Deep Learning Approach to Forecasting COVID-19 Cases in Residential Buildings of Hong Kong Public Housing Estates: The Role of Environment and Sociodemographics

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.