Optimized Forecasting Method for Weekly Influenza Confirmed Cases

Influenza epidemic is a serious threat to the entire world, which causes thousands of death every year and can be considered as a public health emergency that needs to be more addressed and investigated. Forecasting influenza incidences or confirmed cases is very important to do the necessary policies and plans for governments and health organizations. In this paper, we present an enhanced adaptive neuro-fuzzy inference system (ANFIS) to forecast the weekly confirmed influenza cases in China and the USA using official datasets. To overcome the limitations of the original ANFIS, we use two metaheuristics, called flower pollination algorithm (FPA) and sine cosine algorithm (SCA), to enhance the prediction of the ANFIS. The proposed FPASCA-ANFIS is evaluated using two datasets collected from the CDC and WHO websites. Furthermore, it was compared to some previous state-of-the-art approaches. Experimental results confirmed that the FPASCA-ANFIS outperformed the compared methods using variant measures, including RMSRE, MAPE, MAE, and R2.

[1]  Mohamed Elhoseny,et al.  Prediction of biochar yield using adaptive neuro-fuzzy inference system with particle swarm optimization , 2017, 2017 IEEE PES PowerAfrica.

[2]  Mohsen Akbari,et al.  Financial forecasting using ANFIS networks with Quantum-behaved Particle Swarm Optimization , 2014, Expert Syst. Appl..

[3]  Seyedali Mirjalili,et al.  SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..

[4]  Mohamed Elhoseny,et al.  Social-spider optimization algorithm for improving ANFIS to predict biochar yield , 2017, 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT).

[5]  Vjekoslav Galzina,et al.  An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices , 2013, Expert Syst. Appl..

[6]  Sen Pei,et al.  Forecasting the spatial transmission of influenza in the United States , 2018, Proceedings of the National Academy of Sciences.

[7]  Peter Dawson,et al.  Epidemic forecasts as a tool for public health: interpretation and (re)calibration , 2018, Australian and New Zealand journal of public health.

[8]  Mustafa Turkmen,et al.  ADAPTIVE-NETWORK-BASED FUZZY INFERENCE SYSTEM MODELS FOR COMPUTING THE CHARACTERISTIC IMPEDANCES OF AIR-SUSPENDED TRAPEZOIDAL AND RECTANGULAR-SHAPED MICROSHIELD LINES , 2010 .

[9]  Fabio Tozeto Ramos,et al.  Predicting Spatio-Temporal Propagation of Seasonal Influenza Using Variational Gaussian Process Regression , 2016, AAAI.

[10]  Liang-Ying Wei,et al.  A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting , 2016, Appl. Soft Comput..

[11]  Jeng-Shyang Pan,et al.  A Hybrid Krill-ANFIS Model for Wind Speed Forecasting , 2016, AISI.

[12]  J. Shaman,et al.  Forecasting seasonal outbreaks of influenza , 2012, Proceedings of the National Academy of Sciences.

[13]  Mohamed Abdel-Baset,et al.  A Novel Hybrid Flower Pollination Algorithm with Chaotic Harmony Search for Solving Sudoku Puzzles , 2014 .

[14]  Eric J Topol,et al.  Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. , 2020, The Lancet. Digital health.

[15]  Alicia Karspeck,et al.  Real-Time Influenza Forecasts during the 2012–2013 Season , 2013, Nature Communications.

[16]  Ellyn Ayton,et al.  Forecasting influenza-like illness dynamics for military populations using neural networks and social media , 2017, PloS one.

[17]  Sriparna Saha,et al.  Improved Flower Pollination Algorithm for Linear Antenna Design Problems , 2019, SocProS.

[18]  Joao P. S. Catalao,et al.  Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach , 2012 .

[19]  Mohamed Abd Elaziz,et al.  Sine-Cosine Algorithm to Enhance Simulated Annealing for Unrelated Parallel Machine Scheduling with Setup Times , 2019, Mathematics.

[20]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[21]  Andrea L. Bertozzi,et al.  Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data , 2018, ArXiv.

[22]  Bagher Shirmohammadi,et al.  Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran) , 2013, Natural Hazards.

[23]  Ching-Tzu Tsai,et al.  Comparing ANFIS and SEM in linear and nonlinear forecasting of new product development performance , 2011, Expert Syst. Appl..

[24]  Benyuan Liu,et al.  Predicting Flu Trends using Twitter data , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[25]  Jemal H. Abawajy,et al.  Tweetluenza: Predicting flu trends from twitter data , 2019, Big Data Min. Anal..

[26]  V. M. F. Mendes,et al.  Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting , 2011, 2011 IEEE Power and Energy Society General Meeting.

[27]  Jeffrey Shaman,et al.  Forecasting Influenza Outbreaks in Boroughs and Neighborhoods of New York City , 2016, PLoS Comput. Biol..

[28]  John S. Brownstein,et al.  Using electronic health records and Internet search information for accurate influenza forecasting , 2017, BMC Infectious Diseases.

[29]  N. Rajasekar,et al.  A new hybrid bee pollinator flower pollination algorithm for solar PV parameter estimation , 2017 .

[30]  Kannan Govindan,et al.  Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS , 2014, Int. J. Appl. Math. Comput. Sci..

[31]  Zhang Jianhua,et al.  Forecasting Copper Prices Using Hybrid Adaptive Neuro-Fuzzy Inference System and Genetic Algorithms , 2019, Natural Resources Research.

[32]  Diego Oliva,et al.  An improved Opposition-Based Sine Cosine Algorithm for global optimization , 2017, Expert Syst. Appl..

[33]  Mohamed Abd Elaziz,et al.  A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting , 2019, Electronics.

[34]  Betul Bektas Ekici,et al.  Prediction of building energy needs in early stage of design by using ANFIS , 2011, Expert Syst. Appl..

[35]  Yun Kang,et al.  Regional Influenza Prediction with Sampling Twitter Data and PDE Model , 2020, International journal of environmental research and public health.

[36]  Dalia Yousri,et al.  Flower Pollination Algorithm based solar PV parameter estimation , 2015 .

[37]  J. Rainey,et al.  Comparing Observed with Predicted Weekly Influenza-Like Illness Rates during the Winter Holiday Break, United States, 2004-2013 , 2015, PloS one.

[38]  Osama Abdel Raouf,et al.  A Novel Hybrid Flower Pollination Algorithm with Chaotic Harmony Search for Solving Sudoku Puzzles , 2014 .

[39]  George E. Tita,et al.  Self-Exciting Point Process Modeling of Crime , 2011 .

[40]  Lin Yang,et al.  Forecasting Influenza Epidemics from Multi-Stream Surveillance Data in a Subtropical City of China , 2014, PloS one.

[41]  Sunghwan Kim,et al.  Improved Artificial Bee Colony Using Sine-Cosine Algorithm for Multi-Level Thresholding Image Segmentation , 2020, IEEE Access.

[42]  Haruka Morita,et al.  Influenza forecast optimization when using different surveillance data types and geographic scale , 2018, Influenza and other respiratory viruses.

[43]  Mohammed A. A. Al-qaness,et al.  Oil Consumption Forecasting Using Optimized Adaptive Neuro-Fuzzy Inference System Based on Sine Cosine Algorithm , 2018, IEEE Access.

[44]  Andrea L. Bertozzi,et al.  Randomized Controlled Field Trials of Predictive Policing , 2015 .

[45]  Xin-She Yang,et al.  Binary Flower Pollination Algorithm and Its Application to Feature Selection , 2015, Recent Advances in Swarm Intelligence and Evolutionary Computation.

[46]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[47]  Mark Dredze,et al.  Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance , 2015, PLoS Comput. Biol..

[48]  S. Saeedeh Sadegh,et al.  Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm , 2016 .

[49]  Ahmed A. Ewees,et al.  Improving Adaptive Neuro-Fuzzy Inference System Based on a Modified Salp Swarm Algorithm Using Genetic Algorithm to Forecast Crude Oil Price , 2019, Natural Resources Research.