Introduction: The current study investigates the complex association between COVID-19 and the studied districts' socioecology (e.g. internal and external built environment, sociodemographic profiles, etc.) to quantify their contributions to the early outbreaks and epidemic resurgence of COVID-19. Methods: We aligned the analytic model's architecture with the hierarchical structure of the resident's socioecology using a multi-headed hierarchical convolutional neural network to structure the vast array of hierarchically related predictive features representing buildings' internal and external built environments and residents' sociodemographic profiles as model input. COVID-19 cases accumulated in buildings across three adjacent districts in HK, both before and during HK's epidemic resurgence, were modeled. A forward-chaining validation was performed to examine the model's performance in forecasting COVID-19 cases over the 3-, 7-, and 14-day horizons during the two months subsequent to when the model for COVID-19 resurgence was built to align with the forecasting needs in an evolving pandemic. Results: Different sets of factors were found to be linked to the earlier waves of COVID-19 outbreaks compared to the epidemic resurgence of the pandemic. Sociodemographic factors such as work hours, monthly household income, employment types, and the number of non-working adults or children in household populations were of high importance to the studied buildings' COVID-19 case counts during the early waves of COVID-19. Factors constituting one's internal built environment, such as the number of distinct households in the buildings, the number of distinct households per floor, and the number of floors, corridors, and lifts, had the greatest unique contributions to the building-level COVID-19 case counts during epidemic resurgence.

翻译：摘要：引言：本研究探讨新冠肺炎（COVID-19）与研究区域社会生态学因素（如内部与外部建成环境、社会人口学特征等）之间的复杂关联，量化其对疫情早期暴发和流行复发的贡献。方法：我们采用多头层次化卷积神经网络，将分析模型架构与居民社会生态学的层级结构对齐，将代表建筑物内部与外部建成环境及居民社会人口学特征的层级化预测特征作为模型输入。研究了香港三个相邻区域在疫情流行复发前后，各楼宇累积的新冠肺炎确诊病例。通过前向链式验证，评估模型在构建新冠肺炎疫情复发模型后两个月内，对3天、7天和14天预测窗口内确诊病例的预测性能，以满足疫情动态演进中的预测需求。结果：研究发现，相较于疫情复发阶段，早期新冠肺炎暴发浪潮与不同的因素组合相关联。在疫情早期传播阶段，社会人口学因素（如工作时长、家庭月收入、就业类型及家庭中非在职成人或儿童数量）对研究楼宇的确诊病例数具有极高重要性。而在疫情复发阶段，构成内部建成环境的因素（如楼宇内独立住户数、每层独立住户数、楼层数、走廊数及电梯数）对楼宇层面确诊病例数的独特贡献最为显著。