Mapping spatial colleague connectivity patterns from individual-level registry data to inform regional pandemic interventions

Abstract A concern in infectious disease modelling is how accurately population mixing is incorporated, as it shapes the type and frequency of contacts through which infection spreads, and consequently, estimated intervention effectiveness. Although synthesizing mixing patterns from diary-based surveys is an established framework, geographical information is poorly or sparsely captured. Here we propose a generalizable workflow to quantify geographical connectivity from job registry data covering over 8 million Dutch working population. The derived colleague connectedness shows heterogeneous spatial patterns, quantified from the number of connections per municipality triplet, two residential municipalities and one shared workplace municipality. We demonstrate the utility of this spatial connectivity in signalling regions with elevated outbreak risks. Using SARS-CoV-2 Omicron as an example: a ten-fold increase in within-province connections is associated with a 12-day earlier (95% CI: 2 to 22 days) Omicron onset, and between-province connections associated with an 8-day earlier (95% CI: - 4 to 21 days) onset. These results suggest that the impact of regional interventions shifting spatial connectivity patterns should be expected to vary by region and type of intervention. Together, our findings draw attention of using this highly fine-grained spatial connectivity to enable more regionally tailored and network-targeted policy measures.