The novel coronavirus (SARS-CoV-2), which was first discovered in Hubei, China in December 2019, has caused an ongoing pandemic. Due to pauci-symptomatic cases, the virus may spread invisibly in a community. In the absence of vaccination, non-pharmaceutical interventions (NPIs) like interpersonal distancing were implemented in several countries and have been key to effectively reduce viral spreading. In Germany after an exponential growth of case numbers in March 2020, NPIs were able to effectively control the pandemic and sufficiently reduced the daily reported new infections allowing for partial release of NPIs. We developed a novel statistical method to evaluate contacts between individuals, which is essential for virus transmission. We derived the contact index, an index for the intensity and heterogeneity of contact behavior from spatial proximity between individuals as proxy for physical interaction based on complex network science. We estimated the contact index from large-scale GPS mobile phone data of 1.15 to 1.4 million users in Germany per day (March to July 2020). A high correlation between the contact index and the effective reproduction number six days later could be observed (Pearson correlation r=0.96, P-value < 0.001 for all reported Pearson correlations). This correlation was observed in three different phases of the virus spread in Germany 1) the early phase of the first wave with the highest reproduction rate, 2) phase of strict NPIs (lockdown) with the lowest reproduction, 3) release of NPIs accompanied with an increase of reproduction. The results show that the contact index is able to model and potentially forecast the time evolution of the pandemic in Germany.
Rüdiger S, Konigorski S, Edelman J, Zernick D, Thieme A, Lippert C (2020). Predicting the SARS-CoV-2 effective reproduction number using bulk contact data from mobile phones. PNAS 118(31): e2026731118. https://doi.org/10.1073/pnas.2026731118.