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
This research forecasted the incidence rate per 100,000 elderly population with food poisoning, pneumonia, and fever of unknown origin in Khon Kaen Province and Roi Et Province in the northeastern part of Thailand. In the study, the time series forecasting with Box-Jenkins Method (SARIMA model) and Box-Jenkins Method with climate variables, i.e total monthly rainfall, maximum average monthly temperature, average relative humidity, minimum average monthly temperature and average temperature (SARIMAX model) was performed. The study results revealed that the forecasting accuracy was closely similar to the model without the climate variables in the combined analysis although such climate variables had relationship with the incidence rate per 100,000 elderly population with food poisoning, pneumonia, and fever of unknown origin. Therefore, the appropriate model should be the SARIMA model because it is easier for analysis but with higher forecasting accuracy than the SARIMAX model.
This research aims to develop a model for forecasting daily maximum rainfall caused by tropical cyclones over Northeastern Thailand during August and September 2022 and 2023. In the past, the ARIMA or ARIMAX method to forecast rainfall was used in research. It is a short-term rainfall prediction. In this research, the Grey Theory was applied as it is an approach that manages limited and discrete data for long-term forecasting. The Grey Theory has never been used to forecast rainfall that is affected by tropical cyclones in Northeastern Thailand. The Grey model GM(1,1) was analyzed with the highest daily cumulative rainfall data during the August and September tropical cyclones of the years 2018–2021, from the weather stations in Northeastern Thailand in 17 provinces. The results showed that in August 2022 and 2023, only Nong Bua Lamphu province had a highest daily rainfall forecast of over 100 mm, while the other provinces had values of less than 70 mm. For September 2022 and 2023, there were five provinces with the highest daily rainfall forecast of over 100 mm. The average of mean absolute percentage error (MAPE) of the maximum rainfall forecast model in August and September is approximately 20 percent; therefore, the model can be applied in real scenarios. Doi: 10.28991/CEJ-2022-08-08-02 Full Text: PDF
The objective of this research was to develop a mathematical and statistical model for long-term prediction. The Extreme Value Theory (EVT) was applied to analyze the appropriate distribution model by using the peak-over-threshold approach with Generalized Pareto Distribution (GPD) to predict daily extreme precipitation and extreme temperatures in eight provinces located in the upper northeastern region of Thailand. Generally, each province has only 1–2 meteorological stations, so spatial analysis cannot be performed comprehensively. Therefore, the reanalysis data were obtained from the NOAA Physical Sciences Laboratory. The precipitation data were used for spatial analysis at the level of 25 square kilometers, which comprises 71 grid points, whereas the temperature data were used for spatial analysis at the level of 50 square kilometers, which includes 19 grid points. According to the analysis results, GPD was appropriate for the goodness of fit test with Kolmogorov-Smirnov Statistics (KS Test) according to the estimation for the return level in the annual return periods of 2 years, 5 years, 10 years, 25 years, 50 years, and 100 years, indicating the areas with daily extreme precipitation and extreme temperatures. The analysis results would be useful for supplementing decision-making in planning to cope with risk areas as well as in effective planning for resources and prevention. Doi: 10.28991/CEJ-2023-09-07-014 Full Text: PDF
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