Abstract
ABSTRACTThe largest outbreak of Ebola to date is the 2014 West Africa Ebola outbreak, with more than 10,000 cases and over 4000 deaths reported in Liberia alone. To control the spread of the outbreak, multiple interventions were implemented: identification and isolation of cases, contact tracing, quarantining of suspected contacts, proper personal protection, safely conducted burials, improved education, social awareness and individual protective measures. Devising rigorous methodologies for the evaluation of the effectiveness of the control measures implemented to stop an outbreak is of paramount importance. In this paper, we evaluate the effectiveness of the 2014 Ebola outbreak interventions. We rely on model selection to determine the best model that explains the 2014 Ebola outbreak data in Liberia which is the simplest model with a social distancing term. We couple structural and practical identifiability analysis with the computation of confidence intervals to pinpoint the uncertainty in the parameter estimations. Finally, we evaluate the efficacy of control measures using the Ebola model with social distancing. Among all the control measures, we find that social distancing had the most impact on the control of the 2014 Ebola epidemic in Libreria followed by isolation and quarantining.
Highlights
Emerging infectious diseases present challenge to public health every day
We evaluate the impact of the control measures implemented for the Ebola outbreak by applying the sensitivity methods to rank the public health interventions
We develop eight nested models of the Ebola outbreak where each model incorporates the two main characteristics of the Ebola infection: (i) the recovered individuals continue to be infectious for up to 7 weeks after recovery and (ii) deceased individuals who are not yet buried continue to transmit the disease if safe burial practices are not implemented during funerals
Summary
Emerging infectious diseases present challenge to public health every day. Mathematical models can facilitate combating these diseases by projecting potential cases, estimating key parameters of the outbreak, evaluating and optimizing control strategies. Chowell et al [7] suggest that the incidence growth for Ebola in West Africa, on local level, was not exponential, as it might be in an outbreak of influenza, but more reminiscent of the subexponential growth of HIV in the US This is probably due to public awareness and social distancing. We find that models with mass action or standard incidence, which comprise a large percentage of the models used, are not adequate to explain the full data sets for the Ebola outbreak and for the worldwide prevalence and death of HIV [22] For this reason, we use here a modified incidence with an exponential term, incorporated to depict social distancing as the outbreak unfolds. We evaluate the impact of the control measures implemented for the Ebola outbreak by applying the sensitivity methods to rank the public health interventions (education, quarantining, isolation, safe burial and social distancing).
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