Modeling the Impact of Interventions on an Epidemic of Ebola in Sierra Leone and Liberia

Abstract

Background: An Ebola outbreak of unparalleled size is currently affecting several countries in West Africa, and international efforts to control the outbreak are underway. However, the efficacy of these interventions, and their likely impact on an Ebola epidemic of this size, is unknown. Forecasting and simulation of these interventions may inform public health efforts. Methods: We use existing data from Liberia and Sierra Leone to parameterize a mathematical model of Ebola and use this model to forecast the progression of the epidemic, as well as the efficacy of several interventions, including increased contact tracing, improved infection control practices, the use of a hypothetical pharmaceutical intervention to improve survival in hospitalized patients. Findings: Model forecasts until Dec. 31, 2014 show an increasingly severe epidemic with no sign of having reached a peak. Modeling results suggest that increased contact tracing, improved infection control, or a combination of the two can have a substantial impact on the number of Ebola cases, but these interventions are not sufficient to halt the progress of the epidemic. The hypothetical pharmaceutical intervention, while impacting mortality, had a smaller effect on the forecasted trajectory of the epidemic. Interpretation: Near-term, practical interventions to address the ongoing Ebola epidemic may have a beneficial impact on public health, but they will not result in the immediate halting, or even obvious slowing of the epidemic. A long-term commitment of resources and support will be necessary to address the outbreak.

Modeling metapopulation dynamics of HIV epidemic on a linear lattice with nearest neighbour coupling

“…Their study revealed that given a population of 280 million people, it will be possible to prevent 31 million illnesses more by applying the optimal vaccination strategy when compared with the random mass vaccination. Meanwhile Caitlin, et al [16], used existing data from Liberia and Sierra Leone to parameterize a mathematical model of Ebola and used the model to forecast the progression of the epidemic, as well as the efficacy of the several interventions, including increased contact tracing, improved infection-control practices, the use of hypothetical pharmaceutical intervention to improve survival in hospitalized patients. Modeling results showed that increased contact tracing and improved infection control or the combination of the two can have a substantial impact on the number of Ebola cases, but the interventions were not sufficient to halt the progress of the epidemic.…”

Analytical Study of Optimal Control Intervention Strategies for Ebola Epidemic Model

“…Both, untreated patients in I(t) and hospitalized patients in H(t), may experience one of two outcomes: patients may die, with a chance of infecting others during the resulting funeral F (t) before being removed from this model R(t), or they may recover, at which point they are similarly removed. In system (2.1) the values β I , β H , β F , α, γ, δ, σ, ρ, χ, µ and T are the given positive parameters characterizing the Ebola epidemic ( [12], [14]). …”

“…In particular, we use values summarized in Table 1, which were used for modeling of the 2014 Ebola epidemics in Sierra Leone and Liberia ( [7], [14]). Moreover, Table 2 contains the values of the introduced constants.…”

“…Usually, in literature on modeling of the spread of Ebola epidemic, SIR and SEIR models are used ( [1], [3], [12], [14]). Arising here optimal control problems are analyzed and solved numerically ( [2], [6]).…”

Modelling the Effects of Mass Drug Administration on the Molecular Epidemiology of Schistosomes

“…Mathematical models can provide profound logistical, financial, temporal and biological benefits through, for instance, testing potential disease control strategies prior to final design and implementation (Rivers et al, 2014) or to improve current control methods (Turner et al, 2014, Luz et al, 2011. However, valuable models require accurate parameterisations and a reliable understanding of the disease dynamics under investigation and how these can, and do, respond to differential selection pressures.…”