Using Sequence Mining to Predict Complex Systems: A Case Study in Influenza Epidemics
According to the World Health Organisation, three to five million individuals are infected by influenza, and around 250,000 to 500,000 people die of this infectious disease worldwide. Influenza epidemics pose a serious public health threat. Moreover, graver dangers are encountered with influenza sub...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9929013 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832559997094985728 |
|---|---|
| author | Theyazn H. H. Aldhyani Manish R. Joshi Shahab A. AlMaaytah Ahmed Abdullah Alqarni Nizar Alsharif |
| author_facet | Theyazn H. H. Aldhyani Manish R. Joshi Shahab A. AlMaaytah Ahmed Abdullah Alqarni Nizar Alsharif |
| author_sort | Theyazn H. H. Aldhyani |
| collection | DOAJ |
| description | According to the World Health Organisation, three to five million individuals are infected by influenza, and around 250,000 to 500,000 people die of this infectious disease worldwide. Influenza epidemics pose a serious public health threat. Moreover, graver dangers are encountered with influenza subtypes against which there is little or no preexisting human immunity. Such subtypes of influenza have the potential to cause devastating epidemics. Thus, enhancing surveillance systems for the purpose of detecting influenza epidemics in an early stage can quicken response times and save millions of lives. This paper presents three adapting intelligence models: support vector machine regression (SVMR), artificial neural network using particle swarm optimisation (ANNPSO), and our intelligent time series (INTS) to predict influenza epidemics. The novelty of the current study is that it proposes a new intelligent model to predict influenza outbreaks. The INTS model combines clustering with a time series model to enhance the prediction of influenza outbreaks. The innovation of our proposed model integrates the results obtained from the existing weighted exponential smoothing model with centroids obtained from clustering. We developed a surveillance system for influenza epidemics using Google search queries. The current research is based on a weighted version of the Center for Disease Control and Prevention influenza-like illness activity level obtained from the Center for Disease Control and Prevention data, as well as query data obtained from the Goggle search engine in the USA. The influenza-like illness data was collected from January 4, 2009 (week 1), to December 27, 2015 (week 52), stretching across a total time span of 312 weeks. Google Correlate was used to select search queries related to influenza epidemics. In total, 100 search queries were obtained from Google Correlate, 10 of which were better and more relevant search queries selected in this study. The model was evaluated using online Google search queries collected from Google Correlate. Standard measure performance MSE, RMSE, and MAE were employed to estimate the results of the proposed model. The empirical results of the INTS model showed MSE = 0.003, RMSE = 0.036, and MAE = 0.0185, indicating that the errors of the proposed model are very limited. A comparative model of predicting results between the INTS model, alternative Google Flu Trend (GFT), and autoregression with Google search data is also presented. The proposed model outperformed the existing models. |
| format | Article |
| id | doaj-art-c5b6789cfd464e2db94d459d2e671a79 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c5b6789cfd464e2db94d459d2e671a792025-02-03T01:28:44ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/99290139929013Using Sequence Mining to Predict Complex Systems: A Case Study in Influenza EpidemicsTheyazn H. H. Aldhyani0Manish R. Joshi1Shahab A. AlMaaytah2Ahmed Abdullah Alqarni3Nizar Alsharif4Community College of Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa, Saudi ArabiaSchool of Computer Sciences, Kavayitri Bahinabai Chaudhari North Maharashtra University, Jalgaon, MS, IndiaCommunity College of Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa, Saudi ArabiaDepartment of Computer Sciences and Information Technology, Albaha University, Al Bahah, Saudi ArabiaDepartment of Computer Engineering and Science, Albaha University, Al Bahah, Saudi ArabiaAccording to the World Health Organisation, three to five million individuals are infected by influenza, and around 250,000 to 500,000 people die of this infectious disease worldwide. Influenza epidemics pose a serious public health threat. Moreover, graver dangers are encountered with influenza subtypes against which there is little or no preexisting human immunity. Such subtypes of influenza have the potential to cause devastating epidemics. Thus, enhancing surveillance systems for the purpose of detecting influenza epidemics in an early stage can quicken response times and save millions of lives. This paper presents three adapting intelligence models: support vector machine regression (SVMR), artificial neural network using particle swarm optimisation (ANNPSO), and our intelligent time series (INTS) to predict influenza epidemics. The novelty of the current study is that it proposes a new intelligent model to predict influenza outbreaks. The INTS model combines clustering with a time series model to enhance the prediction of influenza outbreaks. The innovation of our proposed model integrates the results obtained from the existing weighted exponential smoothing model with centroids obtained from clustering. We developed a surveillance system for influenza epidemics using Google search queries. The current research is based on a weighted version of the Center for Disease Control and Prevention influenza-like illness activity level obtained from the Center for Disease Control and Prevention data, as well as query data obtained from the Goggle search engine in the USA. The influenza-like illness data was collected from January 4, 2009 (week 1), to December 27, 2015 (week 52), stretching across a total time span of 312 weeks. Google Correlate was used to select search queries related to influenza epidemics. In total, 100 search queries were obtained from Google Correlate, 10 of which were better and more relevant search queries selected in this study. The model was evaluated using online Google search queries collected from Google Correlate. Standard measure performance MSE, RMSE, and MAE were employed to estimate the results of the proposed model. The empirical results of the INTS model showed MSE = 0.003, RMSE = 0.036, and MAE = 0.0185, indicating that the errors of the proposed model are very limited. A comparative model of predicting results between the INTS model, alternative Google Flu Trend (GFT), and autoregression with Google search data is also presented. The proposed model outperformed the existing models.http://dx.doi.org/10.1155/2021/9929013 |
| spellingShingle | Theyazn H. H. Aldhyani Manish R. Joshi Shahab A. AlMaaytah Ahmed Abdullah Alqarni Nizar Alsharif Using Sequence Mining to Predict Complex Systems: A Case Study in Influenza Epidemics Complexity |
| title | Using Sequence Mining to Predict Complex Systems: A Case Study in Influenza Epidemics |
| title_full | Using Sequence Mining to Predict Complex Systems: A Case Study in Influenza Epidemics |
| title_fullStr | Using Sequence Mining to Predict Complex Systems: A Case Study in Influenza Epidemics |
| title_full_unstemmed | Using Sequence Mining to Predict Complex Systems: A Case Study in Influenza Epidemics |
| title_short | Using Sequence Mining to Predict Complex Systems: A Case Study in Influenza Epidemics |
| title_sort | using sequence mining to predict complex systems a case study in influenza epidemics |
| url | http://dx.doi.org/10.1155/2021/9929013 |
| work_keys_str_mv | AT theyaznhhaldhyani usingsequenceminingtopredictcomplexsystemsacasestudyininfluenzaepidemics AT manishrjoshi usingsequenceminingtopredictcomplexsystemsacasestudyininfluenzaepidemics AT shahabaalmaaytah usingsequenceminingtopredictcomplexsystemsacasestudyininfluenzaepidemics AT ahmedabdullahalqarni usingsequenceminingtopredictcomplexsystemsacasestudyininfluenzaepidemics AT nizaralsharif usingsequenceminingtopredictcomplexsystemsacasestudyininfluenzaepidemics |