ArticleScientific reports2021
A data-driven approach to measuring epidemiological susceptibility risk around the world.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- European countries policy responses against SARS-Cov-2 in the context of vaccinations.Scientific reports · 2025Article
- The importance of developing an integrated data-driven modelling platform for herbicide resistance research: A review.Heliyon · 2025Review
- Divergence and aggregation of ESG ratings: A survey.Open research Europe · 2025Review
- SEMbap: Bow-free covariance search and data de-correlation.PLoS computational biology · 2024Article
- Robust Multi-Dimensional Time Series Forecasting.Entropy (Basel, Switzerland) · 2024Article
- Assessing the length of hospital stay for patients with myasthenia gravis based on the data mining MARS approach.Frontiers in neurology · 2023Article
- A New Interactive Tool to Visualize and Analyze COVID-19 Data: The PERISCOPE Atlas.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Epidemic outbreaks are extreme events that become more frequent and severe, associated with large social and real costs. It is therefore important to assess whether countries are prepared to manage epidemiological risks. We use a fully data-driven approach to measure epidemiological susceptibility risk at the country level using time-varying information. We apply both principal component analysis (PCA) and dynamic factor model (DFM) to deal with the presence of strong cross-section dependence in the data. We conduct extensive in-sample model evaluations of 168 countries covering 17 indicators for the 2010-2019 period. The results show that the robust PCA method accounts for about 90% of total variability, whilst the DFM accounts for about 76% of the total variability. Our index could therefore provide the basis for developing risk assessments of epidemiological risk contagion. It could be also used by organizations to assess likely real consequences of epidemics with useful managerial implications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.