臺灣博碩士論文加值系統

隨著疫情升溫，病毒在傳播力方面的重要性日益顯現，也引起各 界關注，然而臺灣因為和他國的文化差異以及公衛政策的不同，使得 疫情爆發時間軸截然不同，因此，研究疫情潛在風險因子和區域才能 有效進行防疫工作。本文研究目的是評估環境因子對臺灣COVID-19 感染風險的影響，使用克利金法對缺乏測站區域之空氣汙染物進行 補值，運用貝氏階層模型分析，為避免共線性問題將PM2.5 和PM10 分開建模，在確診數階層分別採用卜瓦松分布、負二項分布以及零 膨脹卜瓦松分布，空間效應則以Besag York-and-Molliè 模型(BYM) 和 Leroux 模型作為比較，WAIC 作為選模標準，在此六種模型中，以 負二項分布結合BYM 模型的效果最佳。結果顯示，不論是以PM2.5 或PM10 建模的模型，SO2、O3 和人口密度皆為顯著正相關，另外在 PM10 的模型中，PM10 亦為顯著正相關，而在PM2.5 模型中，當分別提 高1ppb SO2、1ppb O3 和1 單位標準化後人口密度，將會提升22.6%、 2.05% 和7.14% 的感染率，在PM10 模型中則為提升27.07%、2.24% 和 6.53% 的感染率。本文表明部分空氣汙染因子與新冠肺炎確診數具顯 著相關，以及不同時間點的熱點區域，在未來的空氣監管和醫療資源 分配政策具有參考價值。

As the COVID-19 spreads rapidly, studying virus transmission plays a crucial role in epidemiology. However, there is a very different COVID-19 pandemic timeline from other countries because of Taiwan＇s unique cultural differences and public health policies. Consequently, there is a need for understanding potential risk factors and the spatio-temporal trend of COVID-19 incidence. This study aims to investigate the impact of environmental factors on the risk of COVID-19 infection in Taiwan. The missing values of air pollutants in areas without monitoring stations are imputed using the kriging method. Hierarchical Bayesian spatio-temporal models are implemented to investigate the association between covariates and the number of cases by assuming different distributions for the confirmed case data, such as Poisson distribution, negative binomial distribution, and zero-inflated Poisson distribution. Spatial effects are considered using the BYM and Leroux models. The model selection criterion based on WAIC reveals that the combination of negative binomial distribution and BYM model best explains COVID-19 incidence rate. In addition, the results show that SO2, O3, and population density have significantly positive association with COVID-19 incidence in both PM2.5 and PM10 models. Additionally, the PM10 model shows a significant positive correlation between PM10 and the incidence rate. In the PM2.5 model, an increase of 1 ppb in SO2, 1 ppb in O3, and the standardized population density increases by 1 unit corresponds to a 22.6%, 2.05%, and 7.14% increase in incidence rates, respectively, while in the PM10 model, the corresponding increases are 27.07%, 2.24%, and 6.53%. This study reveals the significant associations between certain air pollutants and COVID-19 incidence, as well as the identification of hotspots at different time periods. These findings provide valuable insights for future air pollution regulations and resource allocation in healthcare.

口試委員會審定書 ii
誌謝 iii
摘要 iv
Abstract v
1 研究動機 1
2 研究方法 3
2.1 研究流程 3
2.2 克利金法 (Kriging) 4
2.2.1 原理 4
2.2.2 半變異函數 (Semi-variogram) 6
2.3 全域型空間自相關檢定 8
2.4 廣義時空線性混合模型 10
2.4.1 模型架構 10
2.4.2 空間模型 12
2.4.3 自迴歸模型 (Autoregressive model) 13
2.5 模型選擇準則 14
3 資料介紹 16
3.1 資料來源 16
3.2 確診情形 16
3.3 空氣汙染物和溫度 18
4 研究結果 23
4.1 使用空間模型的適當性 23
4.2 模型結果 24
4.3 高相對風險區域 26
4.4 熱點區域 29
5 討論 32
5.1 顯著相關之環境汙染因子 32
5.2 熱點區域特徵探討 32
6 結論與展望 34
參考文獻 35
附錄 39

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