Spatiotemporal dynamics of the Ebola epidemic in Guinea and implications for vaccination and disease elimination: a computational modeling analysis.
Among the three countries most affected by the Ebola virus disease outbreak in 2014-2015, Guinea presents an unusual spatiotemporal epidemic pattern, with several waves and a long tail in the decay of the epidemic incidence.



Here, we develop a stochastic agent-based model at the level of a single household that integrates detailed data on Guinean demography, hospitals, Ebola treatment units, contact tracing, and safe burial interventions. The microsimulation-based model is used to assess the effect of each control strategy and the probability of elimination of the epidemic according to different intervention scenarios, including ring vaccination with the recombinant vesicular stomatitis virus-vectored vaccine.



The numerical results indicate that the dynamics of the Ebola epidemic in Guinea can be quantitatively explained by the timeline of the implemented interventions. In particular, the early availability of Ebola treatment units and the associated isolation of cases and safe burials helped to limit the number of Ebola cases experienced by Guinea. We provide quantitative evidence of a strong negative correlation between the time series of cases and the number of traced contacts. This result is confirmed by the computational model that suggests that contact tracing effort is a key determinant in the control and elimination of the disease. In data-driven microsimulations, we find that tracing at least 5-10 contacts per case is crucial in preventing epidemic resurgence during the epidemic elimination phase. The computational model is used to provide an analysis of the ring vaccination trial highlighting its potential effect on disease elimination.



We identify contact tracing as one of the key determinants of the epidemic's behavior in Guinea, and we show that the early availability of Ebola treatment unit beds helped to limit the number of Ebola cases in Guinea.
Bruno Kessler Foundation, Via Sommarive 18, Trento, 38123, Italy.
Marco
M
Ajelli
Bruno Kessler Foundation, Via Sommarive 18, Trento, 38123, Italy.
Stefano
S
Merler
Bruno Kessler Foundation, Via Sommarive 18, Trento, 38123, Italy.
Laura
L
Fumanelli
Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, 360 Huntington Ave, Boston, MA, 02115, USA.
Ana
A
Pastore Y Piontti
Department of Biostatistics, University of Florida, 2004 Mowry Rd, Gainesville, FL, 32611, USA.
Natalie E
NE
Dean
Department of Biostatistics, University of Florida, 2004 Mowry Rd, Gainesville, FL, 32611, USA.
Ira M
IM
Longini
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N, Seattle, WA, 98109, USA.|School of Public Health, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, USA.
M Elizabeth
ME
Halloran
Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, 360 Huntington Ave, Boston, MA, 02115, USA. a.vespignani@neu.edu.|Institute for Quantitative Social Sciences at Harvard University, 1737 Cambridge St, Cambridge, MA, 02138, USA. a.vespignani@neu.edu.|Institute for Scientific Interchange Foundation, Via Alassio 11/c, Turin, 10126, Italy. a.vespignani@neu.edu.
Alessandro
A
Vespignani
D000293:Adolescent; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D060740:Disease Eradication / Q000379:methods*; D004196:Disease Outbreaks / Q000517:prevention & control; D029043:Ebolavirus / Q000187:drug effects; D058872:Epidemics / Q000517:prevention & control*; D005191:Family Characteristics; D016701:Guinea / Q000453:epidemiology; D019142:Hemorrhagic Fever, Ebola / Q000175:diagnosis / Q000453:epidemiology* / Q000517:prevention & control*; D006761:Hospitals; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D014611:Vaccination / Q000379:methods*
Computational modeling; Ebola epidemiology; Intervention strategies