Uncertainties in the Assessment of COVID-19 Risk: A Study of People's Exposure to High-Risk Environments Using Individual-Level Activity Data

Based on different conceptualizations and measures of individual-level environmental exposure, this study examines how the uncertain geographic context problem (UGCoP) and the neighborhood effect averaging problem (NEAP) might affect the assessment of COVID-19 risk. Using the COVID-19 data on an open-access government Web site and the individual-level activity data of sixty confirmed COVID-19 cases (infected persons) in Hong Kong, we first represent COVID-19 risk environments using case-based and venues-based high-risk locations. The COVID-19 risk of each of the sixty selected cases is then evaluated by three approaches based on their exposures to the case-based or venues-based risk environments: the mobility-based approach, the residence-based approach, and the activity space-based approach. The results indicate that the UGCoP and the NEAP exist in the assessment of COVID-19 risk, which has significant implications: Ecological COVID-19 studies need to address the uncertainties due to the UGCoP and the NEAP by considering people's daily mobility. Otherwise, ignoring peoples' daily mobility and its interactions with complex and dynamic COVID-19 risk environments could lead to misleading results and misinform government nonpharmaceutical intervention measures.

Automated summary

This study examines the impact of UGCoP and NEAP on the assessment of COVID-19 risk based on different conceptualizations and measures of individual-level environmental exposure. The results suggest that addressing uncertainties due to UGCoP and NEAP by considering people's daily mobility is crucial for ecological COVID-19 studies. Failure to account for daily mobility and its interactions with complex COVID-19 risk environments could lead to misleading results and misinform government intervention measures.