傳染病地理學的空間分析方法－回顧與進展

Spatial Analysis Methods for the Geography of Infectious Diseases: Review and Prospect

溫在弘(Tzai-Hung Wen)

《地理學報》 101期 (2022/04) Pp. 109-124

https://doi.org/10.6161/jgs.202204_(101).0006

摘要

地圖繪製是提供瞭解傳染病空間分布與型態的視覺化方式，也是最早被用來分析疾病擴散來源的地理工具。由於地理資訊系統的技術逐漸成熟，以及全球普遍受到新興與再現傳染病的傳播威脅，傳染病地理學的空間分析方法近年已經在地理學、醫學與公共衛生領域有重要的影響力。本篇文章將回顧傳染病地理學的空間分析觀點，主要包括：點資料的群聚分析、面資料的空間相依性與空間異質性，以及因資料彙整而產生的空間尺度效果：可調整面積單元問題。近年來數據科學與人工智慧的興起，分析方法已朝向整合多元尺度與異質資料來源的發展趨勢，因此，這篇文章也提供疾病地理學新興議題的進展，包括：整合時間維度的群聚分析、透過社群媒體與機器學習模式於監測與預測疫情傳播，以及佈建物聯網進行環境危險因子的即時監測，提供疫情早期預警與應變的資訊。

關鍵字

傳染病 ； 疫情控制 ； 空間資訊

並列摘要

Mapping is a common method for visualizing spatial distributions and patterns of diseases, and was the earliest tool for identifying the source of infection. Due to rapid development of geospatial technologies and global threats of emerging and re-emerging infectious diseases, spatial analysis and modeling are considered as important for understanding the geographies of infectious disease and public health. This article briefly reviews spatial analytical perspectives on the geography of disease, including disease spatial clustering, spatial dependency and heterogeneity, and the modifiable areal unit problem (MAUP) due to spatial data aggregation. This study also shares potential applications of data science and artificial intelligence, such as developing new methods for spatial-temporal clustering, using social media and machine learning models for disease surveillance and prediction, and constructing an Internet of Things (IoT) infrastructure to capture the real-time environmental risk factors for epidemic early warning.

並列關鍵字

infectious disease ； epidemic control ； spatial information

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