Literature DB >> 25932579

Ebola superspreading.

Christian L Althaus1.   

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Year:  2015        PMID: 25932579      PMCID: PMC7158960          DOI: 10.1016/S1473-3099(15)70135-0

Source DB:  PubMed          Journal:  Lancet Infect Dis        ISSN: 1473-3099            Impact factor:   25.071


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Ousmane Faye and colleagues recently described the chains of transmission for 152 individuals infected with Ebola virus diseases in Guinea. The resulting transmission trees provide unique insights into the individual variation in the number of secondary cases generated by an infected index case. A better understanding of this variation provides crucial information about epidemic spread, the expected number of superspreading events, and the effects of control measures. The number of secondary cases in the transmission trees is highly skewed, with 72% of individuals not generating further cases (figure ). Fitting a negative binomial distribution to the data (appendix) provides maximum-likelihood estimates of the mean (0·95, 95% CI 0·57–1·34) and the dispersion parameter (k=0·18, 95% CI 0·10–0·26). The mean corresponds to the basic reproduction number (R 0) of the overall population. The estimated value of k, which is substantially smaller than 1, suggests that the distribution of the individual reproduction number is highly overdispersed. The value for Ebola virus disease is similar to that estimated for severe acute respiratory syndrome (k=0·16). This finding suggests that superspreading events for Ebola virus disease are an expected feature of the individual variation in infectiousness.
Figure

Distribution of the number of secondary cases and outbreak trajectories for Ebola virus disease

(A) The histogram represents the observed frequencies in the number of secondary cases as given by the transmission trees in Faye and colleagues' study. The line and dots correspond to the fitted negative binomial distribution. (B) Each line represents one of 200 stochastic realisations of epidemic trajectories. Dots show when the outbreak becomes extinct. A detailed analysis is reported in the appendix. EVD=Ebola virus disease.

Distribution of the number of secondary cases and outbreak trajectories for Ebola virus disease (A) The histogram represents the observed frequencies in the number of secondary cases as given by the transmission trees in Faye and colleagues' study. The line and dots correspond to the fitted negative binomial distribution. (B) Each line represents one of 200 stochastic realisations of epidemic trajectories. Dots show when the outbreak becomes extinct. A detailed analysis is reported in the appendix. EVD=Ebola virus disease. I simulated stochastic trajectories of Ebola virus disease outbreaks starting from one infected index case (figure). To this end, I drew the number of secondary cases for each case from the fitted negative binomial distribution (appendix). The time from disease onset in one case to disease onset in the next case was drawn from the reported gamma-distributed serial interval with a mean duration of 15·3 days. Although most outbreaks rapidly become extinct, some epidemic trajectories can reach to more than 100 infected cases. This finding is particularly remarkable because R 0 is less than 1, and shows the potential for explosive outbreaks of Ebola virus disease. R 0 during the early phase of the Ebola virus disease epidemic in Guinea has been estimated to be roughly 1·5. The transmission trees from Faye and colleagues were generated from data obtained between February and August, 2014, when the reproduction number was fluctuating around unity.1, 4 That scenario is similar to the present situation in parts of west Africa where the incidence is declining but new outbreaks still occur. The observed variation in individual infectiousness for Ebola virus disease means that although the probability of extinction is high, new index cases also have the potential for explosive regrowth of the epidemic. This online publication has been corrected. The corrected version first appeared at thelancet.com/infection on May 19, 2015
  5 in total

1.  Estimating the Reproduction Number of Ebola Virus (EBOV) During the 2014 Outbreak in West Africa.

Authors:  Christian L Althaus
Journal:  PLoS Curr       Date:  2014-09-02

2.  Chains of transmission and control of Ebola virus disease in Conakry, Guinea, in 2014: an observational study.

Authors:  Ousmane Faye; Pierre-Yves Boëlle; Emmanuel Heleze; Oumar Faye; Cheikh Loucoubar; N'Faly Magassouba; Barré Soropogui; Sakoba Keita; Tata Gakou; El Hadji Ibrahima Bah; Lamine Koivogui; Amadou Alpha Sall; Simon Cauchemez
Journal:  Lancet Infect Dis       Date:  2015-01-23       Impact factor: 25.071

3.  Superspreading and the effect of individual variation on disease emergence.

Authors:  J O Lloyd-Smith; S J Schreiber; P E Kopp; W M Getz
Journal:  Nature       Date:  2005-11-17       Impact factor: 49.962

4.  Phylodynamic analysis of ebola virus in the 2014 sierra leone epidemic.

Authors:  Erik Volz; Sergei Pond
Journal:  PLoS Curr       Date:  2014-10-24

5.  Ebola virus disease in West Africa--the first 9 months of the epidemic and forward projections.

Authors:  Bruce Aylward; Philippe Barboza; Luke Bawo; Eric Bertherat; Pepe Bilivogui; Isobel Blake; Rick Brennan; Sylvie Briand; Jethro Magwati Chakauya; Kennedy Chitala; Roland M Conteh; Anne Cori; Alice Croisier; Jean-Marie Dangou; Boubacar Diallo; Christl A Donnelly; Christopher Dye; Tim Eckmanns; Neil M Ferguson; Pierre Formenty; Caroline Fuhrer; Keiji Fukuda; Tini Garske; Alex Gasasira; Stephen Gbanyan; Peter Graaff; Emmanuel Heleze; Amara Jambai; Thibaut Jombart; Francis Kasolo; Albert Mbule Kadiobo; Sakoba Keita; Daniel Kertesz; Moussa Koné; Chris Lane; Jered Markoff; Moses Massaquoi; Harriet Mills; John Mike Mulba; Emmanuel Musa; Joel Myhre; Abdusalam Nasidi; Eric Nilles; Pierre Nouvellet; Deo Nshimirimana; Isabelle Nuttall; Tolbert Nyenswah; Olushayo Olu; Scott Pendergast; William Perea; Jonathan Polonsky; Steven Riley; Olivier Ronveaux; Keita Sakoba; Ravi Santhana Gopala Krishnan; Mikiko Senga; Faisal Shuaib; Maria D Van Kerkhove; Rui Vaz; Niluka Wijekoon Kannangarage; Zabulon Yoti
Journal:  N Engl J Med       Date:  2014-09-22       Impact factor: 91.245

  5 in total
  44 in total

1.  A mechanistic spatio-temporal framework for modelling individual-to-individual transmission-With an application to the 2014-2015 West Africa Ebola outbreak.

Authors:  Max S Y Lau; Gavin J Gibson; Hola Adrakey; Amanda McClelland; Steven Riley; Jon Zelner; George Streftaris; Sebastian Funk; Jessica Metcalf; Benjamin D Dalziel; Bryan T Grenfell
Journal:  PLoS Comput Biol       Date:  2017-10-30       Impact factor: 4.475

2.  The RAPIDD Ebola forecasting challenge: Model description and synthetic data generation.

Authors:  Marco Ajelli; Qian Zhang; Kaiyuan Sun; Stefano Merler; Laura Fumanelli; Gerardo Chowell; Lone Simonsen; Cecile Viboud; Alessandro Vespignani
Journal:  Epidemics       Date:  2017-09-20       Impact factor: 4.396

3.  A Quantitative Framework for Defining the End of an Infectious Disease Outbreak: Application to Ebola Virus Disease.

Authors:  Bimandra A Djaafara; Natsuko Imai; Esther Hamblion; Benido Impouma; Christl A Donnelly; Anne Cori
Journal:  Am J Epidemiol       Date:  2021-04-06       Impact factor: 4.897

4.  Modelling COVID-19 outbreak on the Diamond Princess ship using the public surveillance data.

Authors:  Shi Zhao; Peihua Cao; Daozhou Gao; Zian Zhuang; Weiming Wang; Jinjun Ran; Kai Wang; Lin Yang; Mohammad R Einollahi; Yijun Lou; Daihai He; Maggie H Wang
Journal:  Infect Dis Model       Date:  2022-05-26

5.  An open-access database of infectious disease transmission trees to explore superspreader epidemiology.

Authors:  Juliana C Taube; Paige B Miller; John M Drake
Journal:  PLoS Biol       Date:  2022-06-22       Impact factor: 9.593

6.  The source of individual heterogeneity shapes infectious disease outbreaks.

Authors:  Baptiste Elie; Christian Selinger; Samuel Alizon
Journal:  Proc Biol Sci       Date:  2022-05-04       Impact factor: 5.530

Review 7.  Considerations for use of Ebola vaccine during an emergency response.

Authors:  Jenny A Walldorf; Emily A Cloessner; Terri B Hyde; Adam MacNeil
Journal:  Vaccine       Date:  2017-09-07       Impact factor: 3.641

8.  Estimates of Outbreak Risk from New Introductions of Ebola with Immediate and Delayed Transmission Control.

Authors:  Damon J A Toth; Adi V Gundlapalli; Karim Khader; Warren B P Pettey; Michael A Rubin; Frederick R Adler; Matthew H Samore
Journal:  Emerg Infect Dis       Date:  2015-08       Impact factor: 6.883

9.  Spatial and temporal dynamics of superspreading events in the 2014-2015 West Africa Ebola epidemic.

Authors:  Max S Y Lau; Benjamin Douglas Dalziel; Sebastian Funk; Amanda McClelland; Amanda Tiffany; Steven Riley; C Jessica E Metcalf; Bryan T Grenfell
Journal:  Proc Natl Acad Sci U S A       Date:  2017-02-13       Impact factor: 11.205

10.  Transmission characteristics of MERS and SARS in the healthcare setting: a comparative study.

Authors:  Gerardo Chowell; Fatima Abdirizak; Sunmi Lee; Jonggul Lee; Eunok Jung; Hiroshi Nishiura; Cécile Viboud
Journal:  BMC Med       Date:  2015-09-03       Impact factor: 8.775

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