# Bayesian nonparametrics for stochastic epidemic models

## TL;DR

This paper explores Bayesian nonparametric methods for analyzing disease outbreak data, allowing flexible modeling of infection processes with explicit time dependence, moving beyond traditional parametric epidemic models.

## Contribution

It introduces Bayesian nonparametric approaches to estimate time-dependent infection processes in epidemic models, offering a flexible alternative to parametric assumptions.

## Key findings

- Demonstrates the effectiveness of nonparametric methods in epidemic modeling
- Provides a framework for estimating time-varying infection rates
- Enhances understanding of disease spread dynamics

## Abstract

The vast majority of models for the spread of communicable diseases are parametric in nature and involve underlying assumptions about how the disease spreads through a population. In this article we consider the use of Bayesian nonparametric approaches to analysing data from disease outbreaks. Specifically we focus on methods for estimating the infection process in simple models under the assumption that this process has an explicit time-dependence.