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In 2014, a major epidemic of human Ebola virus disease emerged in West
Africa, where human-to-human transmission has now been sustained for
greater than 12 months. In the summer of 2014, there was great uncertainty
about the answers to several key policy questions concerning the path to
containment. What is the relative importance of nosocomial transmission
compared with community-acquired infection? How much must hospital
capacity increase to provide care for the anticipated patient burden? To
which interventions will Ebola transmission be most responsive? What must
be done to achieve containment? In recent years, epidemic models have been
used to guide public health interventions. But, model-based policy relies
on high quality causal understanding of transmission, including the
availability of appropriate dynamic transmission models and reliable
reporting about the sequence of case incidence for model fitting, which
were lacking for this epidemic. To investigate the range of potential
transmission scenarios, we developed a multi-type branching process model
that incorporates key heterogeneities and time-varying parameters to
reflect changing human behavior and deliberate interventions in Liberia.
Ensembles of this model were evaluated at a set of parameters that were
both epidemiologically plausible and capable of reproducing the observed
trajectory. Results of this model suggested that epidemic outcome would
depend on both hospital capacity and individual behavior. Simulations
suggested that if hospital capacity was not increased, then transmission
might outpace the rate of isolation and the ability to provide care for
the ill, infectious, and dying. Similarly, the model suggested that
containment would require individuals to adopt behaviors that increase the
rates of case identification and isolation and secure burial of the
deceased. As of mid-October, it was unclear that this epidemic would be
contained even by 99% hospitalization at the planned hospital capacity. A
new version of the model, updated to reflect information collected during
October and November 2014, predicts a significantly more constrained set
of possible futures. This model suggests that epidemic outcome still
depends very heavily on individual behavior. Particularly, if future
patient hospitalization rates return to background levels (estimated to be
around 70%), then transmission is predicted to remain just below the
critical point around Reff ...
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