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

158 results found

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, Vol: 22, Pages: 29-35, ISSN: 1755-4365

Outbreaks of novel pathogens such as SARS, pandemic influenza and Ebola require substantial investments in reactive interventions, with consequent implementation plans sometimes revised on a weekly basis. Therefore, short-term forecasts of incidence are often of high priority. In light of the recent Ebola epidemic in West Africa, a forecasting exercise was convened by a network of infectious disease modellers. The challenge was to forecast unseen “future” simulated data for four different scenarios at five different time points. In a similar method to that used during the recent Ebola epidemic, we estimated current levels of transmissibility, over variable time-windows chosen in an ad hoc way. Current estimated transmissibility was then used to forecast near-future incidence. We performed well within the challenge and often produced accurate forecasts. A retrospective analysis showed that our subjective method for deciding on the window of time with which to estimate transmissibility often resulted in the optimal choice. However, when near-future trends deviated substantially from exponential patterns, the accuracy of our forecasts was reduced. This exercise highlights the urgent need for infectious disease modellers to develop more robust descriptions of processes – other than the widespread depletion of susceptible individuals – that produce non-exponential patterns of incidence.

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, ECOLOGY LETTERS, Vol: 20, Pages: 275-292, ISSN: 1461-023X

Journal article

Kucharski A, Riley S, 2016, Reducing uncertainty about flavivirus infections, LANCET INFECTIOUS DISEASES, Vol: 17, Pages: 13-15, ISSN: 1473-3099

Journal article

International Ebola Response Team, 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 JM, 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 transmissions in West Africa: a retrospective observational study, PLOS Medicine, Vol: 13, ISSN: 1549-1277

BACKGROUND: The ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved.METHODS AND FINDINGS: Over 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola ("cases") were asked if they had exposure to other potential Ebola cases ("potential source contacts") in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO's response during the epidemic, and have been updated for publication. We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p < 0.001) between this proportion in a given district for a given month and the within-district transmission intensity, quantified by the estimated reproduction number (R). We also found a negative correlation (r = -0.37, p < 0.001) between R and the district proportion of hospitalised cases admitted within ≤4 days of symptom onset. These two proportions were not correlated, suggesting that reduced funeral attendance and faster hospitalisation independently influenced local transmission intensity. We were able to identify 14% of potential source contacts as cases in the

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

The threat of an influenza A virus pandemic stems from continual virus spillovers from reservoir species, a tiny fraction of which spark sustained transmission in humans. To date, no pandemic emergence of a new influenza strain has been preceded by detection of a closely related precursor in an animal or human. Nonetheless, influenza surveillance efforts are expanding, prompting a need for tools to assess the pandemic risk posed by a detected virus. The goal would be to use genetic sequence and/or biological assays of viral traits to identify those non-human influenza viruses with the greatest risk of evolving into pandemic threats, and/or to understand drivers of such evolution, to prioritize pandemic prevention or response measures. We describe such efforts, identify progress and ongoing challenges, and discuss three specific traits of influenza viruses (hemagglutinin receptor binding specificity, hemagglutinin pH of activation, and polymerase complex efficiency) that contribute to pandemic risk.

Journal article

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

BACKGROUND: Serum antibody to influenza can be used to identify past exposure and measure current immune status. The two most common methods for measuring this are the hemagglutination inhibition assay (HI) and the viral neutralization assay (NT), which have not been systematically compared for a large number of influenza viruses. METHODS: 151 study participants from near Guangzhou, China were enrolled in 2009 and provided serum. HI and NT assays were performed for 12 historic and recently circulating strains of seasonal influenza A. We compared titers using Spearman correlation and fit models to predict NT using HI results. RESULTS: We observed high positive mean correlation between HI and NT assays (Spearman's rank correlation, rho=0.86) across all strains. Correlation was highest within subtypes and within close proximity in time. Overall, an HI=20 corresponded to NT=10, and HI=40 corresponded to NT=20. Linear regression of log(NT) on log(HI) was statistically significant, with age modifying this relationship. Strain-specific area under a curve (AUC) indicated good accuracy (>80%) for predicting NT with HI. CONCLUSIONS: While we found high overall correspondence of titers between NT and HI assays for seasonal influenza A, no exact equivalence between assays could be determined. This was further complicated by correspondence between titers changing with age. These findings support generalized comparison of results between assays and give further support for use of the hemagglutination inhibition assay over the more resource intensive viral neutralization assay for seasonal influenza A, though attention should be given to the effect of age on these assays. This article is protected by copyright. All rights reserved.

Journal article

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

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: 1533-4406

Between December 2013 and April 2016, the largest epidemic of Ebola virus disease (EVD) to date generated more than 28,000 cases and more than 11,000 deaths in the large, mobile populations of Guinea, Liberia, and Sierra Leone. Tracking the rapid rise and slower decline of the West African epidemic has reinforced some common understandings about the epidemiology and control of EVD but has also generated new insights. Despite having more information about the geographic distribution of the disease, the risk of human infection from animals and from survivors of EVD remains unpredictable over a wide area of equatorial Africa. Until human exposure to infection can be anticipated or avoided, future outbreaks will have to be managed with the classic approach to EVD control — extensive surveillance, rapid detection and diagnosis, comprehensive tracing of contacts, prompt patient isolation, supportive clinical care, rigorous efforts to prevent and control infection, safe and dignified burial, and engagement of the community. Empirical and modeling studies conducted during the West African epidemic have shown that large epidemics of EVD are preventable — a rapid response can interrupt transmission and restrict the size of outbreaks, even in densely populated cities. The critical question now is how to ensure that populations and their health services are ready for the next outbreak, wherever it may occur. Health security across Africa and beyond depends on committing resources to both strengthen national health systems and sustain investment in the next generation of vaccines, drugs, and diagnostics.

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 DA, 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: 1091-6490

With more than 1,700 laboratory-confirmed infections, Middle East respiratory syndrome coronavirus (MERS-CoV) remains a significant threat for public health. However, the lack of detailed data on modes of transmission from the animal reservoir and between humans means that the drivers of MERS-CoV epidemics remain poorly characterized. Here, we develop a statistical framework to provide a comprehensive analysis of the transmission patterns underlying the 681 MERS-CoV cases detected in the Kingdom of Saudi Arabia (KSA) between January 2013 and July 2014. We assess how infections from the animal reservoir, the different levels of mixing, and heterogeneities in transmission have contributed to the buildup of MERS-CoV epidemics in KSA. We estimate that 12% [95% credible interval (CI): 9%, 15%] of cases were infected from the reservoir, the rest via human-to-human transmission in clusters (60%; CI: 57%, 63%), within (23%; CI: 20%, 27%), or between (5%; CI: 2%, 8%) regions. The reproduction number at the start of a cluster was 0.45 (CI: 0.33, 0.58) on average, but with large SD (0.53; CI: 0.35, 0.78). It was >1 in 12% (CI: 6%, 18%) of clusters but fell by approximately one-half (47% CI: 34%, 63%) its original value after 10 cases on average. The ongoing exposure of humans to MERS-CoV from the reservoir is of major concern, given the continued risk of substantial outbreaks in health care systems. The approach we present allows the study of infectious disease transmission when data linking cases to each other remain limited and uncertain.

Journal article

Pitzer VE, Aguas R, Riley S, Loeffen WL, Wood JL, 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

Pigs have long been hypothesized to play a central role in the emergence of novel human influenza A virus (IAV) strains, by serving as mixing vessels for mammalian and avian variants. However, the key issue of viral persistence in swine populations at different scales is ill understood. We address this gap using epidemiological models calibrated against seroprevalence data from Dutch finishing pigs to estimate the 'critical herd size' (CHS) for IAV persistence. We then examine the viral phylogenetic evidence for persistence by comparing human and swine IAV. Models suggest a CHS of approximately 3000 pigs above which influenza was likely to persist, i.e. orders of magnitude lower than persistence thresholds for IAV and other acute viruses in humans. At national and regional scales, we found much stronger empirical signatures of prolonged persistence of IAV in swine compared with human populations. These striking levels of persistence in small populations are driven by the high recruitment rate of susceptible piglets, and have significant implications for management of swine and for overall patterns of genetic diversity of IAV.

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: 1930-613X

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 DATet al., 2016, Cohort Profile: A study of influenza immunity in the urban and rural Guangzhou region of China: the Fluscape Study, INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, Vol: 46, ISSN: 0300-5771

Journal article

Riley S, Wu KM, 2016, Estimation of the basic reproductive number and mean serial interval of a novel pathogen in a small, well-observed discrete population, PLOS One, Vol: 11, ISSN: 1932-6203

Background: Accurately assessing the transmissibility and serial interval of an novelhuman pathogen is public health priority so that the timing and required strength ofinterventions may be determined. Recent theoretical work has focused on making bestuse of data from the initial exponential phase of growth of incidence in largepopulations.Methods: We measured generational transmissibility by the basic reproductivenumber R0 and the serial interval by its mean Tg. First, we constructed a simulationalgorithm for case data arising from a small population of known size with R0 and Tgalso known. We then developed an inferential model for the likelihood of these case dataas a function of R0 and Tg. The model was designed to capture a) any signal of theserial interval distribution in the initial stochastic phase b) the growth rate of theexponential phase and c) the unique combination of R0 and Tg that generates a specificshape of peak incidence when the susceptible portion of a small population is depleted.Findings: Extensive repeat simulation and parameter estimation revealed no biasin univariate estimates of either R0 and Tg. We were also able to simultaneouslyestimate both R0 and Tg. However, accurate final estimates could be obtained onlymuch later in the outbreak. In particular, estimates of Tg were considerably lessaccurate in the bivariate case until the peak of incidence had passed.Conclusions: The basic reproductive number and mean serial interval can beestimated simultaneously in real time during an outbreak of an emerging pathogen.Repeated application of these methods to small scale outbreaks at the start of anepidemic would permit accurate estimates of key parameters.

Journal article

Lessler J, Salje H, van Kerkhove M, Collins C, Lessler J, Salje H, Van Kerkhove MD, Ferguson NM, Cauchemez S, Rodriquez-Barraquer I, Hakeem R, Jombart T, Aguas R, Al-Barrak A, Cummings DATet al., 2016, Estimating the Severity and Subclinical Burden of Middle East Respiratory Syndrome Coronavirus Infection in the Kingdom of Saudi Arabia, American Journal of Epidemiology, Vol: 183, Pages: 657-663, ISSN: 1476-6256

Not all persons infected with Middle East respiratory syndrome coronavirus (MERS-CoV) develop severe symptoms, which likely leads to an underestimation of the number of people infected and an overestimation of the severity. To estimate the number of MERS-CoV infections that have occurred in the Kingdom of Saudi Arabia, we applied a statistical model to a line list describing 721 MERS-CoV infections detected between June 7, 2012, and July 25, 2014. We estimated that 1,528 (95% confidence interval (CI): 1,327, 1,883) MERS-CoV infections occurred in this interval, which is 2.1 (95% CI: 1.8, 2.6) times the number reported. The probability of developing symptoms ranged from 11% (95% CI: 4, 25) in persons under 10 years of age to 88% (95% CI: 72, 97) in those 70 years of age or older. An estimated 22% (95% CI: 18, 25) of those infected with MERS-CoV died. MERS-CoV is deadly, but this work shows that its clinical severity differs markedly between groups and that many cases likely go undiagnosed.

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: 1533-4406

Journal article

Pinsent A, Fraser C, Ferguson NM, Riley S, Pinsent A, Fraser C, Ferguson NM, Riley Set al., A systematic review of reported reassortantviral lineages of influenza A, BMC Infectious Diseases, ISSN: 1471-2334

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

Van Kerkhove MD, Cooper MJ, Cost AA, Sanchez JL, Riley Set al., 2015, Risk factors for severe outcomes among members of the United States military hospitalized with pneumonia and influenza, 2000-2012, VACCINE, Vol: 33, Pages: 6970-6976, ISSN: 0264-410X

Journal article

Nouvellet P, Garske T, Mills HL, Nedjati-Gilani G, Hinsley W, Blake IM, Van Kerkhove MD, Cori A, Dorigatti I, Jombart T, Riley S, Fraser C, Donnelly CA, Ferguson NMet al., 2015, The role of rapid diagnostics in managing Ebola epidemics, Nature, Vol: 528, Pages: S109-S116, ISSN: 0028-0836

Ebola emerged in West Africa around December 2013 and swept through Guinea, Sierra Leone and Liberia, giving rise to 27,748 confirmed, probable and suspected cases reported by 29 July 2015. Case diagnoses during the epidemic have relied on polymerase chain reaction-based tests. Owing to limited laboratory capacity and local transport infrastructure, the delays from sample collection to test results being available have often been 2 days or more. Point-of-care rapid diagnostic tests offer the potential to substantially reduce these delays. We review Ebola rapid diagnostic tests approved by the World Health Organization and those currently in development. Such rapid diagnostic tests could allow early triaging of patients, thereby reducing the potential for nosocomial transmission. In addition, despite the lower test accuracy, rapid diagnostic test-based diagnosis may be beneficial in some contexts because of the reduced time spent by uninfected individuals in health-care settings where they may be at increased risk of infection; this also frees up hospital beds. We use mathematical modelling to explore the potential benefits of diagnostic testing strategies involving rapid diagnostic tests alone and in combination with polymerase chain reaction testing. Our analysis indicates that the use of rapid diagnostic tests with sensitivity and specificity comparable with those currently under development always enhances control, whether evaluated at a health-care-unit or population level. If such tests had been available throughout the recent epidemic, we estimate, for Sierra Leone, that their use in combination with confirmatory polymerase chain-reaction testing might have reduced the scale of the epidemic by over a third.

Journal article

Riley P, Ben-Nun M, Linker JA, Cost AA, Sanchez JL, George D, Bacon DP, Riley Set al., 2015, Early characterization of the severity and transmissibility of pandemic influenza using clinical episode data from multiple populations, PLOS Computational Biology, Vol: 11, ISSN: 1553-734X

The potential rapid availability of large-scale clinical episode data during the next influenza pandemic suggests an opportunity for increasing the speed with which novel respiratory pathogens can be characterized. Key intervention decisions will be determined by both the transmissibility of the novel strain (measured by the basic reproductive number R0) and its individual-level severity. The 2009 pandemic illustrated that estimating individual-level severity, as described by the proportion pC of infections that result in clinical cases, can remain uncertain for a prolonged period of time. Here, we use 50 distinct US military populations during 2009 as a retrospective cohort to test the hypothesis that real-time encounter data combined with disease dynamic models can be used to bridge this uncertainty gap. Effectively, we estimated the total number of infections in multiple early-affected communities using the model and divided that number by the known number of clinical cases. Joint estimates of severity and transmissibility clustered within a relatively small region of parameter space, with 40 of the 50 populations bounded by: pC, 0.0133–0.150 and R0, 1.09–2.16. These fits were obtained despite widely varying incidence profiles: some with spring waves, some with fall waves and some with both. To illustrate the benefit of specific pairing of rapidly available data and infectious disease models, we simulated a future moderate pandemic strain with pC approximately ×10 that of 2009; the results demonstrating that even before the peak had passed in the first affected population, R0 and pC could be well estimated. This study provides a clear reference in this two-dimensional space against which future novel respiratory pathogens can be rapidly assessed and compared with previous pandemics.

Journal article

Kwok KO, Davoudi B, Riley S, Pourbohloul Bet al., 2015, Early real-time estimation of the basic reproduction number of emerging or reemerging infectious diseases in a community with heterogeneous contact pattern: Using data from Hong Kong 2009 H1N1 Pandemic Influenza as an illustrative example, PLOS One, Vol: 10, ISSN: 1932-6203

Emerging and re-emerging infections such as SARS (2003) and pandemic H1N1 (2009)have caused concern for public health researchers and policy makers due to the increasedburden of these diseases on health care systems. This concern has prompted the use ofmathematical models to evaluate strategies to control disease spread, making these modelsinvaluable tools to identify optimal intervention strategies. A particularly important quantityin infectious disease epidemiology is the basic reproduction number, R0. Estimation ofthis quantity is crucial for effective control responses in the early phase of an epidemic. Inour previous study, an approach for estimating the basic reproduction number in real timewas developed. This approach uses case notification data and the structure of potentialtransmission contacts to accurately estimate R0 from the limited amount of informationavailable at the early stage of an outbreak. Based on this approach, we extend the existingmethodology; the most recent method features intra- and inter-age groups contact heterogeneity.Given the number of newly reported cases at the early stage of the outbreak, withparsimony assumptions on removal distribution and infectivity profile of the diseases, experimentsto estimate real time R0 under different levels of intra- and inter-group contact heterogeneityusing two age groups are presented. We show that the new method convergesmore quickly to the actual value of R0 than the previous one, in particular when there ishigh-level intra-group and inter-group contact heterogeneity. With the age specific contactpatterns, number of newly reported cases, removal distribution, and information about the natural history of the 2009 pandemic influenza in Hong Kong, we also use the extendedmodel to estimate R0 and age-specific R0.

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

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

Lau SY, Cowling BJ, Cook AR, Riley Set al., 2015, Inferring influenza dynamics and control inhouseholds, Proceedings of the National Academy of Sciences of the United States of America, Vol: 112, Pages: 9094-9099, ISSN: 1091-6490

Household-based interventions are the mainstay of public health policy against epidemic respiratory pathogens when vaccination is not available. Although the efficacy of these interventions has traditionally been measured by their ability to reduce the proportion of household contacts who exhibit symptoms [household secondary attack rate (hSAR)], this metric is difficult to interpret and makes only partial use of data collected by modern field studies. Here, we use Bayesian transmission model inference to analyze jointly both symptom reporting and viral shedding data from a three-armed study of influenza interventions. The reduction in hazard of infection in the increased hand hygiene intervention arm was 37.0% [8.3%, 57.8%], whereas the equivalent reduction in the other intervention arm was 27.2% [−0.46%, 52.3%] (increased hand hygiene and face masks). By imputing the presence and timing of unobserved infection, we estimated that only 61.7% [43.1%, 76.9%] of infections met the case criteria and were thus detected by the study design. An assessment of interventions using inferred infections produced more intuitively consistent attack rates when households were stratified by the speed of intervention, compared with the crude hSAR. Compared with adults, children were 2.29 [1.66, 3.23] times as infectious and 3.36 [2.31, 4.82] times as susceptible. The mean generation time was 3.39 d [3.06, 3.70]. Laboratory confirmation of infections by RT-PCR was only able to detect 79.6% [76.5%, 83.0%] of symptomatic infections, even at the peak of shedding. Our results highlight the potential use of robust inference with well-designed mechanistic transmission models to improve the design of intervention studies.

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

To determine transmission potential of influenza A(H7N9) virus, we used symptom onset data to compare 2 waves of infection in China during 2013–2014. We found evidence of increased transmission potential in the second wave and showed that live bird market closure was significantly less effective in Guangdong than in other regions.

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: 1533-4406

Journal article

Heesterbeek H, Anderson RM, Andreasen V, Bansal S, De Angelis D, Dye C, Eames KTD, Edmunds WJ, Frost SDW, Funk S, Hollingsworth TD, House T, Isham V, Klepac P, Lessler J, Lloyd-Smith JO, Metcalf CJE, Mollison D, Pellis L, Pulliam JRC, Roberts MG, Viboud Cet al., 2015, Modeling infectious disease dynamics in the complex landscape of global health, SCIENCE, Vol: 347, Pages: 1216-U29, ISSN: 0036-8075

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

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

Riley S, Eames K, Isham V, Mollison D, Trapman Pet al., 2015, Five challenges for spatial epidemic models, EPIDEMICS, Vol: 10, Pages: 68-71, ISSN: 1755-4365

Journal article

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