Use of temporal contact graphs to understand the evolution of COVID-19 through contact tracing data

Abstract

Digital contact tracing has been recently advocated by China and many countries as part of digital prevention measures on COVID-19. Controversies have been raised about their effectiveness in practice as it remains open how they can be fully utilized to control COVID-19. In this article, we show that an abundance of information can be extracted from digital contact tracing for COVID-19 prevention and control. 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. The temporal contact graph reveals five time-varying indicators can accurately capture actual contact trends at population level, demonstrating that travel restrictions (e.g., city lockdown) in Wuhan played an important role in containing COVID-19. We reveal a strong correlation between the contacts level and the epidemic size, and estimate several significant epidemiological parameters (e.g., serial interval). We also show that user participation rate exerts higher influence on situation evaluation than user upload rate does, indicating a sub-sampled dataset would be as good at prediction. At individual level, however, the temporal contact graph plays a limited role, since the behavior distinction between the infected and uninfected individuals are not substantial. The revealed results can tell the effectiveness of digital contact tracing against COVID-19, providing guidelines for governments to implement interventions using information technology.

Editorial: Epidemiological considerations in COVID-19 forecasting

“…Many mathematical and computational models have been adapted to describe the epidemiological behavior of COVID-19 spread, including predicting the dynamics to assist efforts to counter rapid dissemination of the disease (3)(4)(5). Different modeling strategies to describe the pandemic include stochastic/probabilistic (3,(6)(7)(8)(9), and chaotic (10,11), with many models using ODEs (Ordinary Differential Equations) adapting the compartmental SIR (Susceptible, Infected, and Recovered) model (5,(12)(13)(14)(15)(16)(17). Many studies of COVID dynamics have been at national level, but spatially disaggregated approaches (e.g.…”