ArticleProceedings. Mathematical, physical, and engineering sciences2018
Bayesian inference of spreading processes on networks.
Article in Proceedings. Mathematical, physical, and engineering sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Connecting mass-action models and network models for infectious diseases.PLoS computational biology · 2025Article
- Accounting for contact network uncertainty in epidemic inferences with Approximate Bayesian Computation.Applied network science · 2025Article
- Flexible Bayesian inference on partially observed epidemics.Journal of complex networks · 2024Article
- Active querying approach to epidemic source detection on contact networks.Scientific reports · 2023Article
- Inference on a Multi-Patch Epidemic Model with Partial Mobility, Residency, and Demography: Case of the 2020 COVID-19 Outbreak in Hermosillo, Mexico.Entropy (Basel, Switzerland) · 2023Article
- Scalable Approximate Bayesian Computation for Growing Network Models via Extrapolated and Sampled Summaries.Bayesian analysis · 2022Article
- Deep learning of contagion dynamics on complex networks.Nature communications · 2021Article
- Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic.BMC public health · 2020Article
- Network inference from population-level observation of epidemics.Scientific reports · 2020Article
- Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation.PLoS computational biology · 2020Article
- Modelling microbial infection to address global health challenges.Nature microbiology · 2019Review
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3 authors.
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Abstract
Infectious diseases are studied to understand their spreading mechanisms, to evaluate control strategies and to predict the risk and course of future outbreaks. Because people only interact with few other individuals, and the structure of these interactions influence spreading processes, the pairwise relationships between individuals can be usefully represented by a network. Although the underlying transmission processes are different, the network approach can be used to study the spread of pathogens in a contact network or the spread of rumours in a social network. We study simulated simple and complex epidemics on synthetic networks and on two empirical networks, a social/contact network in an Indian village and an online social network. Our goal is to learn simultaneously the spreading process parameters and the first infected node, given a fixed network structure and the observed state of nodes at several time points. Our inference scheme is based on approximate Bayesian computation, a likelihood-free inference technique. Our method is agnostic about the network topology and the spreading process. It generally performs well and, somewhat counter-intuitively, the inference problem appears to be easier on more heterogeneous network topologies, which enhances its future applicability to real-world settings where few networks have homogeneous topologies.
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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.