Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models

Birth-death models play a key role in phylodynamic analysis for their interpretation in terms of key epidemiological parameters. In particular, models with piecewise-constant rates varying at different epochs in time, to which we refer as episodic birth-death-sampling (EBDS) models, are valuable for...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:PLoS computational biology Ročník 20; číslo 3; s. e1011640
Hlavní autoři: Shao, Yucai, Magee, Andrew F., Vasylyeva, Tetyana I., Suchard, Marc A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 01.03.2024
Public Library of Science (PLoS)
Témata:
Algorithms
Analysis
Biology and Life Sciences
Birth
Childbirth
Computer and Information Sciences
Computer applications
Death
Disease transmission
Ebola virus infections
Epidemics
Epidemiology
Hamiltonian function
Hemorrhagic Fever, Ebola - epidemiology
Humans
Infectious diseases
Inference
Influenza A
Influenza A Virus, H3N2 Subtype
Influenza, Human - epidemiology
Medicine and Health Sciences
Monte Carlo Method
Mortality
Parameters
Pathogens
Public health
Samplers
Sampling
Vectors
Viral diseases
Ukraine
United States
ISSN:1553-7358, 1553-734X, 1553-7358
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Birth-death models play a key role in phylodynamic analysis for their interpretation in terms of key epidemiological parameters. In particular, models with piecewise-constant rates varying at different epochs in time, to which we refer as episodic birth-death-sampling (EBDS) models, are valuable for their reflection of changing transmission dynamics over time. A challenge, however, that persists with current time-varying model inference procedures is their lack of computational efficiency. This limitation hinders the full utilization of these models in large-scale phylodynamic analyses, especially when dealing with high-dimensional parameter vectors that exhibit strong correlations. We present here a linear-time algorithm to compute the gradient of the birth-death model sampling density with respect to all time-varying parameters, and we implement this algorithm within a gradient-based Hamiltonian Monte Carlo (HMC) sampler to alleviate the computational burden of conducting inference under a wide variety of structures of, as well as priors for, EBDS processes. We assess this approach using three different real world data examples, including the HIV epidemic in Odesa, Ukraine, seasonal influenza A/H3N2 virus dynamics in New York state, America, and Ebola outbreak in West Africa. HMC sampling exhibits a substantial efficiency boost, delivering a 10- to 200-fold increase in minimum effective sample size per unit-time, in comparison to a Metropolis-Hastings-based approach. Additionally, we show the robustness of our implementation in both allowing for flexible prior choices and in modeling the transmission dynamics of various pathogens by accurately capturing the changing trend of viral effective reproductive number.
Bibliografie:new_version
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
The authors have declared that no competing interests exist.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1011640