In the wake of the COVID-19 pandemic many countries implemented containment measures to reduce disease transmission. Studies using digital data sources show that the mobility of individuals was effectively reduced in multiple countries. However, it remains unclear whether these reductions caused deeper structural changes in mobility networks and how such changes may affect dynamic processes on the network. Here we use movement data of mobile phone users to show that mobility in Germany has not only been reduced considerably: Lockdown measures caused substantial and long-lasting structural changes in the mobility network. We find that long-distance travel was reduced disproportionately strongly. The trimming of long-range network connectivity leads to a more local, clustered network and a moderation of the “small-world” effect. We demonstrate that these structural changes have a considerable effect on epidemic spreading processes by “flattening” the epidemic curve and delaying the spread to geographically distant regions.

In complex societies, individuals’ roles are reflected by interactions with other conspecifics. Honey bees (Apis mellifera) generally change tasks as they age, but developmental trajectories of individuals can vary drastically due to physiological and environmental factors. We introduce a succinct descriptor of an individual’s social network that can be obtained without interfering with the colony. This ‘network age’ accurately predicts task allocation, survival, activity patterns, and future behavior. We analyze developmental trajectories of multiple cohorts of individuals in a natural setting and identify distinct developmental pathways and critical life changes. Our findings suggest a high stability in task allocation on an individual level. We show that our method is versatile and can extract different properties from social networks, opening up a broad range of future studies. Our approach highlights the relationship of social interactions and individual traits, and provides a scalable technique for understanding how complex social systems function.

In many social systems, an individual's role is reflected by its interactions with other members of the group (Gordon 2010, Pinter-Wollmann et al. 2014, Krause 2015, Farine & Whitehead 2015. In honey bee colonies ( Apis mellifera ), workers generally perform different tasks as they age, yet there is high behavioral variation in same-aged bees (Seeley 1982, Robinson 1992, Huang and Robinson 1996, Johnson 2010. It is unknown how social interactions within the colony relate to an individual's tasks throughout her life. We propose a new method to extract a single number from each individual's interaction patterns in multimodal social networks that captures her current role in the colony. This "network age" is better than biological age at predicting task allocation (+99%), survival (+157%), and activity patterns (+44-108%) and even predicts task allocation up to one week (around a sixth of her typical lifespan) into the future. Network age identifies distinct developmental paths and task changes throughout a bee's life: We show that individuals change tasks gradually and exhibit high task repeatability, and that same aged bees form stable behavioral subgroups in which they predominantly interact with one another. While we derived interaction networks by automatically tracking a fully tagged colony, we show that tracking only 5% of the bees is sufficient to extract a meaningful representation of the individuals' interaction patterns, demonstrating the feasibility of our method for detecting complex social structures with reduced experimental effort. Since network age more accurately predicts task allocation than biological age, it could be used in experimental manipulations to quantify shifts in the timing of task transitions as a response. We extend our method to extract interaction patterns relevant to other attributes of the individuals, such as their mortality, opening up a broad range of possible applications. Our approach is a scalable instrument to study individual behavior through the lens of social interactions over time in honey bees and other complex social systems.

As the coronavirus disease 2019 (COVID-19) spread globally, emerging variants such as B.1.1.529 quickly became dominant worldwide. Sustained community transmission favors the proliferation of mutated sub-lineages with pandemic potential, due to cross-national mobility flows, which are responsible for consecutive cases surge worldwide. We show that, in the early stages of an emerging variant, integrating data from national genomic surveillance and global human mobility with large-scale epidemic modeling allows to quantify its pandemic potential, providing quantifiable indicators for pro-active policy interventions. We validate our framework on worldwide spreading variants and gain insights about the pandemic potential of BA.5, BA.2.75 and other sub- and lineages. We combine the different sources of information in a simple estimate of the pandemic delay and show that only in combination, the pandemic potentials of the lineages are correctly assessed relative to each other. Compared to a country-level epidemic intelligence, our scalable integrated approach, i.e. pandemic intelligence, permits to enhance global preparedness to contrast the pandemic of respiratory pathogens such as SARS-CoV-2.