Modeling local vulnerability to COVID-19 in an outbreak setting
4 affiliations
- 1Data Science Institute (DSI), I-BioStat, Hasselt University, Agoralaan Gebouw D, 3590 Diepenbeek, Belgium
- 2Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam
- 3I-BioStat, Katholieke Universiteit Leuven, Leuven, Belgium
- 4The Flemish Agency for Care and Health, Brussels, Belgium
Abstract
Background: Identifying factors that affect the vulnerability of areas to COVID-19 infection is essential, as it helps to identify the locations or risk groups that require additional attention. While connectivity among regions is a crucial driver in the epidemic growth, it is also observed that differences amongst locations in the regions exist, with some locations impacted much more or much less when compared to the surrounding region. We aim to develop a statistical model for assessment of localities’ vulnerability to COVID-19 based on risk factors, which also accounts for the regional settlement of the virus.
Methodology: In this cross-sectional ecological study, to determine the local vulnerability to COVID-19 in small areas (Belgian statistical sector level) as compared to in the larger neighborhood (municipality level), we propose a model with the mean incidence decomposed into a local vulnerability component and a regional outbreak component. This is illustrated by analyzing the vulnerability of the Flemish and Brussels regions in Belgium during the second and third waves of the COVID-19 pandemic, from September 2020 to May 2021. We investigate three groups of risk factors: socio-demographic, socio-economic, and environmental factors. As data are incomplete, a sensitivity analysis is undertaken to check the stability of model estimates.
Results: Results for the period September to December 2020 show that sectors with a higher median income and a larger proportion of non-Belgian residents were less vulnerable to COVID-19, while sectors with a higher proportion of pensioners, a lower education level, and/or a higher black carbon level had a higher vulnerability.
Conclusions: We present a simple yet efficient tool to identify areas with an increased vulnerability to COVID-19 in an outbreak setting. This can be helpful for policymakers to develop targeted public health interventions with localized lockdowns, enhanced testing and vaccination, and to plan resource allocations.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12889-026-28678-2.
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BibTeX
@article{nguyen2026modeling,
title = {{Modeling local vulnerability to COVID-19 in an outbreak setting}},
author = {Minh Hanh Nguyen and Thomas Neyens and Naïma Hammami and Geert Molenberghs and Christel Faes},
journal = {BMC Public Health},
year = {2026},
month = aug,
doi = {10.1186/s12889-026-28678-2},
url = {https://doi.org/10.1186/s12889-026-28678-2},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Modeling local vulnerability to COVID-19 in an outbreak setting
AU - Minh Hanh Nguyen
AU - Thomas Neyens
AU - Naïma Hammami
AU - Geert Molenberghs
AU - Christel Faes
PY - 2026
DA - 2026/08/01
JO - BMC Public Health
DO - 10.1186/s12889-026-28678-2
UR - https://doi.org/10.1186/s12889-026-28678-2
AB - Background: Identifying factors that affect the vulnerability of areas to COVID-19 infection is essential, as it helps to identify the locations or risk groups that require additional attention. While connectivity among regions is a crucial driver in the epidemic growth, it is also observed that differences amongst locations in the regions exist, with some locations impacted much more or much less when compared to the surrounding region. We aim to develop a statistical model for assessment of localities’ vulnerability to COVID-19 based on risk factors, which also accounts for the regional settlement of the virus. Methodology: In this cross-sectional ecological study, to determine the local vulnerability to COVID-19 in small areas (Belgian statistical sector level) as compared to in the larger neighborhood (municipality level), we propose a model with the mean incidence decomposed into a local vulnerability component and a regional outbreak component. This is illustrated by analyzing the vulnerability of the Flemish and Brussels regions in Belgium during the second and third waves of the COVID-19 pandemic, from September 2020 to May 2021. We investigate three groups of risk factors: socio-demographic, socio-economic, and environmental factors. As data are incomplete, a sensitivity analysis is undertaken to check the stability of model estimates. Results: Results for the period September to December 2020 show that sectors with a higher median income and a larger proportion of non-Belgian residents were less vulnerable to COVID-19, while sectors with a higher proportion of pensioners, a lower education level, and/or a higher black carbon level had a higher vulnerability. Conclusions: We present a simple yet efficient tool to identify areas with an increased vulnerability to COVID-19 in an outbreak setting. This can be helpful for policymakers to develop targeted public health interventions with localized lockdowns, enhanced testing and vaccination, and to plan resource allocations. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12889-026-28678-2.
SN - 1471-2458
ER -