Sequential Monte Carlo Filtering Estimation of Ebola Progression in West Africa

TL;DR

This paper applies a sequential Monte Carlo filtering approach with mechanistic models to estimate Ebola virus progression and key parameters in real-time, revealing a peak in the reproductive ratio during the outbreak.

Contribution

It introduces a multivariate sequential Monte Carlo filter that estimates disease states and parameters simultaneously using real-time data.

Findings

Identified a peak in the basic reproductive ratio during the outbreak.

Estimated the evolution of R0(t) over time.

Performed online inference as new data became available.

Abstract

We use a multivariate formulation of sequential Monte Carlo filter that utilizes mechanistic models for Ebola virus propagation and available incidence data to simultaneously estimate the disease progression states and the model parameters. This method has the advantage of performing the inference online as the new data becomes available and estimates the evolution of basic reproductive ratio $R_0(t)$ of the Ebola outbreak through time. Our analysis identifies a peak in the basic reproductive ratio close to the time when Ebola cases were reported in Europe and the USA.