疾病群聚是指某種疾病在時間或空間上的異常集中現象。統計學家和流行病學家透過統計推論評估其顯著性，以識別危險因子並制定防治策略。人類疾病通常具有複雜性，因此本研究所使用方法皆可校正危險因子，而高時空解析度分析能提高偵測精確性，準確識別高風險地區與時間點。
我們使用兩種疾病空間統計模型，模型一為Grimson於1981年提出map-based pattern recognition procedure，該模型透過排序疾病發生率並按照疾病強度進行分層(incidence intensity level)，Wu和Shete在2020年將該模型拓展為generalized map-based pattern recognition procedure使其能校正危險因子；模型二為Kulldorff於1997年所提出的spatial scan statistic，該模型使用圓形窗口掃描整個研究區域，透過likelihood ratio test識別最有可能的群聚，接著使用Monte Carlo simulation進行統計推論。他將該模型在1998年和2001年擴展為retrospective space-time scan statistic和prospective space-time scan statistic，使其能偵測時空疾病群聚。本研究對象為2023年台南市，因該年台灣本土登革熱病例數為二戰後第二高，最高為2015年且已調查過，台南市佔全台80.5%。因此，本研究透過高解析度數據與不同時空維度分析方法，偵測2023年台南市37區與649里的登革熱時空群聚。兩者對不同形式的資料皆有不同的敏感度和統計檢定力，兩個方法可以使我們從不同角度觀察疾病時空群聚現象。
我們使用7和21天最大掃描時間窗口表示短時間的快速爆發和長時間維持高發生率現象。以7天為最大掃描時間窗口的最有可能疾病群聚，以南區為中心，總共包含4區和以中西區永華里為中心，包含120里，時間區間為2023年9月30日至10月6日。以21天為最大掃描窗口的結果中，偵測到相同群聚地點，時間區間為2023年9月18日至10月8日。在固定時間區間後，使用generalized map-based pattern recognition procedure識別的疾病群聚中，7和21天的結果增加北區、永康區和安南區；里的最嚴重群聚則位於市中心附近。在前瞻性時空分析中，在2023年10月2日偵測到群聚，對應到回顧性時空群聚分析的7和21天時間區間，若能及時介入，或許能降低群聚之爆發範圍。
本研究資料集僅有年齡和性別兩項登革熱危險因子有完整資訊可用，未來若有其他完整資訊的危險因子如血清型和慢性病史等也可納入分析。我們使用區和里兩種地理解析度，區可以觀察整體區域的疾病發展趨勢，里幫助我們更精準找到群聚位置和時間。資料集內還有其他更細緻地理單位，未來可以嘗試使用它們找到最嚴重群聚。我們使用不同的方法、地理解析度以及時空維度觀察2023年台南市登革熱發病模式。未來官方衛生單位可以透過這些發現進一步調查登革熱群聚之致因，進而制定相關公共衛生政策，使他們能合理分配醫療資源，針對最嚴重的區域優先防治，降低區域內的疾病風險避免群聚範圍擴散。

We investigated spatio-temporal clusters of dengue fever in Tainan City, Taiwan, in 2023, using spatial scan statistics and generalized map-based pattern recognition procedure. Retrospective and prospective space-time scan statistics were applied to detect spatio-temporal clusters. The retrospective space-time scan statistic with 7-day window detected clusters centered in South District and Yonghua Village, West Central District, during September 30 to October 6. 21-day window detected similar clusters from September 18 to October 8 across similar districts and villages, respectively. After fixing the time frames determined by the retrospective space-time scan statistic, the generalized map-based pattern recognition procedure was applied and detected two clusters in both the 7-day and 21-day analyses. The prospective space-time scan statistic detected clusters on October 2 which were the same as those detected in retrospective space-time scan statistic analysis correspondingly. By applying two different models, two different geographic resolutions and spatial and space-time analysis, we investigated the patterns of dengue fever outbreaks in Tainan City in 2023. With this information, public health authorities can better allocate medical resources, identify unknown risk factors, and prioritize interventions in high-risk areas.

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