Quantifying the impact of contact tracing on ebola spreading

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dc.contributor.author Montazeri Shahtori, Narges
dc.date.accessioned 2016-11-18T19:48:10Z
dc.date.available 2016-11-18T19:48:10Z
dc.date.issued 2016-12-01 en_US
dc.identifier.uri http://hdl.handle.net/2097/34540
dc.description.abstract Recent experience of Ebola outbreak of 2014 highlighted the importance of immediate response to impede Ebola transmission at its very early stage. To this aim, efficient and effective allocation of limited resources is crucial. Among standard interventions is the practice of following up with physical contacts of individuals diagnosed with Ebola virus disease -- known as contact tracing. In an effort to objectively understand the effect of possible contact tracing protocols, we explicitly develop a model of Ebola transmission incorporating contact tracing. Our modeling framework has several features to suit early–stage Ebola transmission: 1) the network model is patient–centric because when number of infected cases are small only the myopic networks of infected individuals matter and the rest of possible social contacts are irrelevant, 2) the Ebola disease model is individual–based and stochastic because at the early stages of spread, random fluctuations are significant and must be captured appropriately, 3) the contact tracing model is parameterizable to analyze the impact of critical aspects of contact tracing protocols. Notably, we propose an activity driven network approach to contact tracing, and develop a Monte-Carlo method to compute the basic reproductive number of the disease spread in different scenarios. Exhaustive simulation experiments suggest that while contact tracing is important in stopping the Ebola spread, it does not need to be done too urgently. This result is due to rather long incubation period of Ebola disease infection. However, immediate hospitalization of infected cases is crucial and requires the most attention and resource allocation. Moreover, to investigate the impact of mitigation strategies in the 2014 Ebola outbreak, we consider reported data in Guinea, one the three West Africa countries that had experienced the Ebola virus disease outbreak. We formulate a multivariate sequential Monte Carlo filter that utilizes mechanistic models for Ebola virus propagation to simultaneously estimate the disease progression states and the model parameters according to reported incidence data streams. This method has the advantage of performing the inference online as the new data becomes available and estimating the evolution of the basic reproductive ratio R₀(t) throughout the Ebola outbreak. Our analysis identifies a peak in the basic reproductive ratio close to the time of Ebola cases reports in Europe and the USA. en_US
dc.description.sponsorship This work has been supported in part by the National Science Foundation under Grant No. SCH:1513639. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Infectious diseases spread en_US
dc.subject Ebola virus disease en_US
dc.subject Mathematical modeling of infectious disease en_US
dc.subject State estimation en_US
dc.subject Monte Carlo methods en_US
dc.subject Sequential Monte Carlo method en_US
dc.subject Reproductive ratio en_US
dc.subject Basic reproductive number en_US
dc.subject Temporal network en_US
dc.subject Heterogeneous network en_US
dc.subject Contact tracing en_US
dc.subject Sensitivity en_US
dc.subject Specificity en_US
dc.title Quantifying the impact of contact tracing on ebola spreading en_US
dc.type Thesis en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Electrical and Computer Engineering en_US
dc.description.advisor Faryad Darabi Sahneh en_US
dc.date.published 2016 en_US
dc.date.graduationmonth December en_US

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