Modelling Ebola Virus Transmission Dynamics

  • Zuhaimy Ismail, Philemon ManliuraDatilo

Abstract

Ebola had lately remained re-emergent epidemic in African countries, ravaging many lives. Before the recent outbreak in the Democratic Republic of Congo, Ebola broke out in Guinea in 2014.  The growth of Ebola epidemic had been widely investigated, using both stochastic and deterministic mathematical modeling approaches.  An alternative deterministic epidemic model that comprehensively incorporated compartments that can represent the dynamics of the disease amidst intervention and population dynamics is necessary.  This study formulated SVEQIHFR epidemic model that incorporated pre-exposed immunity and preventive or control compartments for purposes of assessing the effectiveness of these factors in managing Ebola growth in the West African countries.  In order to estimate model parameters and epidemic threshold, nonlinear least square method is used to fit the model to cumulative infected and death cases of Guinea, Liberia and Sierra Leone outbreaks.  The Ebola reproduction number was estimated to be 1.236 for Guinea, 1.751 for Liberia and 1.89 for Sierra Leone. This study implemented LHS/PRCC to carry out uncertainty analysis for the model estimated parameters to Ebola transmission.  Transmission coefficients, effective isolation, safe burial, effective identification and tracking of Ebola victims are critical to stall Ebola transmission.  The new Ebola epidemic growth model provided additional quantitative and qualitative information for planning preventive and control intervention strategies for managing Ebola outbreak.

Published
2020-06-01
How to Cite
Zuhaimy Ismail, Philemon ManliuraDatilo. (2020). Modelling Ebola Virus Transmission Dynamics. International Journal of Advanced Science and Technology, 29(10s), 3287-3296. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/20856
Section
Articles