Spatiotemporal Patterns of the Omicron Wave of COVID-19 in the United States

The transmission network and spatial-temporal distributions of COVID-19: A case study in Lanzhou, China

Public emergencies exert substantial adverse effects on the socioeconomic development of cities. Investigating the transmission characteristics of COVID-19 can lead to evidence-based strategies for future pandemic intervention and prevention. Drawing upon primary COVID-19 data collected at both the street level and from individuals with confirmed cases in Lanzhou, China, our study examined the spatial-temporal distribution of the pandemic at a detailed level. First, we constructed transmission networks based on social relationships and spatial behavior to elucidate the actual natural transmission chain of COVID-19. We then analyze key information regarding pandemic spread, such as superspreaders, superspreading places, and peak hours. Furthermore, we constructed a space-time path model to deduce the spatial transmission trajectory of the pandemic while validating it with real activity trajectory data from confirmed cases. Finally, we investigate the impacts of pandemic prevention and control policies. The progression of the pandemic exhibits distinct stages and spatial clustering characteristics. People with complex social relationships and daily life trajectories and places with high pedestrian flow and commercial activity venues are prone to becoming superspreaders and superspreading places. The transmission path of the pandemic showed a pattern of short-distance and adjacent transmission, with most areas not affected. Early-stage control measures effectively disrupt transmission hotspots and impede the spatiotemporal trajectory of pandemic propagation, thereby enhancing the efficacy of prevention and control efforts. These findings elucidate the characteristics and transmission processes underlying pandemics, facilitating targeted and adaptable policy formulation to shape sustainable and resilient cities.