We developed an agent-based model to investigate the epidemic dynamics of Ebola virus disease (EVD) in Liberia and Sierra Leone from May 27 to December 21, 2014. The dynamics of the agent-based simulator evolve on small-world transmission networks of sizes equal to the population of each country, with adjustable densities to account for the effects of public health intervention policies and individual behavioral responses to the evolving epidemic. Based on time series of the official case counts from the World Health Organization (WHO), we provide estimates for key epidemiological variables by employing the so-called Equation-Free approach. The underlying transmission networks were characterized by rather random structures in the two countries with densities decreasing by ~19% from the early (May 27-early August) to the last period (mid October-December 21). Our estimates for the values of key epidemiological variables, such as the mean time to death, recovery and the case fatality rate, are very close to the ones reported by the WHO Ebola response team during the early period of the epidemic (until September 14) that were calculated based on clinical data. Specifically, regarding the effective reproductive number Re, our analysis suggests that until mid October, Re was above 2.3 in both countries; from mid October to December 21, Re dropped well below unity in Liberia, indicating a saturation of the epidemic, while in Sierra Leone it was around 1.9, indicating an ongoing epidemic. Accordingly, a ten-week projection from December 21 estimated that the epidemic will fade out in Liberia in early March; in contrast, our results flashed a note of caution for Sierra Leone since the cumulative number of cases could reach as high as 18,000, and the number of deaths might exceed 5,000, by early March 2015. However, by processing the reported data of the very last period (December 21, 2014-January 18, 2015), we obtained more optimistic estimates indicative of a remission of the epidemic in Sierra Leone, as reflected by the derived Re (~0.82, 95% CI: 0.81-0.83).
Author Profile
Constantinos Siettos
Institution: National Technical University of Athens
Affiliation: Faculty
Department: Applied Mathematics and Physical Sciences
City: Athens
Country: Greece
Professor Constantinos Siettos was born in Greece in 1971. He is Associate Professor in Computational Science and Engineering at the School of Applied Mathematics and Physical Sciences of the National Technical University of Athens (NTUA). He received a Diploma in 1994 and a Ph.D. in Nonlinear Dynamics and Control Theory from the Dept. of Chemical Engineering at NTUA in 2000. He also holds a major degree in Industrial Management from the University of Piraeus. From 2001 to 2004 he was a Post-Doctoral Fellow at Princeton University. There, he had a key role in a research effort originated in the group of Prof. Yannis Kevrekidis to "bridge the gap" between micro and macroscales of complex systems with important scientific implications. He has been the PI or co-PI on more than five projects mainly funded by the European Social Fund, the Hellenic General Secretariat for Research and Technology and the NTUA. He has published a total of 50 papers in leading international peer-reviewed scientific journals and 33 papers in peer-reviewed conference proceedings. For his work, he has been invited to give several invited presentations. He has been Lead-Guest Editor of the Journal of Applied Mathematics (2013-2014) and he is a member of the Editorial Board of Applied Mathematical and Computational Sciences, ISRN Journal of Computational Mathematics, ISRN Journal of Applied Mathematics and the Annual Review of Chaos Theory, Bifurcations and Dynamical systems. He is also reviewer in several leading peer-reviewed journals of Applied and Computational Mathematics and Engineering and he has been also invited to organize sessions in peer-reviewed international conferences. In 2012, Prof. Siettos was awarded a Fulbright Research Scholar grant for Academic Excellence to conduct research and lecturing on Complex System Dynamics at Princeton University.