Abstract
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
Highlights
Climate change causes seasonal infectious diseases due to the influence of climate uncertainty such as temperature and humidity as parts of climate variation
This research studied the epidemic peaks of food poisoning of elderly people in Khon Kaen Province, Maha Sarakham Province, and Roi Et Province (Thailand) since those provinces were almost in the top ranks of elderly people with food poisoning in the middle northeastern region
The data at 4 epidemic peaks were selected for the analyses with GM(1,1) and Discrete Grey Model (DGM) as shown in Figures 2 to 4
Summary
Climate change causes seasonal infectious diseases due to the influence of climate uncertainty such as temperature and humidity as parts of climate variation. In 2020, the northeastern region contained the largest number of elderly people. Ju-Long (1982, 1989) [1-2] presents the Grey Model or GM(1,1) by basing the study principles on discrete data, uncertainty data distribution, and the limited number of data. Xie and Liu (2008) [3] develop the Discrete Grey Model (DGM) from the effective improvement of the GM(1,1). In medicines and public health, the spread of diseases is affected by several uncertain factors, so the Grey theory with dynamic changes is suitable to be used. This research studied the epidemic peaks of food poisoning of elderly people in Khon Kaen Province, Maha Sarakham Province, and Roi Et Province (Thailand) since those provinces were almost in the top ranks of elderly people with food poisoning in the middle northeastern region. The analyses with GM(1,1) and DGM are useful for planning resources and preventing the diseases effectively
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