Progress in understanding human-COVID-19 dynamics using geospatial big data: a systematic review

Abstract

ABSTRACT The COVID-19 pandemic has dramatically changed human daily life. To mitigate the pandemic’s impacts, different countries and regions implemented various policies to contain COVID-19 and residents showed diverse responses. These human responses in turn shaped the uneven spatial-temporal spread of COVID-19. Such human-pandemic interaction is complex, dynamic, and interconnected. Delineating the reciprocal effects between human society and pandemics is crucial for preparing for and managing future epidemics. Geospatial big data acquired through mobile applications and sensor networks have facilitated near-real-time tracking and assessment of human responses to the pandemic, enabling a surge in researching human-pandemic interactions. However, these investigations involve inconsistent data sources, human activity indicators, relationship detection models, and analysis methods, leading to a fragmented understanding of human-pandemic dynamics. To assess the current state of human-pandemic interactions research using geospatial big data, we conducted a synthesis study based on 67 selected publications between 25 March 2020, and 9 January 2023. We extracted information from each article across six categories, i.e. publication details, research context, research area and time, data, methodological framework, and results and conclusions. Results reveal that the influence of stay-at-home policies on mobility decrease varied regionally, showing limited effectiveness in Europe compared to the US. The positive correlations between human mobility and COVID-19 case rates evolved through time and were highest in the initial outbreak in 2020. Public awareness generally peaked prior to the peaks in COVID-19 cases, with varying intervals of 0 to 19.8 days observed across different countries. This study summarizes the research characteristics of selected articles and highlights the need for future research to spatially and temporally model the long-term, bidirectional causal relationships within human-pandemic systems to inform evidence-based, hyperlocal pandemic mitigation strategies.