Predicting the SARS-CoV-2 effective reproduction number using bulk contact data from mobile phones

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.

Automated summary

Analyzing GPS tracking data from Germany, we found that the contact index (CX) rather than the total number of contacts accurately predicts the effective reproduction number R derived from case numbers, enabling early assessment of social behavior. By quantifying the role of superspreading, the constructed CX allows for assigning risks to specific contact behaviors, and we suggest leveraging the CX value to strengthen social distancing interventions in the coming months.