We study the effect of network topology on the dynamics of the Ebola virus disease epidemic that broke out in the Spring of 2014 and swept through the countries of West Africa leaving behind a death toll of more than 11,000 people. Extending an agent-based model that we have developed and which has proven to approximate and forecast quite well the evolution of the epidemic in Liberia and Sierra Leone, we study the influence of two types of networks that are used to approximate the underlying social transmission network: Newman-Watts and Albert-Barabasi. Here, we focused on the case of Liberia. Key epidemiological parameters, namely, the mean incubation period, the time from the onset of symptoms to death, and the time from the onset of symptoms to recovery were taken to be the reported by the WHO response team, while the per-contact transmission probability, the case fatality ratio and the parameter used for the construction of the corresponding graphs were fitted to the time-series of total cases and deaths reported by WHO from May 27, 2014 to December 22, 2014. Our analysis shows that the agent-based simulations on Newman-Watts topology structures perform adequately in approximating the reported evolution for a wide range of probabilities of adding short-cuts, while with the Albert-Barabasi topology perform less efficiently.
ASJC Scopus subject areas
- Chemical Engineering(all)