An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices
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Background: The integration of empirical data in computational frameworks designed to model the spread of infectious diseases poses a number of challenges that are becoming more pressing with the increasing availability of high-resolution information on human mobility and contacts. This deluge of data has the potential to revolutionize the computational efforts aimed at simulating scenarios, designing containment strategies, and evaluating outcomes. However, the integration of highly detailed data sources yields models that are less transparent and general in their applicability. Hence, given a specific disease model, it is crucial to assess which representations of the raw data work best to inform the model, striking a balance between simplicity and detail. Methods: We consider high-resolution data on the face-to-face interactions of individuals in a pediatric hospital ward, obtained by using wearable proximity sensors. We simulate the spread of a disease in this community by using an SEIR model on top of different mathematical representations of the empirical contact patterns. At the most detailed level, we take into account all contacts between individuals and their exact timing and order. Then, we build a hierarchy of coarse-grained representations of the contact patterns that preserve only partially the temporal and structural information available in the data. We compare the dynamics of the SEIR model across these representations. Results: We show that a contact matrix that only contains average contact durations between role classes fails to reproduce the size of the epidemic obtained using the high-resolution contact data and also fails to identify the most at-risk classes. We introduce a contact matrix of probability distributions that takes into account the heterogeneity of contact durations between (and within) classes of individuals, and we show that, in the case study presented, this representation yields a good approximation of the epidemic spreading properties obtained by using the high-resolution data. Conclusions: Our results mark a first step towards the definition of synopses of high-resolution dynamic contact networks, providing a compact representation of contact patterns that can correctly inform computational models designed to discover risk groups and evaluate containment policies. We show in a typical case of a structured population that this novel kind of representation can preserve in simulation quantitative features of the epidemics that are crucial for their study and management.
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A patient graph may be constructed based on patient similarity, and patient embeddings learned by an LSTM-GNN architecture can be optimized to predict patient outcomes.
“Alternatively, a patient graph may be constructed based on the similarity of patients, and patient embeddings learned by an LSTM-GNN architecture are optimized to predict patient outcomes [210].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [210]
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Graph representation learning in biomedicine and healthcare
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- Authors
- Machens, A., Gesualdo, F., Rizzo, C., Tozzi, A. E., Barrat, A., & Cattuto, C.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Bambino Gesù Children's Hospital (IT)
- Istituto Superiore di Sanità (IT)
- Institute for Scientific Interchange (IT)
- Corresponding author
- Barrat, Alain
- Venue
- BMC Infectious Diseases
- Published
- 2013-04-23
- Volume
- 13
- Issue
- 1
- Pages
- 185-185
- ISSN
- 1471-2334 (ISSN-L)
- PMC
- PMC3640968
- OpenAlex
- W2158939861
- Cited by
- 125
- References
- 63
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 12 · 2023: 6 · 2022: 8 · 2021: 10 · 2020: 4 · 2019: 8 · 2018: 24 · 2017: 11 · 2016: 8 · 2015: 15 · 2014: 9 · 2013: 2
- FWCI
- 11.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Infectious Diseases (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion, cc-by)
- HAL (submittedVersion, other-oa)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- arXiv (Cornell University)
- Funders
- European Commission (317532)
- Agence Nationale de la Recherche (ANR-12, ANR-12-MONU-0018)
- Ministero della Salute
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mathematical and Theoretical Epidemiology and Ecology Models
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Keywords
- infectious disease transmission, dynamic contact networks, contact matrices, SEIR model, face-to-face interaction, pediatric ward, epidemic simulation, risk groups, containment policy, structured populations
- MeSH terms
- Algorithms, Communicable Diseases, Disease Outbreaks, Hospitals, Pediatric, Humans, Models, Theoretical, Contact Tracing
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