An Ensemble Trajectory Method for Real-Time Modeling and Prediction of Unfolding Epidemics: Analysis of the 2005 Marburg Fever Outbreak in Angola

Abstract

We propose a new methodology for the modeling and real time prediction of the course of unfolding epidemic outbreaks. The method posits a class of standard epidemic models and explores uncertainty in empirical data to set up a family of possible outbreak trajectories that span the probability distribution of models parameters and initial conditions. A genetic algorithm is used to estimate likely trajectories consistent with the data and reconstruct the probability distribution of model parameters. In this way the ensemble of trajectories allows for temporal extrapolation to produce estimates of future cases and deaths, with quantified levels of uncertainty. We apply this methodology to an outbreak of Marburg hemorrhagic fever in Angola during 2005 in order to estimate disease epidemiological parameters and assess the effects of interventions. Data for cases and deaths was compiled from World Health Organization as the epidemic unfolded. We describe the outbreak through a standard epidemic model used in the past for Ebola, a closely related viral pathogen. The application of our method allows us to make quantitative prognostics as the outbreak unfolds for the expected time to the end of the epidemic and final numbers of cases and fatalities, which were eventually confirmed. We provided a real time analysis of the effects of intervention and possible under reporting and place bounds on population movements necessary to guarantee that the epidemic did not regain momentum.