Bayesian Non-Parametric Inference for Infectious Disease Data

TL;DR

This paper introduces a Bayesian non-parametric framework for estimating infection rates over time in epidemics, effectively capturing features like seasonality and super-spreading without explicit parametric assumptions.

Contribution

It develops a novel Bayesian non-parametric approach using step-functions and B-splines to model infection rates from partially observed epidemic data.

Findings

Successfully applied to simulated datasets demonstrating accurate rate estimation.

Effectively identified epidemic features like seasonality and super-spreading.

Flexible modeling approach adaptable to various epidemic scenarios.

Abstract

We propose a framework for Bayesian non-parametric estimation of the rate at which new infections occur assuming that the epidemic is partially observed. The developed methodology relies on modelling the rate at which new infections occur as a function which only depends on time. Two different types of prior distributions are proposed namely using step-functions and B-splines. The methodology is illustrated using both simulated and real datasets and we show that certain aspects of the epidemic such as seasonality and super-spreading events are picked up without having to explicitly incorporate them into a parametric model.