Author: Christine Hedde-von Westernhagen, May 2023
Department of Methodology and Statistics, Utrecht University, 2022-2023. Supervisors: Javier Garcia-Bernardo and Ayoub Bagheri.
One of the most pressing problems of the recent past has been to understand the spread of the Covid-19 virus within and across populations. A popular strategy in investigating spreading phenomena have been network models, as they are able to represent complex social interactions. While previous studies have shown that network centrality measures are able to identify influential spreaders, it is not yet known if they can also be used to predict individual infection risks. However, information about individual risks is vital for the design of targeted interventions, and can give citizens more autonomy over their decisions. This study therefore focuses on the predictive abilities of centrality measures for individual infections. I investigate this issue leveraging the framework of multi-layer networks, which allows to explicitly consider human interaction to take place in different contexts simultaneously. Drawing on large-scale administrative data from the Netherlands, I find that existing centrality measures offer good predictions of relative infection risks, but are only weakly informative about the timing of individual infections. I therefore introduce a new Degree-based multi-layer centrality measure that takes into account the transmission rate of Covid-19 in different contexts. The new measure shows similar predictive performance as existing centrality measures, indicating that centrality measures alone are limited in their potential to predict the timing of infections, but could be used to complement other prediction approaches.
Please note:
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This repository is a publicly accessible version of my thesis project. It will remain public on GitHub as a documentation of the performed analyses, so they can be reproduced if necessary. It also stores the final version of the manuscript. There is NO DATA stored in this repository due to privacy regulations.
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To reproduce results shown in the thesis manuscript (
thesis_chw_v080523.pdf), all code in the fileanalyses.Rmdhas to be executed after obtaining access to the original data (info below). -
The code, data and output on the branch
example_analysesserved experimentation purposes in earlier stages of the project.
| Content | Description |
|---|---|
📂data_processed/ |
stores intermediary data objects (empty because discloses individual data) |
📂results/ |
stores results of analyses as displayed in thesis_chw_v080523.pdf as .xlsx |
analyses.Rmd |
script describing and executing all data preparation and analyses |
aux_functions.R |
script with auxiliary functions used in analyses.Rmd |
session_info.txt |
file containing R session info including package versions |
thesis_chw_v080523.pdf |
manuscript of thesis as submitted on 8 May 2023 |
Disclaimer: Results displayed in thesis_chw_v080523.pdf are based on calculations by the author using non-public micro-data from Statistics Netherlands (CBS). Under certain conditions, these micro-data are accessible for statistical and scientific research. For further information contact [email protected]. Permission to use the data for the presented analyses has been granted by the Ethical Review Board of the Faculty of Social and Behavioural Sciences of Utrecht University on November 8, 2022 (case numbers 22-1886, 22-1887, 22-1888).
CBS datasets used:
Network data (documentation):
- PersNw2018_v1.0_links_familie.csv
- PersNw2018_v1.0_links_huishouden.csv
- PersNw2018_v1.0_links_werk.csv
- PersNw2018_v1.0_linktype_labels.csv
Schools and their locations (documentation):
- BRINADRESSEN2020V1.sav
Registered students (documentation):
- INSCHRWPOTAB2020V2.sav
Covid tests:
- CoronIT_GGD_testdata_20210921.sav
This repository is maintained by Christine Hedde-von Westernhagen. The maintainer is solely responsible for all contents. For questions or comments please get in touch via [email protected], or open an issue in this repository.