Publication: Time series modeling of pneumonia admissions and its association with air pollution and climate variables in Chiang Mai Province, Thailand
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Issued Date
2018-11-01
Resource Type
ISSN
16147499
09441344
09441344
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2-s2.0-85053930067
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Mahidol University
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SCOPUS
Bibliographic Citation
Environmental Science and Pollution Research. Vol.25, No.33 (2018), 33277-33285
Suggested Citation
Apaporn Ruchiraset, Kraichat Tantrakarnapa Time series modeling of pneumonia admissions and its association with air pollution and climate variables in Chiang Mai Province, Thailand. Environmental Science and Pollution Research. Vol.25, No.33 (2018), 33277-33285. doi:10.1007/s11356-018-3284-4 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/45865
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Title
Time series modeling of pneumonia admissions and its association with air pollution and climate variables in Chiang Mai Province, Thailand
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Abstract
© 2018, The Author(s). This study aimed to predict the number of pneumonia cases in Chiang Mai Province. An autoregressive integrated moving average (ARIMA) was used in data fitting and to predict future pneumonia cases monthly. Total pneumonia cases of 67,583 were recorded in Chiang Mai during 2003–2014 that the monthly pattern of case was similar every year. Monthly pneumonia cases were increased during February and September, which are the periods of winter and rainy season in Thailand and decreased during April to July (the period of summer season to early rainy season). Using available data on 12 years of pneumonia cases, air pollution, and climate in Chiang Mai, the optimum ARIMA model was investigated based on several conditions. Seasonal change was included in the models due to statistically strong season conditions. Twelve ARIMA model (ARMODEL1–ARMODEL12) scenarios were investigated. Results showed that the most appropriate model was ARIMA (1,0,2)(2,0,0)[12] with PM10 (ARMODEL5) exhibiting the lowest AIC of − 38.29. The predicted number of monthly pneumonia cases by using ARMODEL5 during January to March 2013 was 727, 707, and 658 cases, while the real number was 804, 868, and 783 cases, respectively. This finding indicated that PM10 held the most important role to predict monthly pneumonia cases in Chiang Mai, and the model was able to predict future pneumonia cases in Chiang Mai accurately.