Bayesian analysis of immune response dynamics with sparse time series data

TL;DR

This paper develops a Bayesian modeling approach combining MCMC and ABC to analyze sparse immune response time series data, providing new insights into vaccine efficacy mechanisms in HIV/SIV studies.

Contribution

It introduces novel dynamic models with time delays and a combined Bayesian fitting strategy tailored for sparse data, advancing immune response analysis.

Findings

Insights into immune response variability across subjects

Effective model fitting strategy for sparse data

Enhanced understanding of vaccine response dynamics

Abstract

In vaccine development, the temporal profiles of relative abundance of subtypes of immune cells (T-cells) is key to understanding vaccine efficacy. Complex and expensive experimental studies generate very sparse time series data on this immune response. Fitting multi-parameter dynamic models of the immune response dynamics-- central to evaluating mechanisms underlying vaccine efficacy-- is challenged by data sparsity. The research reported here addresses this challenge. For HIV/SIV vaccine studies in macaques, we: (a) introduce novel dynamic models of progression of cellular populations over time with relevant, time-delayed components reflecting the vaccine response; (b) define an effective Bayesian model fitting strategy that couples Markov chain Monte Carlo (MCMC) with Approximate Bayesian Computation (ABC)-- building on the complementary strengths of the two approaches, neither of which is effective alone; (c) explore questions of information content in the sparse time series for each of the model parameters, linking into experimental design and model simplification for future experiments; and (d) develop, apply and compare the analysis with samples from a recent HIV/SIV experiment, with novel insights and conclusions about the progressive response to the vaccine, and how this varies across subjects.