pollination algorithm based solar PV parameter estimation. Energy Convers. Manag. 2015, 101, 410–422. [Google Scholar] [CrossRef]
- Ram, J.P.; Babu, T.S.; Dragicevic, T.; Rajasekar, N. A new hybrid bee pollinator flower pollination algorithm for solar PV parameter estimation. Energy Convers. Manag. 2017, 135, 463–476. [Google Scholar] [CrossRef]
- Abdel-Raouf, O.; El-Henawy, I.; Abdel-Baset, M. A novel hybrid flower pollination algorithm with chaotic harmony search for solving sudoku puzzles. Int. J. Mod. Educ. Comput. Sci. 2014, 6, 38. [Google Scholar] [CrossRef]
- Mirjalili, S. SCA: A sine cosine algorithm for solving optimization problems. Knowl. Based Syst. 2016, 96, 120–133. [Google Scholar] [CrossRef]
- Jouhari, H.; Lei, D.; Al-qaness, M.A.; Elaziz, M.A.; Ewees, A.A.; Farouk, O. Sine-Cosine Algorithm to Enhance Simulated Annealing for Unrelated Parallel Machine Scheduling with Setup Times. Mathematics 2019, 7, 1120. [Google Scholar] [CrossRef] [Green Version]
- Ewees, A.A.; Elaziz, M.A.; Al-Qaness, M.A.; Khalil, H.A.; Kim, S. Improved Artificial Bee Colony Using Sine-Cosine Algorithm for Multi-Level Thresholding Image Segmentation. IEEE Access 2020, 8, 26304–26315. [Google Scholar] [CrossRef]
- Elaziz, M.A.; Oliva, D.; Xiong, S. An improved opposition-based sine cosine algorithm for global optimization. Expert Syst. Appl. 2017, 90, 484–500. [Google Scholar] [CrossRef]
- Centers for Disease Control and Prevention (CDC). Weekly Influenza Case Datasets. 2020. Available online: https://www.cdc.gov/flu/weekly/ (accessed on 5 February 2020).
- World Health Organization. Influenza. 2020. Available online: https://www.who.int/influenza (accessed on 5 February 2020).
- Ahmed, K.; Ewees, A.A.; El Aziz, M.A.; Hassanien, A.E.; Gaber, T.; Tsai, P.W.; Pan, J.S. A hybrid krill-ANFIS model for wind speed forecasting. In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, Cairo, Egypt, 24–26 October 2016; pp. 365–372. [Google Scholar]
- Alameer, Z.; Elaziz, M.A.; Ewees, A.A.; Ye, H.; Jianhua, Z. Forecasting copper prices using hybrid adaptive neuro-fuzzy inference system and genetic algorithms. Nat. Resour. Res. 2019, 28, 1385–1401. [Google Scholar] [CrossRef]
- Wang, B.; Luo, X.; Zhang, F.; Yuan, B.; Bertozzi, A.L.; Brantingham, P.J. Graph-based deep modeling and real time forecasting of sparse spatio-temporal data. arXiv 2018, arXiv:1804.00684. [Google Scholar]
- Mohler, G.O.; Short, M.B.; Brantingham, P.J.; Schoenberg, F.P.; Tita, G.E. Self-exciting point process modeling of crime. J. Am. Stat. Assoc. 2011, 106, 100–108. [Google Scholar] [CrossRef]
- Mohler, G.O.; Short, M.B.; Malinowski, S.; Johnson, M.; Tita, G.E.; Bertozzi, A.L.; Brantingham, P.J. Randomized controlled field trials of predictive policing. J. Am. Stat. Assoc. 2015, 110, 1399–1411. [Google Scholar] [CrossRef] [Green Version]
Method | MAE | RMSE | MAPE | R2 | RMSRE | Time |
---|---|---|---|---|---|---|
ARIMA | 872 | 1689 | 60.77 | 0.888 | 1.080 | - |
SARIMA | 877 | 1740 | 54.79 | 0.870 | 0.779 | - |
LSTM | 436 | 816 | 27.78 | 0.972 | 0.354 | - |
ANFIS | 570 | 952 | 37.61 | 0.969 | 0.551 | - |
PSO-ANFIS | 494 | 798 | 34.13 | 0.978 | 0.510 | 25.43 |
GA-ANFIS | 480 | 766 | 35.44 | 0.98 | 0.53 | 28.74 |
ABC-ANFIS | 564 | 878 | 39.79 | 0.972 | 0.593 | 49.27 |
FPA-ANFIS | 411 | 618 | 37.69 | 0.979 | 0.570 | 24.58 |
FPASCA-ANFIS | 405 | 611 | 33.64 | 0.981 | 0.501 | 25.01 |
Method | MAE | RMSE | MAPE | R2 | RMSRE | Time |
---|---|---|---|---|---|---|
ARIMA | 606 | 962 | 57.02 | 0.914 | 0.899 | - |
SARIMA | 620 | 981 | 40.43 | 0.748 | 0.794 | - |
LSTM | 398 | 623 | 21.31 | 0.901 | 0.490 | - |
ANFIS | 405 | 718 | 64.21 | 0.858 | 1.198 | - |
PSO-ANFIS | 353 | 620 | 52.07 | 0.892 | 0.871 | 31.64 |
GA-ANFIS | 362 | 622 | 87.91 | 0.902 | 3.216 | 34.83 |
ABC-ANFIS | 433 | 696 | 53.3 | 0.887 | 1.101 | 60.87 |
FPA-ANFIS | 371 | 622 | 80.55 | 0.898 | 3.152 | 30.42 |
FPASCA-ANFIS | 362 | 606 | 39.58 | 0.906 | 0.743 | 25.58 |
Dataset | ANFIS | PSO | GA | ABC | FPA | ARIMA | SARIMA | LSTM |
---|---|---|---|---|---|---|---|---|
USA | 0.000 | 0.000 | 0.007 | 0.000 | 0.152 | 0.000 | 0.000 | 0.029 |
China | 0.000 | 0.105 | 0.001 | 0.016 | 0.124 | 0.000 | 0.000 | 0.145 |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Al-qaness, M.A.A.; Ewees, A.A.; Fan, H.; Abd Elaziz, M. Optimized Forecasting Method for Weekly Influenza Confirmed Cases. Int. J. Environ. Res. Public Health 2020, 17, 3510. https://doi.org/10.3390/ijerph17103510
Al-qaness MAA, Ewees AA, Fan H, Abd Elaziz M. Optimized Forecasting Method for Weekly Influenza Confirmed Cases. International Journal of Environmental Research and Public Health. 2020; 17(10):3510. https://doi.org/10.3390/ijerph17103510
Chicago/Turabian StyleAl-qaness, Mohammed A. A., Ahmed A. Ewees, Hong Fan, and Mohamed Abd Elaziz. 2020. "Optimized Forecasting Method for Weekly Influenza Confirmed Cases" International Journal of Environmental Research and Public Health 17, no. 10: 3510. https://doi.org/10.3390/ijerph17103510