Temporal contact patterns and the implications for predicting superspreaders and planning of targeted outbreak control
Authors: Pung Rachael , Firth Josh A , Russell Timothy , Rogers Tim , Lee Vernon J , Kucharski Adam J 
Published: November 2023

AbstractEpidemic models often heavily simplify the dynamics of human-to-human contacts, but the resulting bias in outbreak dynamics – and hence requirements for control measures – remains unclear. Even if high-resolution temporal contact data were routinely used for modelling, the role of this temporal network structure towards outbreak control is not well characterised. We address this by assessing dynamic networks across varied social settings in three ways. Firstly, we characterised the distribution of retained contacts over consecutive timesteps by developing a novel metric, the “retention index”, which accounts for the change in the number of contacts over consecutive timesteps on a normalised scale with the extremes representing fully static and fully dynamic networks. Secondly, we described the repetition of contacts over the days by estimating the frequency of contact pairs occurring over the study duration. Thirdly, we distinguish the difference between ‘superspreader’ and infectious individuals driving ‘superspreading events’ by estimating the connectivity of an individual (i.e. individual has high connectivity in a timestep if he accounts for 80% of the contacts in the timestep) and the frequency of exhibiting high connectivity. Using 11 networks from 5 settings studied over 3–10 days, we estimated that more than 80% of the individuals in most settings were highly connected for only short periods. This suggests a challenge to identify ‘superspreaders’, and more individuals would need to be targeted as part of outbreak interventions to achieve the same reduction in transmission as predicted from a static network. Taking into account repeated contacts over multiple days, we estimated simple resource planning models might overestimate the number of contacts made by an infector by 20%–70%. In workplaces and schools, contacts in the same department accounted for most of the retained contacts. Hence, outbreak control measures would be better off targeting specific sub-populations in these settings to reduce transmission. In contrast, no obvious type of contact dominated the retained contacts in hospitals, so reducing the risk of disease introduction is critical to avoid disrupting the interdependent work functions. This study identified key epidemiological properties of temporal networks that potentially shape outbreak dynamics and illustrated the need for incorporating such properties in outbreak simulations.SignificanceDirectly transmitted infectious diseases spread through social contacts that can change over time. Modelling studies have largely focused on simplifying these contact patterns to predict outbreaks but the assumptions on contact patterns may bias results and, in turn, conclusions on the effectiveness of control measures. An ongoing challenge is, therefore, how to measure key properties of complex and dynamic networks to facilitate the development of network disease simulation models, which ensures that outbreak analysis is transparent and interpretable in the real-world context. To address this challenge, we analysed 11 networks from 5 different settings and developed new metrics to capture crucial epidemiological features of these networks. We showed that there is an inherent difficulty in identifying individual ‘superspreaders’ reliably in most networks. In addition, the key types of individuals driving transmission vary across settings, thus requiring different outbreak control measures to reduce disease transmission or the risk of introduction. Simple models to mimic disease transmission in temporal networks may not capture the repeated contacts over the days, and hence could incorrectly estimate the resources required for outbreak control. Our study characterised temporal network data in epidemiologically relevant ways and is a step towards developing simplified contact networks to capture real-world contact patterns for future outbreak simulation studies.