Temporal Contact Graph Reveals the Evolving Epidemic Situation of COVID-19

Abstract Contact tracing APPs have been recently advocated by many countries (e.g., the United Kingdom, Australia, etc.) as part of control measures on COVID-19. Controversies have been raised about their effectiveness in practice as it still remains unclear how they can be fully utilized to fuel the fight against COVID-19. In this article, we show that an abundance of information can be extracted from contact tracing for COVID-19 prevention and control, providing the first data-driven evidence that supports the wide implementation of such APPs. Specifically, we construct a temporal contact graph that quantifies the daily contacts between infectious and susceptible individuals by exploiting a large volume of location related data contributed by 10,527,737 smartphone users in Wuhan, China. Five time-varying indicators we introduce can accurately capture actual contact trends at individual and population levels, demonstrating that travel restriction in Wuhan played an important role in containing COVID-19. We reveal a strong correlation (Pearson coefficient 0.929) between daily confirmed cases and daily total contacts, which can be utilized as a new and efficient way to evaluate and predict the evolving epidemic situation of COVID-19. Further, we find that there is a prominent distinction of contact behaviors between the infected and uninfected contacted individuals, and design an infection risk evaluation framework to identify infected ones. This can help narrow down the search of high risk contacted individuals for quarantine. Our results indicate that user involvement has an explicit impact on individual-level contact trend estimation while minor impact on situation evaluation, offering guidelines for governments to implement contact tracing APPs.