Abstract
Over the last months, cases of SARS-CoV-2 surged repeatedly in many countries but could often be controlled with nonpharmaceutical interventions including social distancing. We analyzed deidentified Global Positioning System (GPS) tracking data from 1.15 to 1.4 million cell phones in Germany per day between March and November 2020 to identify encounters between individuals and statistically evaluate contact behavior. Using graph sampling theory, we estimated the contact index (CX), a metric for number and heterogeneity of contacts. We found that CX, and not the total number of contacts, is an accurate predictor for the effective reproduction number R derived from case numbers. A high correlation between CX and R recorded more than 2 wk later allows assessment of social behavior well before changes in case numbers become detectable. By construction, the CX quantifies the role of superspreading and permits assigning risks to specific contact behavior. We provide a critical CX value beyond which R is expected to rise above 1 and propose to use that value to leverage the social-distancing interventions for the coming months.
Highlights
Over the last months, cases of SARS-CoV-2 surged repeatedly in many countries but could often be controlled with nonpharmaceutical interventions including social distancing
We found that a very broad distribution of contact numbers for individuals is responsible for the large contact index (CX) values before the lockdown
In this paper we have devised a graph-theory–based method that can take full advantage of the information contained in bulk cell phone data
Summary
Cases of SARS-CoV-2 surged repeatedly in many countries but could often be controlled with nonpharmaceutical interventions including social distancing. With an estimated incubation time of 5 d [3], a reported time delay of 6 d between symptoms and diagnosis based on laboratory tests [8], and additional time delay for reporting to authorities [9], laboratory testing does not appear to be sufficient for early outbreak detection and outbreak control given the short infection doubling time of SARS-Cov-2 of 1.4 to 2.5 d [10] In response to this problematic model, other methods must be considered that allow for the early detection and control of outbreaks. In contrast to contact-tracing apps, a blending of the mobility data with case data, which increases the risks of privacy breaches, Significance
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