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      Determinants of Transmission Risk During the Late Stage of the West African Ebola Epidemic

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          Abstract

          Understanding risk factors for Ebola transmission is key for effective prediction and design of interventions. We used data on 860 cases in 129 chains of transmission from the latter half of the 2013–2016 Ebola epidemic in Guinea. Using negative binomial regression, we determined characteristics associated with the number of secondary cases resulting from each infected individual. We found that attending an Ebola treatment unit was associated with a 38% decrease in secondary cases (incidence rate ratio (IRR) = 0.62, 95% confidence interval (CI): 0.38, 0.99) among individuals that did not survive. Unsafe burial was associated with a higher number of secondary cases (IRR = 1.82, 95% CI: 1.10, 3.02). The average number of secondary cases was higher for the first generation of a transmission chain (mean = 1.77) compared with subsequent generations (mean = 0.70). Children were least likely to transmit (IRR = 0.35, 95% CI: 0.21, 0.57) compared with adults, whereas older adults were associated with higher numbers of secondary cases. Men were less likely to transmit than women (IRR = 0.71, 95% CI: 0.55, 0.93). This detailed surveillance data set provided an invaluable insight into transmission routes and risks. Our analysis highlights the key role that age, receiving treatment, and safe burial played in the spread of EVD.

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          Most cited references15

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          After Ebola in West Africa — Unpredictable Risks, Preventable Epidemics

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            Ebola Hemorrhagic Fever Transmission and Risk Factors of Contacts, Uganda1

            From August 2000 through January 2001, a large epidemic of Ebola hemorrhagic fever occurred in Uganda, with 425 cases and 224 deaths. Starting from three laboratory-confirmed cases, we traced the chains of transmission for three generations, until we reached the primary case-patients (i.e., persons with an unidentified source of infection). We then prospectively identified the other contacts in whom the disease had developed. To identify the risk factors associated with transmission, we interviewed both healthy and ill contacts (or their proxies) who had been reported by the case-patients (or their proxies) and who met the criteria set for contact tracing during surveillance. The patterns of exposure of 24 case-patients and 65 healthy contacts were defined, and crude and adjusted prevalence proportion ratios (PPR) were estimated for different types of exposure. Contact with the patient’s body fluids (PPR = 4.61%, 95% confidence interval 1.73 to 12.29) was the strongest risk factor, although transmission through fomites also seems possible.
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              Maximum Likelihood Estimation of the Negative Binomial Dispersion Parameter for Highly Overdispersed Data, with Applications to Infectious Diseases

              Background The negative binomial distribution is used commonly throughout biology as a model for overdispersed count data, with attention focused on the negative binomial dispersion parameter, k. A substantial literature exists on the estimation of k, but most attention has focused on datasets that are not highly overdispersed (i.e., those with k≥1), and the accuracy of confidence intervals estimated for k is typically not explored. Methodology This article presents a simulation study exploring the bias, precision, and confidence interval coverage of maximum-likelihood estimates of k from highly overdispersed distributions. In addition to exploring small-sample bias on negative binomial estimates, the study addresses estimation from datasets influenced by two types of event under-counting, and from disease transmission data subject to selection bias for successful outbreaks. Conclusions Results show that maximum likelihood estimates of k can be biased upward by small sample size or under-reporting of zero-class events, but are not biased downward by any of the factors considered. Confidence intervals estimated from the asymptotic sampling variance tend to exhibit coverage below the nominal level, with overestimates of k comprising the great majority of coverage errors. Estimation from outbreak datasets does not increase the bias of k estimates, but can add significant upward bias to estimates of the mean. Because k varies inversely with the degree of overdispersion, these findings show that overestimation of the degree of overdispersion is very rare for these datasets.
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                Author and article information

                Journal
                Am J Epidemiol
                Am. J. Epidemiol
                aje
                American Journal of Epidemiology
                Oxford University Press
                0002-9262
                1476-6256
                July 2019
                03 April 2019
                03 April 2019
                : 188
                : 7
                : 1319-1327
                Affiliations
                [1 ]Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
                [2 ]World Health Organization, Geneva, Switzerland
                [3 ]Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
                [4 ]Department of Biostatistics, University of Florida, Gainesville, Florida
                [5 ]World Health Organization Ebola Vaccination Team, Conakry, Guinea
                [6 ]Ministry of Health, Conakry, Guinea
                Author notes
                Correspondence to Dr. Rosalind M. Eggo, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT (e-mail: r.eggo@ 123456lshtm.ac.uk ).
                Author information
                http://orcid.org/0000-0002-0362-6717
                Article
                kwz090
                10.1093/aje/kwz090
                6601535
                30941398
                e9c764e0-d6f8-4f7c-999f-4e75c0b4d983
                © The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 05 September 2018
                : 26 March 2019
                : 27 March 2019
                Page count
                Pages: 9
                Categories
                Original Contributions

                Public health
                ebola,guinea,multiple imputation,regression analysis,risk factors
                Public health
                ebola, guinea, multiple imputation, regression analysis, risk factors

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