目標：本研究探討運用戶籍人口資料與電信人流資料進行臺北市、新北市新冠肺炎熱區預警的建構，比較其預測準確度與應用上的限制，期提供未來疫情防治參考。方法：研究的時空範圍為2021年5月之雙北市村里，分別使用靜態、動態兩種資料建構人流網絡。前者以重力模型模擬人流，後者以電信數據測量人流變化，透過網絡空間的結構等位特性估計人流足跡相似度，進而計算村里感染風險，並以ROC曲線、羅吉斯迴歸檢驗在不同確診門檻值下的模型表現。結果：在研究區間內，電信人流之平均曲線下面積較高（AUC為0.75，重力模型之AUC為0.69），且傾向預測距離疫情爆發中心較遠的村里，適合用於預測空間上的傳染趨勢。羅吉斯迴歸的結果也顯示，使用電信人流資料所計算的未來一周確診人數是否高於門檻值的平均風險之勝算比為1.45；重力模型之平均勝算比為1.10。結論：在評估地區的感染風險時，除了參考疫情調查與累積病例數外，人流網絡的資訊可以協助辨識潛在的高風險區域並進行及早預警。

Objectives: This study aimed to set up the prediction model of COVID-19 hotspot areas by using the census data and human mobility from telecommunication data in Taipei and New Taipei City. The comparison between their accuracy and limitations can provide the relevant insights for future epidemic control. Methods: The spatio-temporal resolution is fixed at the village level in two cities in May 2021. The static and dynamic data are used to construct the mobility network. The former applies gravity model to mimic human flow, and the latter uses telecommunication data as the measure of mobility. We propose the footprints similarity by structural equivalence of spatial networks and integrate it with the number of confirmed cases for computing the risk level of the villages. The performance of the models is evaluated using ROC curves and logistic regression under different thresholds for the confirmed cases. Results: The mobility derived from the telecommunication data provided better prediction performance than that from the census data; they have an average AUC of 0.75 and 0.69, respectively. Besides, the telecommunication data had a tendency to identify a further village as high-risk zone compared to the gravity model. According to the results of logistic regression, the odds ratio (OR) of exceeding the cases’ threshold estimated from the telecommunication data is 1.45 on average, while the one estimated from the census data is 1.10. Conclusions: Telecommunication data can be beneficial in identifying the potential high-risk areas and enhancing situational awareness in advance.