Publications
255 results found
Kucharski AJ, Mills HL, Donnelly CA, et 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.
Agua-Agum J, Ariyarajah A, Blake IM, et al., 2015, Ebola virus disease among children in West Africa, New England Journal of Medicine, Vol: 372, Pages: 1274-1277, ISSN: 1533-4406
Heesterbeek H, Anderson RM, Andreasen V, et al., 2015, Modeling infectious disease dynamics in the complex landscape of global health, SCIENCE, Vol: 347, Pages: 1216-U29, ISSN: 0036-8075
- Author Web Link
- Cite
- Citations: 366
Kucharski AJ, Lessler J, Read JM, et al., 2015, Estimating the life course of Influenza A (H3N2) antibody responses from cross-sectional data, PLOS Biology, Vol: 13, ISSN: 1545-7885
Eames K, Bansal S, Frost S, et al., 2015, Six challenges in measuring contact networks for use in modelling, EPIDEMICS, Vol: 10, Pages: 72-77, ISSN: 1755-4365
- Author Web Link
- Cite
- Citations: 55
Lloyd-Smith JO, Funk S, McLean AR, et al., 2015, Nine challenges in modelling the emergence of novel pathogens, EPIDEMICS, Vol: 10, Pages: 35-39, ISSN: 1755-4365
- Author Web Link
- Cite
- Citations: 38
Britton T, House T, Lloyd AL, et al., 2015, Five challenges for stochastic epidemic models involving global transmission, EPIDEMICS, Vol: 10, Pages: 54-57, ISSN: 1755-4365
- Author Web Link
- Cite
- Citations: 35
Riley S, Eames K, Isham V, et al., 2015, Five challenges for spatial epidemic models, EPIDEMICS, Vol: 10, Pages: 68-71, ISSN: 1755-4365
- Author Web Link
- Cite
- Citations: 107
Agua-Agum J, Ariyarajah A, Aylward B, et al., 2015, West African Ebola epidemic after one year - slowing but not yet under control, New England Journal of Medicine, Vol: 372, Pages: 584-587, ISSN: 1533-4406
Sridhar S, Begom S, Hoschler K, et al., 2015, Longevity and Determinants of Protective Humoral Immunity after Pandemic Influenza Infection, AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, Vol: 191, Pages: 325-332, ISSN: 1073-449X
- Author Web Link
- Open Access Link
- Cite
- Citations: 19
Dye C, 2015, Goal-Directed Resuscitation in Septic Shock, NEW ENGLAND JOURNAL OF MEDICINE, Vol: 372, Pages: 189-189, ISSN: 0028-4793
- Author Web Link
- Cite
- Citations: 11
Lessler J, Rodriguez-Barraquer I, Cummings T, et al., 2014, Estimating potential incidence of MERS-CoV associated with Hajj pilgrims to Saudi Arabia, 2014, PLoS Currents, Vol: Edition 1, ISSN: 2157-3999
Between March and June 2014 the Kingdom of Saudi Arabia (KSA) had a large outbreak of MERS-CoV, renewing fears of a major outbreak during the Hajj this October. Using KSA Ministry of Health data, the MERS-CoV Scenario and Modeling Working Group forecast incidence under three scenarios. In the expected incidence scenario, we estimate 6.2 (95% Prediction Interval [PI]: 1–17) pilgrims will develop MERS-CoV symptoms during the Hajj, and 4.0 (95% PI: 0–12) foreign pilgrims will be infected but return home before developing symptoms. In the most pessimistic scenario, 47.6 (95% PI: 32–66) cases will develop symptoms during the Hajj, and 29.0 (95% PI: 17–43) will be infected but return home asymptomatic. Large numbers of MERS-CoV cases are unlikely to occur during the 2014 Hajj even under pessimistic assumptions, but careful monitoring is still needed to detect possible mass infection events and minimize introductions into other countries.
Russell CA, Kasson PM, Donis RO, et al., 2014, Improving pandemic influenza risk assessment, ELIFE, Vol: 3, ISSN: 2050-084X
- Author Web Link
- Cite
- Citations: 43
WHO Ebola Response Team, 2014, Ebola virus disease in West Africa — The first 9 months of the epidemic and forward projections, New England Journal of Medicine, Vol: 371, Pages: 1481-1495, ISSN: 0028-4793
BACKGROUNDOn March 23, 2014, the World Health Organization (WHO) was notified of an outbreak of Ebola virus disease (EVD) in Guinea. On August 8, the WHO declared the epidemic to be a “public health emergency of international concern.”METHODSBy September 14, 2014, a total of 4507 probable and confirmed cases, including 2296 deaths from EVD (Zaire species) had been reported from five countries in West Africa — Guinea, Liberia, Nigeria, Senegal, and Sierra Leone. We analyzed a detailed subset of data on 3343 confirmed and 667 probable Ebola cases collected in Guinea, Liberia, Nigeria, and Sierra Leone as of September 14.RESULTSThe majority of patients are 15 to 44 years of age (49.9% male), and we estimate that the case fatality rate is 70.8% (95% confidence interval [CI], 69 to 73) among persons with known clinical outcome of infection. The course of infection, including signs and symptoms, incubation period (11.4 days), and serial interval (15.3 days), is similar to that reported in previous outbreaks of EVD. On the basis of the initial periods of exponential growth, the estimated basic reproduction numbers (R0) are 1.71 (95% CI, 1.44 to 2.01) for Guinea, 1.83 (95% CI, 1.72 to 1.94) for Liberia, and 2.02 (95% CI, 1.79 to 2.26) for Sierra Leone. The estimated current reproduction numbers (R) are 1.81 (95% CI, 1.60 to 2.03) for Guinea, 1.51 (95% CI, 1.41 to 1.60) for Liberia, and 1.38 (95% CI, 1.27 to 1.51) for Sierra Leone; the corresponding doubling times are 15.7 days (95% CI, 12.9 to 20.3) for Guinea, 23.6 days (95% CI, 20.2 to 28.2) for Liberia, and 30.2 days (95% CI, 23.6 to 42.3) for Sierra Leone. Assuming no change in the control measures for this epidemic, by November 2, 2014, the cumulative reported numbers of confirmed and probable cases are predicted to be 5740 in Guinea, 9890 in Liberia, and 5000 in Sierra Leone, exceeding 20,000 in total.CONCLUSIONSThese data indicate that without drastic improvements in control measures, the numbers of
Wu KM, Riley S, 2014, Simulation-guided design of serological surveys of the cumulative incidence of influenza infection, BMC INFECTIOUS DISEASES, Vol: 14
- Author Web Link
- Cite
- Citations: 3
Kwok KO, Cowling BJ, Wei VWI, et al., 2014, Social contacts and the locations in which they occur as risk factors for influenza infection, PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 281, ISSN: 0962-8452
- Author Web Link
- Cite
- Citations: 34
Pinsent A, Blake IM, White MT, et al., 2014, Surveillance of low pathogenic novel H7N9 avian influenza in commercial poultry barns: detection of outbreaks and estimation of virus introduction time, BMC INFECTIOUS DISEASES, Vol: 14, ISSN: 1471-2334
BackgroundBoth high and low pathogenic subtype A avian influenza remain ongoing threats to the commercial poultry industry globally. The emergence of a novel low pathogenic H7N9 lineage in China presents itself as a new concern to both human and animal health and may necessitate additional surveillance in commercial poultry operations in affected regions.MethodsSampling data was simulated using a mechanistic model of H7N9 influenza transmission within commercial poultry barns together with a stochastic observation process. Parameters were estimated using maximum likelihood. We assessed the probability of detecting an outbreak at time of slaughter using both real-time polymerase chain reaction (rt-PCR) and a hemagglutinin inhibition assay (HI assay) before considering more intense sampling prior to slaughter. The day of virus introduction and R 0 were estimated jointly from weekly flock sampling data. For scenarios where R 0 was known, we estimated the day of virus introduction into a barn under different sampling frequencies.ResultsIf birds were tested at time of slaughter, there was a higher probability of detecting evidence of an outbreak using an HI assay compared to rt-PCR, except when the virus was introduced <2 weeks before time of slaughter. Prior to the initial detection of infection N s a m p l e = 50 (1%) of birds were sampled on a weekly basis once, but after infection was detected, N s a m p l e = 2000 birds (40%) were sampled to estimate both parameters. We accurately estimated the day of virus introduction in isolation with weekly and 2-weekly sampling.ConclusionsA strong sampling effort would be required to infer both the day of virus introduction and R 0. Such a sampling effort would not be required to estimate the day of virus introduction alone once R 0 was known, and sampling N s a m p l e = 50 of birds in the flock on a weekly or 2 weekly basis would be sufficient.
Read JM, Lessler J, Riley S, et al., 2014, Social mixing patterns in rural and urban areas of southern China, PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 281, ISSN: 0962-8452
- Author Web Link
- Cite
- Citations: 101
Riley S, 2014, Phylogenetic evidence for a mild H1 pandemic in the early 1900s, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 111, Pages: 7892-7893, ISSN: 0027-8424
- Author Web Link
- Cite
- Citations: 4
Kucharski AJ, Kwok KO, Wei VWI, et al., 2014, The Contribution of Social Behaviour to the Transmission of Influenza A in a Human Population, PLOS PATHOGENS, Vol: 10, ISSN: 1553-7366
- Author Web Link
- Cite
- Citations: 55
Mills HL, Riley S, 2014, The Spatial Resolution of Epidemic Peaks, PLOS COMPUTATIONAL BIOLOGY, Vol: 10, ISSN: 1553-734X
- Author Web Link
- Cite
- Citations: 18
Kwok KO, Jiang C, Tan L, et al., 2014, [An international collaborative study on influenza viruses antibody titers and contact patterns of individuals in rural and urban household of Guangzhou]., Zhonghua Liu Xing Bing Xue Za Zhi, Vol: 35, Pages: 433-436, ISSN: 0254-6450
OBJECTIVE: To describe the influenza viruses antibody levels and contact patterns of individuals in rural and urban regions of Guangzhou and to understand how contact patterns and other factors would correlate with the levels on the titers of antibody. METHODS: "Google Map" was used to randomly select the study points from the administrative areas in Guangzhou region. Each participant was required to provide 5 ml blood serum sample to be tested against different strains of H1N1 and H3N2 influenza viruses. RESULTS: 1) Using "Google map", 50 study points were selected but only 40 study points would meet the inclusion criteria. The cohort of this study consisted 856 households with 2 801 individuals. 1 821 participants (65% of the total number individuals in the cohort) completed the questionnaires. Among the 1 821 participants, 77.3% (1 407/1 821) and 22.7% (414/1 821) of them were from rural and urban areas respectively. There were more male participants in the rural but more female participants in the urban regions. Majority of the participants were from age group 18-59 followed by group 60 with aged 2-17 the least, in both rural and urban areas. 2) 78.1% (1 423/1 821) of the participants provided their serum samples. There appeared a strong correlation between age of the participants and the strength of their antibodies against that strain when a strain first circulated. In particular, seroprevalence was the highest at the age group 2-17. 3) 'Contact' was defined as persons having physical touch or/and conversation within one meter with the participants. Participants reported all having had large number of contacts. The proportion of participants having contacts with ten persons or above was the highest, ranging from 49.8% to 72.6%, particularly in age group 6-17. Compared to weekdays, participants had fewer contact persons on weekends. CONCLUSION: There was a strong correlation between the age of participants at the time when the strains first
Kucharski A, Mills H, Pinsent A, et al., 2014, Distinguishing Between Reservoir Exposure and Human-to-Human Transmission for Emerging Pathogens Using Case Onset Data., PLoS Curr, Vol: 6
Pathogens such as MERS-CoV, influenza A/H5N1 and influenza A/H7N9 are currently generating sporadic clusters of spillover human cases from animal reservoirs. The lack of a clear human epidemic suggests that the basic reproductive number R0 is below or very close to one for all three infections. However, robust cluster-based estimates for low R0 values are still desirable so as to help prioritise scarce resources between different emerging infections and to detect significant changes between clusters and over time. We developed an inferential transmission model capable of distinguishing the signal of human-to-human transmission from the background noise of direct spillover transmission (e.g. from markets or farms). By simulation, we showed that our approach could obtain unbiased estimates of R0, even when the temporal trend in spillover exposure was not fully known, so long as the serial interval of the infection and the timing of a sudden drop in spillover exposure were known (e.g. day of market closure). Applying our method to data from the three largest outbreaks of influenza A/H7N9 outbreak in China in 2013, we found evidence that human-to-human transmission accounted for 13% (95% credible interval 1%-32%) of cases overall. We estimated R0 for the three clusters to be: 0.19 in Shanghai (0.01-0.49), 0.29 in Jiangsu (0.03-0.73); and 0.03 in Zhejiang (0.00-0.22). If a reliable temporal trend for the spillover hazard could be estimated, for example by implementing widespread routine sampling in sentinel markets, it should be possible to estimate sub-critical values of R0 even more accurately. Should a similar strain emerge with R0>1, these methods could give a real-time indication that sustained transmission is occurring with well-characterised uncertainty.
Pepin KM, Spackman E, Brown JD, et al., 2014, Using quantitative disease dynamics as a tool for guiding response to avian influenza in poultry in the United States of America, PREVENTIVE VETERINARY MEDICINE, Vol: 113, Pages: 376-397, ISSN: 0167-5877
- Author Web Link
- Cite
- Citations: 20
Cauchemez S, Fraser C, Van Kerkhove MD, et al., 2014, Middle East respiratory syndrome coronavirus: quantification of the extent of the epidemic, surveillance biases, and transmissibility, LANCET INFECTIOUS DISEASES, Vol: 14, Pages: 50-56, ISSN: 1473-3099
- Author Web Link
- Open Access Link
- Cite
- Citations: 226
Pepin KM, Lloyd-Smith JO, Webb CT, et al., 2013, Minimizing the threat of pandemic emergence from avian influenza in poultry systems, BMC INFECTIOUS DISEASES, Vol: 13, ISSN: 1471-2334
- Author Web Link
- Cite
- Citations: 13
Riley S, 2013, Complex Disease Dynamics and the Design of Influenza Vaccination Programs, PLOS MEDICINE, Vol: 10, ISSN: 1549-1277
- Author Web Link
- Cite
- Citations: 2
Van Kerkhove MD, Hirve S, Koukounari A, et al., 2013, Estimating age-specific cumulative incidence for the 2009 influenza pandemic: a meta-analysis of A(H1N1)pdm09 serological studies from 19 countries, Influenza and Other Respiratory Viruses, Vol: 7, Pages: 872-886, ISSN: 1750-2640
BACKGROUND: The global impact of the 2009 influenza A(H1N1) pandemic (H1N1pdm) is not well understood. OBJECTIVES: We estimate overall and age-specific prevalence of cross-reactive antibodies to H1N1pdm virus and rates of H1N1pdm infection during the first year of the pandemic using data from published and unpublished H1N1pdm seroepidemiological studies. METHODS: Primary aggregate H1N1pdm serologic data from each study were stratified in standardized age groups and evaluated based on when sera were collected in relation to national or subnational peak H1N1pdm activity. Seropositivity was assessed using well-described and standardized hemagglutination inhibition (HI titers >/=32 or >/=40) and microneutralization (MN >/= 40) laboratory assays. The prevalence of cross-reactive antibodies to the H1N1pdm virus was estimated for studies using sera collected prior to the start of the pandemic (between 2004 and April 2009); H1N1pdm cumulative incidence was estimated for studies in which collected both pre- and post-pandemic sera; and H1N1pdm seropositivity was calculated from studies with post-pandemic sera only (collected between December 2009-June 2010). RESULTS: Data from 27 published/unpublished studies from 19 countries/administrative regions - Australia, Canada, China, Finland, France, Germany, Hong Kong SAR, India, Iran, Italy, Japan, Netherlands, New Zealand, Norway, Reunion Island, Singapore, United Kingdom, United States, and Vietnam - were eligible for inclusion. The overall age-standardized pre-pandemic prevalence of cross-reactive antibodies was 5% (95%CI 3-7%) and varied significantly by age with the highest rates among persons >/=65 years old (14% 95%CI 8-24%). Overall age-standardized H1N1pdm cumulative incidence was 24% (95%CI 20-27%) and varied significantly by age with the highest in children 5-19 (47% 95%CI 39-55%) and 0-4 years old (36% 95%CI 30-43%). CONCLUSIONS: Our results offer unique insight into the global impact of the H1N1 pandemic a
Strelioff CC, Vijaykrishna D, Riley S, et al., 2013, Inferring patterns of influenza transmission in swine from multiple streams of surveillance data, PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 280, ISSN: 0962-8452
- Author Web Link
- Cite
- Citations: 11
Cauchemez S, Van Kerkhove MD, Riley S, et al., 2013, Transmission scenarios for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and how to tell them apart, EUROSURVEILLANCE, Vol: 18, Pages: 7-13, ISSN: 1560-7917
- Author Web Link
- Cite
- Citations: 8
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.