Bayesian workflow for disease transmission modeling in Stan [tutorial].

Grinsztajn, Léo; Semenova, Elizaveta; Margossian, Charles C; Riou, Julien (2021). Bayesian workflow for disease transmission modeling in Stan [tutorial]. Statistics in medicine, 40(27), pp. 6209-6234. Wiley-Blackwell 10.1002/sim.9164

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This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and other infectious diseases in a Bayesian framework. Bayesian modeling provides a principled way to quantify uncertainty and incorporate both data and prior knowledge into the model estimates. Stan is an expressive probabilistic programming language that abstracts the inference and allows users to focus on the modeling. As a result, Stan code is readable and easily extensible, which makes the modeler's work more transparent. Furthermore, Stan's main inference engine, Hamiltonian Monte Carlo sampling, is amiable to diagnostics, which means the user can verify whether the obtained inference is reliable. In this tutorial, we demonstrate how to formulate, fit, and diagnose a compartmental transmission model in Stan, first with a simple susceptible-infected-recovered model, then with a more elaborate transmission model used during the SARS-CoV-2 pandemic. We also cover advanced topics which can further help practitioners fit sophisticated models; notably, how to use simulations to probe the model and priors, and computational techniques to scale-up models based on ordinary differential equations.

Item Type:

Journal Article (Further Contribution)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)

UniBE Contributor:

Riou, Julien Yannis

Subjects:

600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services

ISSN:

0277-6715

Publisher:

Wiley-Blackwell

Funders:

[4] Swiss National Science Foundation

Language:

English

Submitter:

Andrea Flükiger-Flückiger

Date Deposited:

17 Sep 2021 09:46

Last Modified:

03 Jan 2023 11:29

Publisher DOI:

10.1002/sim.9164

PubMed ID:

34494686

Uncontrolled Keywords:

Bayesian workflow compartmental models epidemiology infectious diseases

BORIS DOI:

10.48350/159351

URI:

https://boris.unibe.ch/id/eprint/159351

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