Temporal contact patterns and the implications for predicting superspreaders and planning of targeted outbreak control

Abstract

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 and developing a novel metric to measure contact retention over time and to identify highly connected individuals. 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.SignificanceDirectly transmitted infectious diseases spread through social contacts that can change over time. Simplifying these contact patterns when predicting outbreaks with models may bias results, and in turn conclusions on the effectiveness of control measures. An ongoing challenge is therefore how to capture key properties of complex and dynamic networks while also ensuring analysis is transparent and interpretable. 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.