疾病群聚通常指疾病在時間或空間中有異常高的發生情況。檢測疾病群聚有助於發現潛在的危險因子，並應用在疾病監測系統中，對疾病的預防與控制有很大的幫助。本研究偵測台灣北部COVID-19時空群聚現象，空間中偵測方法我們分別使用Kulldorff於1997年所提出的spatial scan statistic和Wu與Shete於2020年所提出的generalized map-based pattern recognition procedure，時空中偵測方法使用Kulldorff於1998年所提出的space-time scan statistic。COVID-19如大多數疾病一樣是複雜性的，所以上述方法都可以校正危險因子。
在空間與時空偵測疾病群聚分析中，最常被使用的方法之一為spatial scan statistic，此方法主要以圓形掃描窗口對研究區域進行掃描，透過likelihood ratio test找出最大可能疾病群聚，再使用Monte Carlo simulation獲得機率分布並進行統計推論。Wu與Shete的方法用疾病發生率的排序來尋找群聚，且可以探討疾病稀少的地區。兩者對不同形式的疾病群聚，有不同的敏感度與統計檢定力，各自的優勢可以找出具有代表性的群聚，並提供我們不同且有用的資訊。
Space-time scan statistic為時空中疾病群聚的偵測方法，是將原本spatial scan statistic的概念，加上時間後延伸到三維的時空中。我們將時間的概念加入，使得原本的圓形掃描窗口，在把時間當作高度加入後，變成圓柱狀的掃描窗口，以進行時空中疾病群聚偵測。在我們的研究中，時空中的時間掃描區間使用7天與21天，分別表示短時間內快速上升與長時間內維持最高疾病發生率的時空疾病群聚現象。
台灣從2020年1月開始有COVID-19爆發，而直到2022年4月後有最高一波的疫情發生，尤其在2022/4/1-2022/12/31佔台灣確診人數的99%以上，所以我們選擇此為時空中時間掃描區間，而台灣北部為疫情爆發的源頭，特別在台北、新北、基隆與桃園，為人口稠密區且有大量病例數，所以地理上掃描包含上述4個縣市，並依照行政區分成61區進行掃描。校正年齡性別後，以7天為最大掃描窗口的最大最有可能疾病群聚M，以新北新莊為中心，總共包含11個地區，時間區間為2022/5/11-2022/5/17。以21天為最大掃描窗口的結果中，偵測到同樣以新北新莊為中心的M，也偵測到被包含在其中的疾病群聚MO，地點只有新北三重，時間區間為2022/5/6-2022/5/26。而在固定時間區間後，繼續使用Wu與Shete的方法偵測到的疾病群聚，都以新北三重為主要中心。
由於資料的限制，本研究只針對年齡和性別進行校正，但發現大部分的時空群聚不只在短時間中有快速上升的現象，也持續維持最高發生率的情況。在我們的研究中，使用時空疾病群聚偵測會比空間偵測更加有用。時空偵測除了確定群聚地點外，也確認群聚發生的時間區間，讓我們可以針對可能的疾病群聚探討其潛在的危險因子，以利預防或控制未來傳染性疾病與慢性病的擴大發生。另外，本研究除了疾病群聚外，也針對疾病相對稀少進行探討。

COVID-19 has evolved into a global pandemic since 2020, and Taiwan experienced the worst wave of the outbreak since April 2022. We performed spatial and space-time clustering analysis of COVID-19 from April 1, 2022 to December 31, 2022, in northern Taiwan, including 4 cities: Taipei, New Taipei, Taoyuan and Keelung. The clustering detection methods we used in space are the spatial scan statistic by Kulldorff in 1997 and the generalized map-based pattern recognition procedure by Wu and Shete in 2020. The space-time clustering analysis method we used is the space-time scan statistic also by Kulldorff in 1998. In this thesis, we considered and adjusted for 2 risk factors, age and gender. In the space-time disease clustering analysis by the space-time scan statistic, the 7-day analysis detected a disease cluster whose center was located in Xinzhuang, New Taipei, including 11 districts, from May 11 to May 17, 2022. The 21-day analysis detected the same disease cluster. In addition, a subcluster was found within this disease cluster whose center was located in Sanchong, New Taipei, including only 1 district, within the time frame from May 6 to May 26, 2022. We found that the majority of the observed space-time clusters were not only quickly emerging clusters within a shorter period of time but also sustained clusters with the highest incidence rate over a longer time. The spatial and space-time disease cluster analyses with adjustment of known risk factors allow us to explore more potential risk factors associated with the occurrence of disease clusters, facilitating future disease prevention and control.

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