COVID-19 case numbers in 161 sub-districts of Wuhan were investigated based on landscape epidemiology, and their landscape metrics were calculated based on land use/land cover (LULC). Initially, a mediation model verified a partially mediated population role in the relationship between landscape pattern and infection number. Adjusted incidence rate (AIR) and community safety index (CSI), two indicators for infection risk in sub-districts, were 25.82∼63.56 ‱ and 3.00∼15.87 respectively, and central urban sub-districts were at higher infection risk. Geographically weighted regression (GWR) performed better than OLS regression with AICc differences of 7.951∼181.261. The adjusted R² in GWR models of class-level index and infection risk were 0.697 to 0.817, while for the landscape-level index they were 0.668 to 0.835. Secondly, 16 key landscape metrics were identified based on GWR, and then a prediction model for infection risk in sub-districts and communities was developed. Using principal component analysis (PCA), development intensity, landscape level, and urban blue-green space were considered to be principal components affecting disease infection risk, explaining 73.1 % of the total variance. Cropland (PLAND and LSI), urban land (NP, LPI, and LSI) and unused land (NP) represent development intensity, greatly affecting infection risk in urban areas. Landscape level CONTAG, DIVISION, SHDI, and SHEI represent mobility and connectivity, having a profound impact on infection risk in both urban and suburban areas. Water (PLAND, NP, LPI, and LSI) and woodland (NP, and LSI) represent urban blue-green spaces, and were particularly important for infection risk in suburban areas.

Based on urban landscape pattern, we proposed a framework to understand and evaluate infection risk. These findings provide a basis for risk evaluation and policy-making of urban infectious disease, which is significant for community management and urban planning for infectious disease worldwide.

基于景观流行病学调查武汉市 161 个街道的 COVID-19 病例数，并根据土地利用/土地覆盖 (LULC) 计算其景观指标。最初，中介模型验证了人口在景观格局和感染数量之间的关系中的部分中介作用。调整后的发病率（AIR）和社区安全指数（CSI）是街道感染风险的两项指标，分别为25.82∼63.56‱和3.00∼15.87，中心城区的感染风险较高。地理加权回归 (GWR) 表现优于 OLS 回归，AICc 差异为 7.951∼181.261。调整后的 R ²在 GWR 模型中，类级指数和感染风险为 0.697 至 0.817，而景观级指数为 0.668 至 0.835。其次，基于GWR确定了16个关键景观指标，进而开发了街道和社区感染风险预测模型。使用主成分分析 (PCA)，开发强度、景观水平和城市蓝绿空间被认为是影响疾病感染风险的主要成分，解释了总方差的 73.1%。农田（PLAND 和 LSI）、城市土地（NP、LPI 和 LSI）和未利用土地（NP）代表开发强度，极大地影响城市地区的感染风险。景观级别 CONTAG、DIVISION、SHDI 和 SHEI 代表移动性和连接性，对城市和郊区的感染风险产生深远影响。水（计划，

基于城市景观格局，我们提出了一个理解和评估感染风险的框架。这些发现为城市传染病的风险评估和决策提供了依据，对全球传染病的社区管理和城市规划具有重要意义。