Clustering of COVID-19 Testing and Incidence Time Series: A Method to Understand Epidemic Dynamics at the Territorial Scale
Abstract
Introduction: The COVID-19 pandemic highlighted socioeconomic disparities in France. Health measures were used to control the epidemic at national and local levels. We studied the temporal patterns of COVID-19 data at the sub-municipal level in Marseille. Methods: We obtained socioeconomic and COVID-19 data (testing and incidence rates) at the IRIS level (sub-municipal areas <5000 inhabitants) in Marseille. We built socioeconomic profiles using principal component analysis and clustering. We smoothed COVID-19 testing and incidence time series using functional data transformation, then grouped them by combining functional principal component analysis (FPCA) with clustering. We compared these clusters with the socioeconomic profiles, and examined testing in deprived IRIS with and without health mediation interventions. Results: We identified four socioeconomic profiles (deprived, intermediate-employee-family, intermediate-active-city-center, and privileged) and grouped the IRIS into nine testing clusters and eight incidence clusters. Two testing clusters, covering 50% of deprived IRIS and 38.1% of intermediate-employee-family IRIS, showed higher testing between June and October 2021 (health pass period). One incidence cluster, covering 52.9% of deprived IRIS, had high incidence between June and October 2021, then low incidence from March 2022 (no health measures). Deprived IRIS with health mediation interventions (31.4% of deprived IRIS) had higher testing rates from June 2021 onward than those without. Conclusion: This study showed that testing and incidence patterns coincided with public health measures (including health mediation interventions) and with socioeconomic profiles, suggesting a differential association across areas. This method is a useful tool to identify priority areas for targeted public health interventions during epidemics.
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BibTeX
@article{canton2026clustering,
title = {{Clustering of COVID-19 Testing and Incidence Time Series: A Method to Understand Epidemic Dynamics at the Territorial Scale}},
author = {Luka Canton and Pierre Schalkwijk and Jordi Landier and Stanislas Rebaudet and Eva Legendre and Emilie Mosnier and Pascal Handschumacher and Laurent Lehot and David Nerini and Florian Franke and Stève Nauleau and Guy Launoy and Cyrille Delpierre and Michelle Kelly Irving and Smaili Sabira and Stephanie Vandentorren and Jean Gaudart},
journal = {Epidemics},
year = {2026},
month = oct,
doi = {10.1016/j.epidem.2026.100948},
url = {https://hal.science/hal-05784423v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Clustering of COVID-19 Testing and Incidence Time Series: A Method to Understand Epidemic Dynamics at the Territorial Scale
AU - Luka Canton
AU - Pierre Schalkwijk
AU - Jordi Landier
AU - Stanislas Rebaudet
AU - Eva Legendre
AU - Emilie Mosnier
AU - Pascal Handschumacher
AU - Laurent Lehot
AU - David Nerini
AU - Florian Franke
AU - Stève Nauleau
AU - Guy Launoy
AU - Cyrille Delpierre
AU - Michelle Kelly Irving
AU - Smaili Sabira
AU - Stephanie Vandentorren
AU - Jean Gaudart
PY - 2026
DA - 2026/10/07
JO - Epidemics
DO - 10.1016/j.epidem.2026.100948
UR - https://hal.science/hal-05784423v1
AB - Introduction: The COVID-19 pandemic highlighted socioeconomic disparities in France. Health measures were used to control the epidemic at national and local levels. We studied the temporal patterns of COVID-19 data at the sub-municipal level in Marseille. Methods: We obtained socioeconomic and COVID-19 data (testing and incidence rates) at the IRIS level (sub-municipal areas <5000 inhabitants) in Marseille. We built socioeconomic profiles using principal component analysis and clustering. We smoothed COVID-19 testing and incidence time series using functional data transformation, then grouped them by combining functional principal component analysis (FPCA) with clustering. We compared these clusters with the socioeconomic profiles, and examined testing in deprived IRIS with and without health mediation interventions. Results: We identified four socioeconomic profiles (deprived, intermediate-employee-family, intermediate-active-city-center, and privileged) and grouped the IRIS into nine testing clusters and eight incidence clusters. Two testing clusters, covering 50% of deprived IRIS and 38.1% of intermediate-employee-family IRIS, showed higher testing between June and October 2021 (health pass period). One incidence cluster, covering 52.9% of deprived IRIS, had high incidence between June and October 2021, then low incidence from March 2022 (no health measures). Deprived IRIS with health mediation interventions (31.4% of deprived IRIS) had higher testing rates from June 2021 onward than those without. Conclusion: This study showed that testing and incidence patterns coincided with public health measures (including health mediation interventions) and with socioeconomic profiles, suggesting a differential association across areas. This method is a useful tool to identify priority areas for targeted public health interventions during epidemics.
SN - 1755-4365
SN - 1878-0067
ER -