Abstract
Multiple epidemiological models have been proposed to predict the spread of Ebola in West Africa. These models include consideration of counter-measures meant to slow and, eventually, stop the spread of the disease. Here, we examine one component of Ebola dynamics that is of ongoing concern – the transmission of Ebola from the dead to the living. We do so by applying the toolkit of mathematical epidemiology to analyze the consequences of post-death transmission. We show that underlying disease parameters cannot be inferred with confidence from early-stage incidence data (that is, they are not “identifiable”) because different parameter combinations can produce virtually the same epidemic trajectory. Despite this identifiability problem, we find robustly that inferences that don't account for post-death transmission tend to underestimate the basic reproductive number – thus, given the observed rate of epidemic growth, larger amounts of post-death transmission imply larger reproductive numbers. From a control perspective, we explain how improvements in reducing post-death transmission of Ebola may reduce the overall epidemic spread and scope substantially. Increased attention to the proportion of post-death transmission has the potential to aid both in projecting the course of the epidemic and in evaluating a portfolio of control strategies.
Highlights
Multiple epidemiological models have been proposed to predict the spread of Ebola in West Africa
In a SEIR model representation of Ebola virus disease (EVD) dynamics, the R class accounts for two types of individuals: those who recovered from the disease and those who have died from the disease
A SEIRD model includes an additional transmission route: dead individuals can transmit EVD to susceptible individuals at a rate bD over a period of infectiousness TD, after which they are permanently removed from the system via burial or loss of infectiousness
Summary
Multiple epidemiological models have been proposed to predict the spread of Ebola in West Africa. We show that underlying disease parameters cannot be inferred with confidence from early-stage incidence data (that is, they are not ‘‘identifiable’’) because different parameter combinations can produce virtually the same epidemic trajectory. Even with intervention and changes in behavior, a follow-up study by an independent group in October 2014 estimated that 100, 000 additional cases could be expected in Liberia alone by mid-December 2014, unless a coordinated, large-scale response is implemented rapidly[2] These predictions leveraged the structure of previous epidemiological models[3,4] that encapsulate the infection cycle of Ebola virus disease (EVD), by tracking the dynamics and interactions of different types of individuals within a population including Susceptible, Exposed, Infectious and Removed types. Uncertainty in the relative force of infection before and after death has a number of consequences for estimating R0 and the potential for control of the ongoing Ebola epidemic
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