Assessing the usefulness of digital contact tracing using real-world contact data

The worldwide health emergency caused by SARS-CoV-2 has profoundly reshaped healthcare systems and social behaviors, leading many countries to implement digital contact tracing (DCT) technologies. This study assesses Peru's DCT strategy during COVID-19 by analyzing real-world data from 1.66 million users of the Perú en tus manos app, among whom 80,068 cases were confirmed. Although low adoption constrained individual-level tracing, the dataset allowed for an examination of macro-level mobility trends, showing how trip lengths and travel behaviors changed across different policy phases. It also facilitated the analysis of micro-level contact patterns using bipartite stream graphs, identifying that higher temporal connectivity and participation in smaller gatherings were associated with greater infection risk. The research further illustrates how socioeconomic disparities affected mobility and transmission dynamics, as lower-income populations displayed wider movement ranges and higher infection rates than more affluent groups. Beyond its original purpose of notifying individuals about potential exposures, the findings underscore the broader potential of DCT data to guide public health policies, improve resource distribution, and mitigate inequities in pandemic responses, even when user engagement is limited. To support ongoing research, we share a dataset that integrates reconstructed large-scale contact networks with infection statuses, seeking to advance the creation of more effective DCT solutions.