Monday, October 19, 2015
Grand Ballroom EH (Hyatt Regency St. Louis at the Arch)
Poster Board # PS2-34

Kejing Jiang, Stanford University Department of Management Science and Engineering, Stanford, CA, Jason Andrews, Stanford University School of Medicine, Stanford, CA and Jeremy D. Goldhaber-Fiebert, PhD, Stanford Health Policy, Centers for Health Policy and Primary Care and Outcomes Research, Department of Medicine, Stanford University, Stanford, CA
Purpose: In the previous 4 decades, there have been 19 major outbreaks of Ebola virus disease, but none have approached the magnitude of the 2014 outbreak in Liberia nor have they occurred in such large, urban settings. For the 2014 outbreak, outcry erupted due to delays in implementing safe burial practices and social distancing interventions. While experts expect future urban outbreaks, empirical evaluations of alternative population control strategies are infeasible, necessitating simulation modeling approaches to aid preparedness. 

Method: We developed a 5-compartment dynamic transmission model of Ebola for the 2014 Liberian outbreak and performed literature review to characterize model inputs and their uncertainty. We matched 2-week moving averages of new Ebola cases reported by the World Health Organization both before widespread burial and social distancing interventions began in Liberia (prior to September 2014) as well as afterwards (through mid-May 2015) by performing 10,000 Neldor-Mead search calibrations from random starting sets of inputs. By simultaneously calibrating to both periods, we recovered natural history parameters and intervention effectiveness. The objective function of the calibration was a weighted sum-of-squares where weights were the inverse of the standard error of the observed estimates under the binomial distribution. For analyses of alternative timings of interventions, we sampled 1,000 calibrated parameter sets with replacement from the 10,000, weighting the sampling by an approximation of the likelihood function so that better-fitting sets were more likely to be sampled.

Result: Compared to the observed 10,604 cumulative Ebola cases from the current outbreak in Liberia, our model predicts 10,519 cases [95%CrI: 9,755-10,992]. If interventions had been implemented earlier by 1 month, total cases are predicted at 1,904 [95%CrI: 1,359-2,951]. At 2 months earlier, these figures are 485 cases [95%CrI: 273-1,041]. 

Conclusion: Initiating safe burial and social distancing interventions earlier via better surveillance and epidemic preparedness has the potential to substantially decrease the impact of future Ebola epidemics in urban African settings.