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      Potential for large outbreaks of Ebola virus disease

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          Highlights

          • We revisited data from the first known Ebola outbreak in Zaire in 1976.

          • Using a mathematical model, we estimated transmission rates in different settings.

          • Analysis suggests the person-to-person R0 was 1.34 (95% CI: 0.92–2.11).

          • Epidemiological conditions in 1976 could have generated a larger outbreak.

          Abstract

          Outbreaks of Ebola virus can cause substantial morbidity and mortality in affected regions. The largest outbreak of Ebola to date is currently underway in West Africa, with 3944 cases reported as of 5th September 2014. To develop a better understanding of Ebola transmission dynamics, we revisited data from the first known Ebola outbreak, which occurred in 1976 in Zaire (now Democratic Republic of Congo). By fitting a mathematical model to time series stratified by disease onset, outcome and source of infection, we were able to estimate several epidemiological quantities that have previously proved challenging to measure, including the contribution of hospital and community infection to transmission. We found evidence that transmission decreased considerably before the closure of the hospital, suggesting that the decline of the outbreak was most likely the result of changes in host behaviour. Our analysis suggests that the person-to-person reproduction number was 1.34 (95% CI: 0.92–2.11) in the early part of the outbreak. Using stochastic simulations we demonstrate that the same epidemiological conditions that were present in 1976 could have generated a large outbreak purely by chance. At the same time, the relatively high person-to-person basic reproduction number suggests that Ebola would have been difficult to control through hospital-based infection control measures alone.

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          Most cited references19

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          The spread of awareness and its impact on epidemic outbreaks.

          When a disease breaks out in a human population, changes in behavior in response to the outbreak can alter the progression of the infectious agent. In particular, people aware of a disease in their proximity can take measures to reduce their susceptibility. Even if no centralized information is provided about the presence of a disease, such awareness can arise through first-hand observation and word of mouth. To understand the effects this can have on the spread of a disease, we formulate and analyze a mathematical model for the spread of awareness in a host population, and then link this to an epidemiological model by having more informed hosts reduce their susceptibility. We find that, in a well-mixed population, this can result in a lower size of the outbreak, but does not affect the epidemic threshold. If, however, the behavioral response is treated as a local effect arising in the proximity of an outbreak, it can completely stop a disease from spreading, although only if the infection rate is below a threshold. We show that the impact of locally spreading awareness is amplified if the social network of potential infection events and the network over which individuals communicate overlap, especially so if the networks have a high level of clustering. These findings suggest that care needs to be taken both in the interpretation of disease parameters, as well as in the prediction of the fate of future outbreaks.
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            The reemergence of Ebola hemorrhagic fever, Democratic Republic of the Congo, 1995. Commission de Lutte contre les Epidémies à Kikwit.

            In May 1995, an international team characterized and contained an outbreak of Ebola hemorrhagic fever (EHF) in Kikwit, Democratic Republic of the Congo. Active surveillance was instituted using several methods, including house-to-house search, review of hospital and dispensary logs, interview of health care personnel, retrospective contact tracing, and direct follow-up of suspect cases. In the field, a clinical case was defined as fever and hemorrhagic signs, fever plus contact with a case-patient, or fever plus at least 3 of 10 symptoms. A total of 315 cases of EHF, with an 81% case fatality, were identified, excluding 10 clinical cases with negative laboratory results. The earliest documented case-patient had onset on 6 January, and the last case-patient died on 16 July. Eighty cases (25%) occurred among health care workers. Two individuals may have been the source of infection for >50 cases. The outbreak was terminated by the initiation of barrier-nursing techniques, health education efforts, and rapid identification of cases.
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              A likelihood-based method for real-time estimation of the serial interval and reproductive number of an epidemic.

              We present a method for the simultaneous estimation of the basic reproductive number, R(0), and the serial interval for infectious disease epidemics, using readily available surveillance data. These estimates can be obtained in real time to inform an appropriate public health response to the outbreak. We show how this methodology, in its most simple case, is related to a branching process and describe similarities between the two that allow us to draw parallels which enable us to understand some of the theoretical properties of our estimators. We provide simulation results that illustrate the efficacy of the method for estimating R(0) and the serial interval in real time. Finally, we implement our proposed method with data from three infectious disease outbreaks.
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                Author and article information

                Contributors
                Journal
                Epidemics
                Epidemics
                Epidemics
                Elsevier
                1755-4365
                1878-0067
                1 December 2014
                December 2014
                : 9
                : 70-78
                Affiliations
                [a ]Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
                [b ]Fogarty International Center, National Institutes of Health, United States
                [c ]London School of Hygiene and Tropical Medicine, London, United Kingdom
                Author notes
                [* ]Corresponding author. Tel.: +442079272407. anton.camacho@ 123456lshtm.ac.uk
                [** ]Corresponding author. Tel.: +442079588273. adam.kucharski@ 123456lshtm.ac.uk
                [1]

                These authors contributed equally to the work.

                Article
                S1755-4365(14)00052-8
                10.1016/j.epidem.2014.09.003
                4255970
                25480136
                0ac4f96f-449e-4393-9b0e-cb872b3195a3
                © 2014 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

                History
                : 21 August 2014
                : 11 September 2014
                : 16 September 2014
                Categories
                Article

                Public health
                ebola,1976 zaire outbreak,mathematical model,basic reproduction number
                Public health
                ebola, 1976 zaire outbreak, mathematical model, basic reproduction number

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