Heterogeneity is essential for contact tracing

Background (Dated: June 5, 2020) The COVID-19 pandemic is most often modelled by well-mixed models, sometimes stratified by age and work. People are, however, different from one another in terms of interaction frequency as well as in formation of social groups. This contact heterogeneity especially challenges models of contact tracing (CT), but also predictions of epidemic severity generally. We explore how heterogeneity affects CT effectiveness and overall epidemic severity, using a real-world contact network. Methods Utilizing smartphone proximity data from Danish university students, we simulate the spread of COVID-19 on a network with realistic contact structure. Two modes of network homogenization are implemented to probe effects of heterogeneity. We then simulate a CT scheme on the network and explore the impact of heterogeneity, testing probability and contact threshold for quarantining. Results Measuring contact heterogeneity, we find an exponential distribution which persists on a timescale of several weeks. Comparing the true network to edge-swapped and randomized versions, we find that heterogeneity decreases the severity of COVID-19 in general, and that it drastically improves CT. Conclusions To capture heterogeneity, it is necessary to reconsider disease transmission models. Our findings show that heterogeneity is essential for CT, and that CT is effective even if only the most frequent contacts can be tracked down. We find that contact heterogeneity impedes the spread of COVID-19 in comparison with well-mixed networks. In perspective, this means that fitting traditional SEIR models to epidemic data is likely to overestimate the severity.