Imperial College London

Steven Riley

Faculty of MedicineSchool of Public Health

Professor of Infectious Disease Dynamics
 
 
 
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Contact

 

+44 (0)20 7594 2452s.riley

 
 
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Location

 

UG8Medical SchoolSt Mary's Campus

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Summary

 

Publications

Publication Type
Year
to

119 results found

Cori A, Donnelly CA, dorigatti, ferguson NM, fraser, garske, jombart, Nedjati-Gilani G, Nouvellet, Riley, Van Kerkhove, Mills, Blake IMet al., Key data for outbreak evaluation: building on the Ebola experience, Philosophical Transactions of the Royal Society B: Biological Sciences, ISSN: 1471-2970

Following the detection of an infectious disease outbreak, rapid epidemiological assessmentis critical to guidean effectivepublic health response. To understand the transmission dynamics and potential impact of an outbreak, several types of data are necessary. Here we build on experience gained inthe West AfricanEbolaepidemic and prior emerging infectious disease outbreaksto set out a checklist of data needed to: 1) quantify severity and transmissibility;2) characterise heterogeneities in transmission and their determinants;and 3) assess the effectiveness of different interventions.We differentiate data needs into individual-leveldata (e.g. a detailed list of reported cases), exposure data(e.g.identifying where / howcases may have been infected) and populationlevel data (e.g.size/demographicsof the population(s)affected andwhen/where interventions were implemented). A remarkable amount of individual-level and exposuredata was collected during the West African Ebola epidemic, which allowed the assessment of (1) and (2). However,gaps in population-level data (particularly around which interventions were applied whenand where)posed challenges to the assessment of (3).Herewehighlight recurrent data issues, give practical suggestions for addressingthese issues and discuss priorities for improvements in data collection in future outbreaks.

JOURNAL ARTICLE

Garske T, Cori A, Ariyarajah A, Blake I, Dorigatti I, Eckmanns T, Fraser C, Hinsley W, Jombart T, Mills H, Nedjati-Gilani G, Newton E, Nouvellet P, Perkins D, Riley S, Schumacher D, Shah A, Van Kerkhove M, Dye C, Ferguson N, Donnelly Cet al., Heterogeneities in the case fatality ratio in the West African Ebola outbreak 2013 – 2016, Philosophical Transactions of the Royal Society B: Biological Sciences, ISSN: 1471-2970

The 2013 –2016 Ebola outbreak in West Africa is the largest on record with 28,616confirmed, probable and suspected casesand 11,310 deaths officially recorded by 10 June 2016, the true burden likely considerablyhigher. The case fatality ratio (CFR, proportion of cases that are fatal) is a key indicator of disease severity useful for gauging the appropriate public health response and for evaluating treatment benefits,if estimated accurately.We analysed individual-level clinical outcome data fromGuinea, Liberia and Sierra Leoneofficially reported tothe World Health Organization. The overall mean CFR was 62.9% (95% CI: 61.9% to 64.0%)among confirmed cases with recorded clinical outcomes. Age was the most important modifier of survival probabilities, but country, stage of the epidemic and whether patients were hospitalisedalso played roles. We developed a statistical analysis to detect outliers in CFR between districts of residence and treatment centres, adjusting for known factors influencing survival and identified eight districtsand three treatment centres with a CFR significantly different from the average. From the current dataset we cannot determine whether the observed variation in CFR seen by district or treatment centre reflects real differences in survival, related to the quality of care or other factors,or was caused by differences in reporting practices or case ascertainment.

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Kucharski A, Riley S, 2017, Reducing uncertainty about flavivirus infections, LANCET INFECTIOUS DISEASES, Vol: 17, Pages: 13-15, ISSN: 1473-3099

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Lau MS, Dalziel BD, Funk S, McClelland A, Tiffany A, Riley S, Metcalf CJ, Grenfell BTet al., 2017, Spatial and temporal dynamics of superspreading events in the 2014-2015 West Africa Ebola epidemic., Proc Natl Acad Sci U S A, Vol: 114, Pages: 2337-2342

The unprecedented scale of the Ebola outbreak in Western Africa (2014-2015) has prompted an explosion of efforts to understand the transmission dynamics of the virus and to analyze the performance of possible containment strategies. Models have focused primarily on the reproductive numbers of the disease that represent the average number of secondary infections produced by a random infectious individual. However, these population-level estimates may conflate important systematic variation in the number of cases generated by infected individuals, particularly found in spatially localized transmission and superspreading events. Although superspreading features prominently in first-hand narratives of Ebola transmission, its dynamics have not been systematically characterized, hindering refinements of future epidemic predictions and explorations of targeted interventions. We used Bayesian model inference to integrate individual-level spatial information with other epidemiological data of community-based (undetected within clinical-care systems) cases and to explicitly infer distribution of the cases generated by each infected individual. Our results show that superspreaders play a key role in sustaining onward transmission of the epidemic, and they are responsible for a significant proportion ([Formula: see text]61%) of the infections. Our results also suggest age as a key demographic predictor for superspreading. We also show that community-based cases may have progressed more rapidly than those notified within clinical-care systems, and most transmission events occurred in a relatively short distance (with median value of 2.51 km). Our results stress the importance of characterizing superspreading of Ebola, enhance our current understanding of its spatiotemporal dynamics, and highlight the potential importance of targeted control measures.

JOURNAL ARTICLE

Nouvellet P, Cori A, Garske T, Blake IM, Dorigatti I, Hinsley W, Jombart T, Mills HL, Nedjati-Gilani G, Van Kerkhove MD, Fraser C, Donnelly CA, Ferguson NM, Riley Set al., 2017, A simple approach to measure transmissibility and forecast incidence, Epidemics, ISSN: 1755-4365

JOURNAL ARTICLE

Pepin KM, Kay SL, Golas BD, Shriner SS, Gilbert AT, Miller RS, Graham AL, Riley S, Cross PC, Samuel MD, Hooten MB, Hoeting JA, Lloyd-Smith JO, Webb CT, Buhnerkempe MGet al., 2017, Inferring infection hazard in wildlife populations by linking data across individual and population scales., Ecol Lett, Vol: 20, Pages: 275-292

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease.

JOURNAL ARTICLE

Yuan H-Y, Baguelin M, Kwok KO, Arinaminpathy N, van Leeuwen E, Riley Set al., 2017, The impact of stratified immunity on the transmission dynamics of influenza, Epidemics, ISSN: 1755-4365

JOURNAL ARTICLE

Agua-Agum J, Allegranzi B, Ariyarajah A, Aylward RB, Blake IM, Barboza P, Bausch D, Brennan RJ, Clement P, Coffey P, Cori A, Donnelly CA, Dorigatti I, Drury P, Durski K, Dye C, Eckmanns T, Ferguson NM, Fraser C, Garcia E, Garske T, Gasasira A, Gurry C, Gutierrez GJ, Hamblion E, Hinsley W, Holden R, Holmes D, Hugonnet S, Jombart T, Kelley E, Santhana R, Mahmoud N, Mills HL, Mohamed Y, Musa E, Naidoo D, Nedjati-Gilani G, Newton E, Norton I, Nouvellet P, Perkins D, Perkins M, Riley S, Schumacher D, Shah A, Minh T, Varsaneux O, Van Kerkhove MDet al., 2016, After Ebola in West Africa - Unpredictable Risks, Preventable Epidemics, NEW ENGLAND JOURNAL OF MEDICINE, Vol: 375, Pages: 587-596, ISSN: 0028-4793

JOURNAL ARTICLE

Agua-Agum J, Ariyarajah A, Aylward B, Bawo L, Bilivogui P, Blake IM, Brennan RJ, Cawthorne A, Cleary E, Clement P, Conteh R, Cori A, Dafae F, Dahl B, Dangou J-M, Diallo B, Donnelly CA, Dorigatti I, Dye C, Eckmanns T, Fallah M, Ferguson NM, Fiebig L, Fraser C, Garske T, Gonzalez L, Hamblion E, Hamid N, Hersey S, Hinsley W, Jambei A, Jombart T, Kargbo D, Keita S, Kinzer M, George FK, Godefroy B, Gutierrez G, Kannangarage N, Mills HL, Moller T, Meijers S, Mohamed Y, Morgan O, Nedjati-Gilani G, Newton E, Nouvellet P, Nyenswah T, Perea W, Perkins D, Riley S, Rodier G, Rondy M, Sagrado M, Savulescu C, Schafer IJ, Schumacher D, Seyler T, Shah A, Van Kerkhove MD, Wesseh CS, Yoti Zet al., 2016, Exposure Patterns Driving Ebola Transmission in West Africa: A Retrospective Observational Study, PLOS MEDICINE, Vol: 13, ISSN: 1549-1676

JOURNAL ARTICLE

Agua-Agum J, Ariyarajah A, Blake IM, Cori A, Donnelly CA, Dorigatti I, Dye C, Eck-Manns T, Ferguson NM, Fraser C, Garske T, Hinsley W, Jombart T, Mills HL, Nedjati-Gilani G, Newton E, Nouvellet P, Perkins D, Riley S, Schumacher D, Shah A, Thomas LJ, Van Kerkhove MDet al., 2016, Ebola Virus Disease among Male and Female Persons in West Africa, NEW ENGLAND JOURNAL OF MEDICINE, Vol: 374, Pages: 96-98, ISSN: 0028-4793

JOURNAL ARTICLE

Cauchemez S, Nouvellet P, Cori A, Jombart T, Garske T, Clapham H, Moore S, Mills HL, Salje H, Collins C, Rodriquez-Barraquer I, Riley S, Truelove S, Algarni H, Alhakeem R, AlHarbi K, Turkistani A, Aguas RJ, Cummings DAT, Van Kerkhove MD, Donnelly CA, Lessler J, Fraser C, Al-Barrak A, Ferguson NMet al., 2016, Unraveling the drivers of MERS-CoV transmission, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 113, Pages: 9081-9086, ISSN: 0027-8424

JOURNAL ARTICLE

Jiang CQ, Lessler J, Kim L, Kwok KO, Read JM, Wang S, Tan L, Hast M, Zhu H, Guan Y, Riley S, Cummings DAet al., 2016, Cohort Profile: A study of influenza immunity in the urban and rural Guangzhou region of China: the Fluscape Study., Int J Epidemiol

JOURNAL ARTICLE

Lipsitch M, Barclay W, Raman R, Russell CJ, Belser JA, Cobey S, Kasson PM, Lloyd-Smith JO, Maurer-Stroh S, Riley S, Beauchemin CAA, Bedford T, Friedrich TC, Handel A, Herfst S, Murcia PR, Roche B, Wilke CO, Russell CAet al., 2016, Viral factors in influenza pandemic risk assessment, ELIFE, Vol: 5, ISSN: 2050-084X

JOURNAL ARTICLE

Pinsent A, Fraser C, Ferguson NM, Riley Set al., 2016, A systematic review of reported reassortant viral lineages of influenza A, BMC INFECTIOUS DISEASES, Vol: 16, ISSN: 1471-2334

JOURNAL ARTICLE

Pitzer VE, Aguas R, Riley S, Loeffen WLA, Wood JLN, Grenfell BTet al., 2016, High turnover drives prolonged persistence of influenza in managed pig herds, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 13, ISSN: 1742-5689

JOURNAL ARTICLE

Riley P, Cost AA, Riley S, 2016, Intra-Weekly Variations of Influenza-Like Illness in Military Populations, MILITARY MEDICINE, Vol: 181, Pages: 364-368, ISSN: 0026-4075

JOURNAL ARTICLE

Riley S, 2016, Making high-res Zika maps, NATURE MICROBIOLOGY, Vol: 1

JOURNAL ARTICLE

Truelove S, Zhu H, Lessler J, Riley S, Read JM, Wang S, Kwok KO, Guan Y, Jiang CQ, Cummings DATet al., 2016, A comparison of hemagglutination inhibition and neutralization assays for characterizing immunity to seasonal influenza A, INFLUENZA AND OTHER RESPIRATORY VIRUSES, Vol: 10, Pages: 518-524, ISSN: 1750-2640

JOURNAL ARTICLE

Agua-Agum J, Ariyarajah A, Blake IM, Cori A, Donnelly CA, Dorigatti I, Dye C, Eckmanns T, Ferguson NM, Fowler RA, Fraser C, Garske T, Hinsley W, Jombart T, Mills HL, Murthy S, Nedjati-Gilani G, Nouvellet P, Pelletier L, Riley S, Schumacher D, Shah A, Van Kerkhove MDet al., 2015, Ebola Virus Disease among Children in West Africa, NEW ENGLAND JOURNAL OF MEDICINE, Vol: 372, Pages: 1274-1277, ISSN: 0028-4793

JOURNAL ARTICLE

Bedford T, Riley S, Barr IG, Broor S, Chadha M, Cox NJ, Daniels RS, Gunasekaran CP, Hurt AC, Kelso A, Klimov A, Lewis NS, Li X, McCauley JW, Odagiri T, Potdar V, Rambaut A, Shu Y, Skepner E, Smith DJ, Suchard MA, Tashiro M, Wang D, Xu X, Lemey P, Russell CAet al., 2015, Global circulation patterns of seasonal influenza viruses vary with antigenic drift, NATURE, Vol: 523, Pages: 217-U206, ISSN: 0028-0836

JOURNAL ARTICLE

Britton T, House T, Lloyd AL, Mollison D, Riley S, Trapman Pet al., 2015, Five challenges for stochastic epidemic models involving global transmission, EPIDEMICS, Vol: 10, Pages: 54-57, ISSN: 1755-4365

JOURNAL ARTICLE

Chretien J-P, Riley S, George DB, 2015, Mathematical modeling of the West Africa Ebola epidemic, eLife, Vol: 4, ISSN: 2050-084X

As of November 2015, the Ebola virus disease (EVD) epidemic that began in West Africa in late 2013 is waning. The human toll includes more than 28,000 EVD cases and 11,000 deaths in Guinea, Liberia, and Sierra Leone, the most heavily-affected countries. We reviewed 66 mathematical modeling studies of the EVD epidemic published in the peer-reviewed literature to assess the key uncertainties models addressed, data used for modeling, public sharing of data and results, and model performance. Based on the review, we suggest steps to improve the use of modeling in future public health emergencies.

JOURNAL ARTICLE

Eames K, Bansal S, Frost S, Riley Set al., 2015, Six challenges in measuring contact networks for use in modelling, EPIDEMICS, Vol: 10, Pages: 72-77, ISSN: 1755-4365

JOURNAL ARTICLE

Kucharski AJ, Lessler J, Read JM, Zhu H, Jiang CQ, Guan Y, Cummings DAT, Riley Set al., 2015, Estimating the Life Course of Influenza A (H3N2) Antibody Responses from Cross-Sectional Data, PLOS BIOLOGY, Vol: 13, ISSN: 1545-7885

JOURNAL ARTICLE

Kucharski AJ, Mills HL, Donnelly CA, Riley Set al., 2015, Transmission Potential of Influenza A(H7N9) Virus, China, 2013-2014, EMERGING INFECTIOUS DISEASES, Vol: 21, Pages: 852-855, ISSN: 1080-6040

JOURNAL ARTICLE

Lau MSY, Cowling BJ, Cook AR, Riley Set al., 2015, Inferring influenza dynamics and control in households, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 112, Pages: 9094-9099, ISSN: 0027-8424

JOURNAL ARTICLE

Lipsitch M, Donnelly CA, Fraser C, Blake IM, Cori A, Dorigatti I, Ferguson NM, Garske T, Mills HL, Riley S, Van Kerkhove MD, Hernan MAet al., 2015, Potential Biases in Estimating Absolute and Relative Case-Fatality Risks during Outbreaks, PLOS NEGLECTED TROPICAL DISEASES, Vol: 9, ISSN: 1935-2735

JOURNAL ARTICLE

Lloyd-Smith JO, Funk S, McLean AR, Riley S, Wood JLNet al., 2015, Nine challenges in modelling the emergence of novel pathogens, EPIDEMICS, Vol: 10, Pages: 35-39, ISSN: 1755-4365

JOURNAL ARTICLE

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

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