Semi-parametric modeling of SARS-CoV-2 transmission using tests, cases, deaths, and seroprevalence data

3 code implementations • 6 Sep 2020 • Jonathan Fintzi, Damon Bayer, Isaac Goldstein, Keith Lumbard, Emily Ricotta, Sarah Warner, Lindsay M. Busch, Jeffrey R. Strich, Daniel S. Chertow, Daniel M. Parker, Bernadette Boden-Albala, Alissa Dratch, Richard Chhuon, Nichole Quick, Matthew Zahn, Vladimir N. Minin

We devised a modeling framework for integrating SARS-CoV-2 diagnostics test and mortality time series data, as well as seroprevalence data from cross-sectional studies, and tested the importance of individual data streams for both inference and forecasting.

Applications Populations and Evolution

A Bayesian Phylogenetic Hidden Markov Model for B Cell Receptor Sequence Analysis

1 code implementation • 27 Jun 2019 • Amrit Dhar, Duncan K. Ralph, Vladimir N. Minin, Frederick A. Matsen IV

Statistical phylogenetic methods are often used to model the mutational dynamics of BCR sequence data, but these techniques do not consider all the complexities associated with B cell diversification such as the V(D)J rearrangement process.

Methodology Genomics Applications

Fitting stochastic epidemic models to gene genealogies using linear noise approximation

1 code implementation • 24 Feb 2019 • Mingwei Tang, Gytis Dudas, Trevor Bedford, Vladimir N. Minin

We propose a Bayesian model that combines phylodynamic inference and stochastic epidemic models, and achieves computational tractability by using a linear noise approximation (LNA) --- a technique that allows us to approximate probability densities of stochastic epidemic model trajectories.

19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology

1 code implementation • 28 Nov 2018 • Mathieu Fourment, Andrew F. Magee, Chris Whidden, Arman Bilge, Frederick A. Matsen IV, Vladimir N. Minin

The marginal likelihood of a model is a key quantity for assessing the evidence provided by the data in support of a model.

Locally-adaptive Bayesian nonparametric inference for phylodynamics

1 code implementation • 13 Aug 2018 • James R. Faulkner, Andrew F. Magee, Beth Shapiro, Vladimir N. Minin

We also use our models to reconstruct past changes in genetic diversity of human hepatitis C virus in Egypt and to estimate population size changes of ancient and modern steppe bison.

Methodology Populations and Evolution

Spatio-Temporal Analysis of Surveillance Data

1 code implementation • 1 Nov 2017 • Jon Wakefield, Tracy Qi Dong, Vladimir N. Minin

In this chapter, we consider space-time analysis of surveillance count data.

Applications Methodology

Using genotype abundance to improve phylogenetic inference

1 code implementation • 29 Aug 2017 • William S. DeWitt III, Luka Mesin, Gabriel D. Victora, Vladimir N. Minin, Frederick A. Matsen IV

Modern biological techniques enable very dense genetic sampling of unfolding evolutionary histories, and thus frequently sample some genotypes multiple times.

Efficient data augmentation for fitting stochastic epidemic models to prevalence data

2 code implementations • 26 Jun 2016 • Jonathan Fintzi, Xiang Cui, Jon Wakefield, Vladimir N. Minin

We present our algorithm in the context of multiple stochastic epidemic models in which the data are binomially sampled prevalence counts, and apply our method to data from an outbreak of influenza in a British boarding school.

Computation Populations and Evolution