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泰国清迈省肺炎住院人数的时间序列建模及其与空气污染和气候变量的关系。

Time series modeling of pneumonia admissions and its association with air pollution and climate variables in Chiang Mai Province, Thailand.

机构信息

Department of Social and Environmental Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.

出版信息

Environ Sci Pollut Res Int. 2018 Nov;25(33):33277-33285. doi: 10.1007/s11356-018-3284-4. Epub 2018 Sep 26.

DOI:10.1007/s11356-018-3284-4
PMID:30255274
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6245022/
Abstract

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 PM 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.

摘要

本研究旨在预测清迈府的肺炎病例数。使用自回归积分移动平均 (ARIMA) 进行数据拟合，并按月预测未来的肺炎病例数。在 2003-2014 年期间，清迈共记录了 67583 例肺炎病例，每年的病例模式相似。每月肺炎病例在 2 月和 9 月增加，这是泰国冬季和雨季的时期，而在 4 月至 7 月（夏季到早雨季）减少。利用清迈可用的 12 年肺炎病例、空气污染和气候数据，根据几个条件研究了最优的 ARIMA 模型。由于统计学上季节条件较强，模型中包含季节性变化。研究了 12 个 ARIMA 模型（ARMODEL1-ARMODEL12）情景。结果表明，最适合的模型是 ARIMA（1,0,2）（2,0,0）[12]，其中 PM10（ARMODEL5）的 AIC 最低，为-38.29。使用 ARMODEL5 在 2013 年 1 月至 3 月预测的每月肺炎病例数分别为 727、707 和 658 例，而实际数字分别为 804、868 和 783 例。这一发现表明 PM 对预测清迈的每月肺炎病例数起着最重要的作用，该模型能够准确预测清迈的未来肺炎病例数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a12a/6245022/2fd9fcb8f007/11356_2018_3284_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a12a/6245022/2a64b10a62c4/11356_2018_3284_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a12a/6245022/1a2d3be54a09/11356_2018_3284_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a12a/6245022/517bbdc05899/11356_2018_3284_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a12a/6245022/db8d886a991e/11356_2018_3284_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a12a/6245022/15fdd509c2e8/11356_2018_3284_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a12a/6245022/2fd9fcb8f007/11356_2018_3284_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a12a/6245022/2a64b10a62c4/11356_2018_3284_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a12a/6245022/1a2d3be54a09/11356_2018_3284_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a12a/6245022/517bbdc05899/11356_2018_3284_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a12a/6245022/db8d886a991e/11356_2018_3284_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a12a/6245022/15fdd509c2e8/11356_2018_3284_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a12a/6245022/2fd9fcb8f007/11356_2018_3284_Fig6_HTML.jpg

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