ArticleBioinformatics (Oxford, England)2022
From viral evolution to spatial contagion: a biologically modulated Hawkes model.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- Parallel MCMC algorithms: theoretical foundations, algorithm design, case studies.Transactions of mathematics and its applications : a journal of the IMA · 2024Article
- On the surprising effectiveness of a simple matrix exponential derivative approximation, with application to global SARS-CoV-2.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Cumulative damage for multi-type epidemics and an application to infectious diseases.Journal of mathematical biology · 2023Article
- A quantum parallel Markov chain Monte Carlo.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
summaryMutations sometimes increase contagiousness for evolving pathogens. During an epidemic, scientists use viral genome data to infer a shared evolutionary history and connect this history to geographic spread. We propose a model that directly relates a pathogen's evolution to its spatial contagion dynamics-effectively combining the two epidemiological paradigms of phylogenetic inference and self-exciting process modeling-and apply this phylogenetic Hawkes process to a Bayesian analysis of 23 421 viral cases from the 2014 to 2016 Ebola outbreak in West Africa. The proposed model is able to detect individual viruses with significantly elevated rates of spatiotemporal propagation for a subset of 1610 samples that provide genome data. Finally, to facilitate model application in big data settings, we develop massively parallel implementations for the gradient and Hessian of the log-likelihood and apply our high-performance computing framework within an adaptively pre-conditioned Hamiltonian Monte Carlo routine. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.