Network-based analysis of a small Ebola outbreak

Mark G. Burch; Karly A. Jacobsen; Joseph H. Tien; Grzegorz A. Rempała

doi:10.3934/mbe.2017005

Article Contents

2017, Volume 14, Issue 1: 67-77. Doi: 10.3934/mbe.2017005

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Network-based analysis of a small Ebola outbreak

1.
College of Public Health, The Ohio State University, Columbus, OH 43210, USA
2.
Mathematical Biosciences Institute, The Ohio State University, Columbus, OH 43210, USA
3.
Department of Mathematics and Mathematical Biosciences Institute, The Ohio State University, Columbus, OH 43210, USA
4.
College of Public Health and Mathematical Biosciences Institute, The Ohio State University, Columbus, OH 43210, USA

Received: November 05, 2015

Accepted: April 14, 2016

Published: September 2017

This research has been supported in part by the Mathematical Biosciences Institute and the National Science Foundation under grants RAPID DMS-1513489 and DMS-1440386.

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Abstract

We present a method for estimating epidemic parameters in network-based stochastic epidemic models when the total number of infections is assumed to be small. We illustrate the method by reanalyzing the data from the 2014 Democratic Republic of the Congo (DRC) Ebola outbreak described in Maganga et al. (2014).

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Mathematics Subject Classification: Primary: 92B; Secondary: 92C60.

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Figure 1. The empirical secondary case distribution in the DRC outbreak dataset (neglecting the index case), as given by Maganga et al.[22]

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Figure 2. Estimated posterior densities for the parameters of interest for the non-Markovian MCMC sampler. Green line denotes the posterior mean

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Figure 3. Final outbreak size distribution based on 20,000 simulations of the branching processes from the posterior parameter distribution. The actual outbreak size of 69 based on the DRC dataset (black line) is shown for comparison

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