Mapping spatial colleague connectivity patterns from individual-level registry data to inform regional pandemic interventions
- 1Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- 2Pandemic and Disaster Preparedness Center (PDPC), Delft, Rotterdam, The Netherlands
- 3Department of Public Administration and Sociology, Erasmus University Rotterdam, Rotterdam, The Netherlands
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 illustrate the epidemiological relevance of this spatial connectivity by using SARS-CoV-2 Omicron as an example: a two-fold increase in within-province connections was associated with a 3.7-day earlier (95% CI: 0.6 to 6.6 days) Omicron onset, and between-province connectivity was associated with a 2.5 days earlier (95% CI: -1.0 to 6.2 days) onset. Based on our estimates of spatial connectivity, we quantified the number of colleague connections that would be removed in case of regional mobility restrictions such as a lockdown: locking down the whole province Zeeland would remove 2.6% of colleague links at the national level while the city Amsterdam alone would remove 10.0%. In future modelling studies, these highly fine-grained spatial connectivity data could be used as spatial mixing matrices to more explicitly capture the connectedness and dependency between regions to inform more tailored policy measures. Author summary: Respiratory infectious disease outbreaks and pandemics pose a significant societal risk because of their direct health burden as well as the social and economic disruptions caused by measures to control transmission. To better prepare society for future pandemics, it is important to answer the question of which interventions should be implemented when, where and to which population groups, in order to reduce transmission effectively while limiting societal disruptions due to for example lockdowns. In this study, we examine how Dutch municipalities are connected through workplace-related connections, using unique country-wide registry data of where Dutch people live and where they travel for work. By showing which regions are more or less linked, we provide evidence that can support more targeted control measures, such as regional lockdowns and long-distance travel bans. Our approach could also be extended to other social relationships, e.g., school and family connections, and to other countries, such as Nordic countries where similar registry data are available. Together, our findings provide data-driven evidence on spatial connectivity patterns that can inform the design of targeted control measures as potential alternatives to nationwide mandates in future pandemics.
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BibTeX
@article{song2026mapping,
title = {{Mapping spatial colleague connectivity patterns from individual-level registry data to inform regional pandemic interventions}},
author = {PingPing Song and Sake J. de Vlas and Tom Emery and Luc E. Coffeng},
journal = {PLOS Computational Biology},
year = {2026},
month = aug,
doi = {10.1371/journal.pcbi.1014721},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014721},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Mapping spatial colleague connectivity patterns from individual-level registry data to inform regional pandemic interventions
AU - PingPing Song
AU - Sake J. de Vlas
AU - Tom Emery
AU - Luc E. Coffeng
PY - 2026
DA - 2026/08/31
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014721
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014721
AB - 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 illustrate the epidemiological relevance of this spatial connectivity by using SARS-CoV-2 Omicron as an example: a two-fold increase in within-province connections was associated with a 3.7-day earlier (95% CI: 0.6 to 6.6 days) Omicron onset, and between-province connectivity was associated with a 2.5 days earlier (95% CI: -1.0 to 6.2 days) onset. Based on our estimates of spatial connectivity, we quantified the number of colleague connections that would be removed in case of regional mobility restrictions such as a lockdown: locking down the whole province Zeeland would remove 2.6% of colleague links at the national level while the city Amsterdam alone would remove 10.0%. In future modelling studies, these highly fine-grained spatial connectivity data could be used as spatial mixing matrices to more explicitly capture the connectedness and dependency between regions to inform more tailored policy measures. Author summary: Respiratory infectious disease outbreaks and pandemics pose a significant societal risk because of their direct health burden as well as the social and economic disruptions caused by measures to control transmission. To better prepare society for future pandemics, it is important to answer the question of which interventions should be implemented when, where and to which population groups, in order to reduce transmission effectively while limiting societal disruptions due to for example lockdowns. In this study, we examine how Dutch municipalities are connected through workplace-related connections, using unique country-wide registry data of where Dutch people live and where they travel for work. By showing which regions are more or less linked, we provide evidence that can support more targeted control measures, such as regional lockdowns and long-distance travel bans. Our approach could also be extended to other social relationships, e.g., school and family connections, and to other countries, such as Nordic countries where similar registry data are available. Together, our findings provide data-driven evidence on spatial connectivity patterns that can inform the design of targeted control measures as potential alternatives to nationwide mandates in future pandemics.
SN - 1553-7358
ER -