Spatiotemporal infection dynamics: Linking individual movement patterns to infection status

The swift relaxation of the zero-COVID policy in late 2022 led to an unprecedented surge in Omicron variant infections in many cities of China. Reconstructing the spatiotemporal spread of infections is crucial for effective disease prevention. However, the challenge arose due to limited data from surveys and testing results. As such, we utilized large-scale mobile phone data to estimate daily infections in Beijing from November 2022 to January 2023. Our study demonstrated that an individual's mobility status (staying home or going outside), inferred from long-term mobile phone signaling data, could indicate his or her infection status. Then, the inferred statuses of millions of individuals could be summed to reconstruct the citywide spatiotemporal dynamics of infections. We found that the infection incidence peaked on 21 December, and 80.1 % of population had been infected by 14 January 2023 in Beijing. Furthermore, infection dynamics exhibited significant demographic and spatiotemporal disparities, with urban centers experiencing faster initial increases compared to suburbs. Our work provides a new viewpoint for sensing the epidemic spatiotemporally from residents' mobility patterns in a city when official measures of confirming cases are not available, and our findings facilitate city policymaking in terms of relaxing containment measures.