Reducing societal impacts of SARS-CoV-2 interventions through subnational implementation
- Utrecht University, Netherlands
- Erasmus MC, Netherlands
- University of Amsterdam, Netherlands
Abstract
To curb the initial spread of SARS-CoV-2, many countries relied on nation-wide implementation of non-pharmaceutical intervention measures, resulting in substantial socio-economic impacts. Potentially, subnational implementations might have had less of a societal impact, but comparable epidemiological impact. Here, using the first COVID-19 wave in the Netherlands as a case in point, we address this issue by developing a high-resolution analysis framework that uses a demographically-stratified population and a spatially-explicit, dynamic, individual contact-pattern based epidemiology, calibrated to hospital admissions data and mobility trends extracted from mobile phone signals and Google. We demonstrate how a subnational approach could achieve similar level of epidemiological control in terms of hospital admissions, while some parts of the country could stay open for a longer period. Our framework is exportable to other countries and settings, and may be used to develop policies on subnational approach as a better strategic choice for controlling future epidemics.
Data availability
Data associated with mobility and mixing reductions (Google mobility and PIENTER) [17, 34], age-stratified mixing matrices used in the analysis (POLYMOD) [9], and hospital admission data (NICE) publicly available as described in SI A.5, have been made available at the Data Repository https://osf.io/muj4q/. All analysis codes have been made available at https://github.com/MarkMDekker/covid_intervention_evaluation. Our analysis also uses mobility information as input. This dataset is owned by a commercial party (Mezuro) and can therefore not be made public. For the purpose of enabling readers to run our codes and obtaining comparable results, we have made synthetic mobility data available, also at the Data Repository https://osf.io/muj4q/. This synthetic data has been generated using a gravity model. For frequent travels, this is entirely standard, for infrequent visits square root of the distance is used in the numerator. The prefactor G in the standard gravity model is chosen as 0.5 to account for the double counting due to return journeys. For infrequent visits, mostly weekend trips, we have used G = 1/7. Request for the actual mobility data can be sent to info@mezuro.com as a proposal. Access to the data may require payment, and will certainly be subject to vetting related to privacy issues by GDPR (General Data Protection Regulation).
Article and author information
Author details
Funding
ZonMw (10430022010001)
- Sake J de Vlas
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, Dekker et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Reducing societal impacts of SARS-CoV-2 interventions through subnational implementation
eLife 12:e80819.
https://doi.org/10.7554/eLife.80819
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