Epidemic Peaks Forecasting on Re-emerging Diseases in Elderly People using the Grey Disaster Model

Nipaporn Chutiman; Pannarat Guayjarernpanishk; Butsakorn Kong-ied; Piyapatr Busababodhin; Monchaya Chiangpradit

doi:10.28991/esj-2021-01325

Authors

Nipaporn Chutiman Data Science and Sustainable Agriculture Research Unit, Department of Mathematics, Faculty of Science, Mahasarakham University, Maha Sarakham 41150,, Thailand
Pannarat Guayjarernpanishk Faculty of Interdisciplinary Studies, Nong Khai Campus, Khon Kaen University, Nong Khai 43000,, Thailand
Butsakorn Kong-ied Data Science and Sustainable Agriculture Research Unit, Department of Mathematics, Faculty of Science, Mahasarakham University, Maha Sarakham 41150,, Thailand
Piyapatr Busababodhin Data Science and Sustainable Agriculture Research Unit, Department of Mathematics, Faculty of Science, Mahasarakham University, Maha Sarakham 41150,, Thailand
Monchaya Chiangpradit
monchaya.c@msu.ac.th
Data Science and Sustainable Agriculture Research Unit, Department of Mathematics, Faculty of Science, Mahasarakham University, Maha Sarakham 41150,, Thailand

Abstract
How to Cite
Metrics
References
License

Climate change causes the spread of non-vector diseases due to the influence of climate uncertainty. The elderly group, which is vulnerable, is affected by such disasters. Therefore, the objectives of this study were to forecast epidemic peaks of food poisoning, which was found as one of the re-emerging diseases in elderly people in Khon Kaen Province, Maha Sarakham Province, and Roi Et Province, which are in the Northeastern region of Thailand by using 2 types of Grey Model: GM(1,1) and Discrete Grey Model (DGM). The monthly rate of food poisoning incidence per 100,000 elderly people from January 2017 to December 2020 i.e., 48 months in total were used in the study. The study result revealed that the DGM had higher forecasting effectiveness than that of the GM(1,1) in all three provinces. The food poisoning incidences in elderly people were forecasted to re-emerge from August to September 2021 in Khon Kaen Province, from August to September 2022 in Maha Sarakham Province, and from May to June 2022 in Roi Et Province. The results of this study are useful and helpful for the government, the Ministry of Public Health and related cooperatives to effectively help services planning resource preparation and prevention measures.

Doi: 10.28991/esj-2021-01325

Full Text: PDF

Chutiman, N., Guayjarernpanishk, P., Kong-ied, B., Busababodhin, P., & Chiangpradit, M. (2021). Epidemic Peaks Forecasting on Re-emerging Diseases in Elderly People using the Grey Disaster Model. Emerging Science Journal, 5(6), 974–982. https://doi.org/10.28991/esj-2021-01325

Download Citation

Ju-Long, Deng. "Control Problems of Grey Systems.” Systems and Control Letters 1, no. 5 (1982): 288–94. doi:10.1016/S0167-6911(82)80025-X.

Ju-Long, Deng. "Introduction to grey system theory." The Journal of grey system 1, no. 1 (1989): 1-24.

Xie, Nai ming, and Si feng Liu. "Discrete Grey Forecasting Model and Its Optimization.” Applied Mathematical Modelling 33, no. 2 (2009): 1173–86. doi:10.1016/j.apm.2008.01.011.

Lin, Wei Zhong, Jian An Fang, Xuan Xiao, and Kuo Chen Chou. "IDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model.” PLoS ONE 6, no. 9 (2011): 24756. doi:10.1371/journal.pone.0024756.

Shen, Xuejun, Limin Ou, Xiaojun Chen, Xin Zhang, and Xuerui Tan. "The Application of the Grey Disaster Model to Forecast Epidemic Peaks of Typhoid and Paratyphoid Fever in China.” PLoS ONE, 2013. doi:10.1371/journal.pone.0060601.

Nsoesie, Elaine O., Madhav Marathe, and John S. Brownstein. "Forecasting Peaks of Seasonal Influenza Epidemics.” PLoS Currents 5, no. Outbreaks (2013). doi:10.1371/currents.outbreaks.bb1e879a23137022ea79a8c508b030bc.

Kuniya, Toshikazu. "Prediction of the Epidemic Peak of Coronavirus Disease in Japan, 2020.” Journal of Clinical Medicine 9, no. 3 (2020): 789. doi:10.3390/jcm9030789.

Ceylan, Zeynep. "Short-Term Prediction of COVID-19 Spread Using Grey Rolling Model Optimized by Particle Swarm Optimization.” Applied Soft Computing 109 (2021). doi:10.1016/j.asoc.2021.107592.

Saxena, Akash. "Grey Forecasting Models Based on Internal Optimization for Novel Corona Virus (COVID-19).” Applied Soft Computing 111 (2021): 107735. doi:10.1016/j.asoc.2021.107735.

Hsu, Li Chang. "Applying the Grey Prediction Model to the Global Integrated Circuit Industry.” Technological Forecasting and Social Change 70, no. 6 (2003): 563–74. doi:10.1016/S0040-1625(02)00195-6.

Hsu, Che Chiang, and Chia Yon Chen. "Applications of Improved Grey Prediction Model for Power Demand Forecasting.” Energy Conversion and Management 44, no. 14 (2003): 2241–49. doi:10.1016/S0196-8904(02)00248-0.

Li, J, B Y Wang, and B S Zhang. "Application of Improved Grey Prediction Model to Petroleum Cost Forecasting.” Petroleum Science 3 (2006): 89–92.

Tsai, Chen Fang. "The Application of Grey Theory to Taiwan Pollution Prediction.” In Lecture Notes in Engineering and Computer Science, 2198:731–36, 2012.

Ene, Seval, and Nursel Öztürk. "Grey Modelling Based Forecasting System for Return Flow of End-of-Life Vehicles.” Technological Forecasting and Social Change 115 (2017): 155–66. doi:10.1016/j.techfore.2016.09.030.

Yuan, Chaoqing, Yuxin Zhu, Ding Chen, Sifeng Liu, and Zhigeng Fang. "Using the GM(1,1) Model Cluster to Forecast Global Oil Consumption.” Grey Systems: Theory and Application 7, no. 2 (2017): 286–96. doi:10.1108/gs-01-2017-0001.

Wang, Zheng Xin, Qin Li, and Ling Ling Pei. "Grey Forecasting Method of Quarterly Hydropower Production in China Based on a Data Grouping Approach.” Applied Mathematical Modelling 51 (2017): 302–16. doi:10.1016/j.apm.2017.07.003.

Wang, Qianru, Li Liu, Shu Wang, Jian Zhou Wang, and Ming Liu. "Predicting Beijing's Tertiary Industry with an Improved Grey Model.” Applied Soft Computing 57 (2017): 482–94. doi:10.1016/j.asoc.2017.04.022.

Ding, Song, Keith W. Hipel, and Yao guo Dang. "Forecasting China's Electricity Consumption Using a New Grey Prediction Model.” Energy 149 (2018): 314–28. doi:10.1016/j.energy.2018.01.169.

Li, Menglu, Wei Wang, Gejirifu De, Xionghua Ji, and Zhongfu Tan. "Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm.” Energies 11, no. 9 (2018): 2475–2489. doi:10.3390/en11092475.

Luo, Dang, Xiaolei Wang, Decai Sun, and Guozheng Zhang. "Discrete Grey DGM(1,1,T) Model with Time Periodic Term and Its Application.” Xitong Gongcheng Lilun Yu Shijian/System Engineering Theory and Practice 40, no. 10 (2020): 2737–46. doi:10.12011/1000-6788-2019-1335-10.

Wu, Lifeng, and Hongying Zhao. "Discrete Grey Model with the Weighted Accumulation.” Soft Computing 23, no. 23 (2019): 12873–81. doi:10.1007/s00500-019-03845-3.

Guo, Hua, Shengxiang Deng, Jinbiao Yang, Jiangwei Liu, and Changda Nie. "Analysis and Prediction of Industrial Energy Conservation in Underdeveloped Regions of China Using a Data Pre-Processing Grey Model.” Energy Policy 139 (2020): 111244. doi:10.1016/j.enpol.2020.111244.

Cao, Yun, Kedong Yin, Xuemei Li, and Chenchen Zhai. "Forecasting CO2 Emissions from Chinese Marine Fleets Using Multivariable Trend Interaction Grey Model.” Applied Soft Computing 104 (2021): 107220. doi:10.1016/j.asoc.2021.107220.

Ding, Fan, Dante S. Zarlenga, Yudong Ren, Guangxing Li, Jin Luan, and Xiaofeng Ren. "Use of the D-R Model to Define Trends in the Emergence of Ceftazidime-Resistant Escherichia Coli in China.” PLoS ONE 6, no. 12 (2011): 27295. doi:10.1371/journal.pone.0027295.

Epidemic Peaks Forecasting on Re-emerging Diseases in Elderly People using the Grey Disaster Model

Authors

Downloads

Dimension Badge

Downloads

Downloads

Issue

Section

Keywords

Address

Contact Info: