تقرير
Bayesian Inference of Reproduction Number from Epidemiological and Genetic Data Using Particle MCMC
| العنوان: | Bayesian Inference of Reproduction Number from Epidemiological and Genetic Data Using Particle MCMC |
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| المؤلفون: | Gill, Alicia, Koskela, Jere, Didelot, Xavier, Everitt, Richard G. |
| سنة النشر: | 2023 |
| المجموعة: | Quantitative Biology Statistics |
| مصطلحات موضوعية: | Statistics - Methodology, Quantitative Biology - Genomics, Quantitative Biology - Populations and Evolution, Statistics - Applications, Statistics - Computation, 62P10, 65C05, 92D10, 92D30 |
| الوصف: | Inference of the reproduction number through time is of vital importance during an epidemic outbreak. Typically, epidemiologists tackle this using observed prevalence or incidence data. However, prevalence and incidence data alone is often noisy or partial. Models can also have identifiability issues with determining whether a large amount of a small epidemic or a small amount of a large epidemic has been observed. Sequencing data however is becoming more abundant, so approaches which can incorporate genetic data are an active area of research. We propose using particle MCMC methods to infer the time-varying reproduction number from a combination of prevalence data reported at a set of discrete times and a dated phylogeny reconstructed from sequences. We validate our approach on simulated epidemics with a variety of scenarios. We then apply the method to a real data set of HIV-1 in North Carolina, USA, between 1957 and 2019. Comment: 28 pages, 18 figures (44 pages, 35 figures including appendices) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2311.09838 |
| رقم الأكسشن: | edsarx.2311.09838 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |