Mathematical modelling of the West African Ebola virus epidemic

Abstract

We present a variant of the SEIR (susceptible-exposed-infectious-recovered) stochastic population-based compartment model of epidemiology to capture the spatial transmission dynamics of the Ebola virus disease epidemic in Sierra Leone, Liberia, and Guinea. Using registered data from the World Health Organization (WHO) situation reports we attempt to capture the transmission dynamics and the spatial spread of the Ebola epidemic. The projected number of newly infected and death cases are estimated and presented. Our objective is to achieve optimal Bayesian tracking of Ebola epidemic in both space and time with data that is (a) irregularly aggregated, and (b) only episodic in its availability. We use Ebola disease incidence as a posterior from the WHO reports. The ensemble optimal statistical interpolation (EnOSI) data assimilation method has been shown to produce optimal Bayesian statistical tracking of emerging epidemics (Cobb et al., 2014). We observe that the prediction improves as data is assimilated over time. The analysis thus provides a realization conditioned on all prior data and newly arrived data. We also found that EnOSI can efficiently adjust its estimated spatial distribution of the number of infected, if and when the epidemic jumps to a new city.

* Indicates faculty mentor.