ArticlePLoS computational biology2020
Beyond ranking nodes: Predicting epidemic outbreak sizes by network centralities.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Assessing the usefulness of digital contact tracing using real-world contact data.Scientific reports · 2026Article
- Preoperative and postoperative imaging features in thoracic surgery: insights from a single-center study.Journal of medicine and life · 2025Article
- Harnessing multi-output machine learning approach and dynamical observables from network structure to optimize COVID-19 intervention strategies.Biology methods & protocols · 2025Article
- Distribution of centrality measures on undirected random networks via the cavity method.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Infection patterns in simple and complex contagion processes on networks.PLoS computational biology · 2024Article
- Static graph approximations of dynamic contact networks for epidemic forecasting.Scientific reports · 2024Article
- The spatiotemporal analysis of the population migration network in China, 2021.Infectious Disease Modelling · 2023Article
- The coming Omicron waves and factors affecting its spread after China reopening borders.BMC medical informatics and decision making · 2023Article
- A Two-Phase Feature Selection Method for Identifying Influential Spreaders of Disease Epidemics in Complex Networks.Entropy (Basel, Switzerland) · 2023Article
- Characteristics of the 100 largest modern zoonotic disease outbreaks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Article
- Unraveling the dynamic importance of county-level features in trajectory of COVID-19.Scientific reports · 2021Article
- Top influencers can be identified universally by combining classical centralities.Scientific reports · 2020Article
- Epidemic Forecasting via Hybrid Deep Learning With Unified Visibility and Temporal Graphs Under Stochastic Noise Modelling.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying important nodes for disease spreading is a central topic in network epidemiology. We investigate how well the position of a node, characterized by standard network measures, can predict its epidemiological importance in any graph of a given number of nodes. This is in contrast to other studies that deal with the easier prediction problem of ranking nodes by their epidemic importance in given graphs. As a benchmark for epidemic importance, we calculate the exact expected outbreak size given a node as the source. We study exhaustively all graphs of a given size, so do not restrict ourselves to certain generative models for graphs, nor to graph data sets. Due to the large number of possible nonisomorphic graphs of a fixed size, we are limited to ten-node graphs. We find that combinations of two or more centralities are predictive (R2 scores of 0.91 or higher) even for the most difficult parameter values of the epidemic simulation. Typically, these successful combinations include one normalized spectral centrality (such as PageRank or Katz centrality) and one measure that is sensitive to the number of edges in the graph.
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Registered trials
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