Publications
433 results found
Ali ST, Kadi AS, Ferguson NM, 2013, Transmission dynamics of the 2009 influenza A (H1N1) pandemic in India: The impact of holiday-related school closure, EPIDEMICS, Vol: 5, Pages: 157-163, ISSN: 1755-4365
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- Citations: 29
Abdallat MM, Abroug F, Al Dhahry SHS, et al., 2013, State of knowledge and data gaps of middle east respiratory syndrome coronavirus (MERS-CoV) in humans, PLoS Currents, Vol: 5
BACKGROUND: Between September 2012 and 22 October 2013, 144 laboratory-confirmed and 17 probable MERS-CoV cases from nine countries were notified to WHO. METHODS: We summarize what is known about the epidemiology, virology, phylogeny and emergence of MERS-CoV to inform public health policies. RESULTS: The median age of patients (n=161) was 50 years (range 14 months to 94 years), 64.5% were male and 63.4% experienced severe respiratory disease. 76.0% of patients were reported to have ≥1 underlying medical condition and fatal cases, compared to recovered or asymptomatic cases were more likely to have an underlying condition (86.8% vs. 42.4%, p<0.001). Analysis of genetic sequence data suggests multiple independent introductions into human populations and modelled estimates using epidemiologic and genetic data suggest R0 is <1, though the upper range of estimates may exceed 1. Index/sporadic cases (cases with no epidemiologic-link to other cases) were more likely to be older (median 59.0 years vs. 43.0 years, p<0.001) compared to secondary cases, although these proportions have declined over time. 80.9% vs. 67.2% of index/sporadic and secondary cases, respectively, reported ≥1 underlying condition. Clinical presentation ranges from asymptomatic to severe pneumonia with acute respiratory distress syndrome and multi-organ failure. Nearly all symptomatic patients presented with respiratory symptoms and 1/3 of patients also had gastrointestinal symptoms. CONCLUSIONS: Sustained human-to-human transmission of MERS-CoV has not been observed. Outbreaks have been extinguished without overly aggressive isolation and quarantine suggesting that transmission of virus may be stopped with implementation of appropriate infection control measures.
Cori A, Ferguson NM, Fraser C, et al., 2013, A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics, AMERICAN JOURNAL OF EPIDEMIOLOGY, Vol: 178, Pages: 1505-1512, ISSN: 0002-9262
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- Citations: 498
Storms AD, Van Kerkhove MD, Azziz-Baumgartner E, et al., 2013, Worldwide transmission and seasonal variation of pandemic influenza A(H1N1)2009 virus activity during the 2009-2010 pandemic, INFLUENZA AND OTHER RESPIRATORY VIRUSES, Vol: 7, Pages: 1328-1335, ISSN: 1750-2640
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- Citations: 9
Aguas R, Ferguson NM, 2013, Feature Selection Methods for Identifying Genetic Determinants of Host Species in RNA Viruses, PLOS COMPUTATIONAL BIOLOGY, Vol: 9
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- Citations: 15
Ampofo WK, Baylor N, Cobey S, et al., 2013, Improving influenza vaccine virus selection: report of a WHO informal consultation held at WHO headquarters, Geneva, Switzerland, 14-16 June 2010, INFLUENZA AND OTHER RESPIRATORY VIRUSES, Vol: 7, Pages: 52-53, ISSN: 1750-2640
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- Citations: 5
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
Juergens MC, Voeroes J, Rautureau GJP, et al., 2013, The hepatitis B virus preS1 domain hijacks host trafficking proteins by motif mimicry, NATURE CHEMICAL BIOLOGY, Vol: 9, Pages: 540-U32, ISSN: 1552-4450
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- Citations: 24
Dorigatti I, Cauchemez S, Ferguson NM, 2013, Increased transmissibility explains the third wave of infection by the 2009 H1N1 pandemic virus in England, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 110, Pages: 13422-13427, ISSN: 0027-8424
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- Citations: 54
Gambhir M, Swerdlow DL, Finelli L, et al., 2013, Multiple Contributory Factors to the Age Distribution of Disease Cases: A Modeling Study in the Context of Influenza A(H3N2v), CLINICAL INFECTIOUS DISEASES, Vol: 57, Pages: S23-S27, ISSN: 1058-4838
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- Citations: 10
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
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- Citations: 8
Cauchemez S, Epperson S, Biggerstaff M, et al., 2013, Using Routine Surveillance Data to Estimate the Epidemic Potential of Emerging Zoonoses: Application to the Emergence of US Swine Origin Influenza A H3N2v Virus, PLOS MEDICINE, Vol: 10, ISSN: 1549-1277
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- Citations: 42
Garske T, Ferguson NM, Ghani AC, 2013, Estimating Air Temperature and Its Influence on Malaria Transmission across Africa, PLOS ONE, Vol: 8, ISSN: 1932-6203
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- Citations: 43
Cauchemez S, Horby P, Fox A, et al., 2012, Influenza Infection Rates, Measurement Errors and the Interpretation of Paired Serology, PLOS PATHOGENS, Vol: 8, ISSN: 1553-7366
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- Citations: 72
Killingley B, Hayward A, Enstone JE, et al., 2012, Pre-existing immunity in human challenge studies of influenza transmission Reply, LANCET INFECTIOUS DISEASES, Vol: 12, Pages: 744-745, ISSN: 1473-3099
Truscott J, Ferguson NM, 2012, Evaluating the Adequacy of Gravity Models as a Description of Human Mobility for Epidemic Modelling, PLOS COMPUTATIONAL BIOLOGY, Vol: 8, ISSN: 1553-734X
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- Citations: 61
de Silva E, Ferguson NM, Fraser C, 2012, Inferring pandemic growth rates from sequence data, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 9, Pages: 1797-1808, ISSN: 1742-5689
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- Citations: 32
Ster IC, Dodd PJ, Ferguson NM, 2012, Within-farm transmission dynamics of foot and mouth disease as revealed by the 2001 epidemic in Great Britain, EPIDEMICS, Vol: 4, Pages: 158-169, ISSN: 1755-4365
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- Citations: 35
Van Kerkhove MD, Riley S, Lipsitch M, et al., 2012, Comment on "Seroevidence for H5N1 Influenza Infections in Humans: Meta-Analysis", SCIENCE, Vol: 336, ISSN: 0036-8075
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- Citations: 28
Yu H, Cauchemez S, Donnelly CA, et al., 2012, Transmission Dynamics, Border Entry Screening, and School Holidays during the 2009 Influenza A (H1N1) Pandemic, China, EMERGING INFECTIOUS DISEASES, Vol: 18, Pages: 758-766, ISSN: 1080-6040
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- Citations: 41
Van Kerkhove MD, Ferguson NM, 2012, Epidemic and intervention modelling – a scientific rationale for policy decisions? Lessons from the 2009 influenza pandemic, Bulletin of the World Health Organization, Vol: 90
ProblemOutbreak analysis and mathematical modelling are crucial for planning public health responses to infectious disease outbreaks, epidemics and pandemics. This paper describes the data analysis and mathematical modelling undertaken during and following the 2009 influenza pandemic, especially to inform public health planning and decision-making.ApproachSoon after A(H1N1)pdm09 emerged in North America in 2009, the World Health Organization convened an informal mathematical modelling network of public health and academic experts and modelling groups. This network and other modelling groups worked with policy-makers to characterize the dynamics and impact of the pandemic and assess the effectiveness of interventions in different settings.SettingThe 2009 A(H1N1) influenza pandemic.Relevant changesModellers provided a quantitative framework for analysing surveillance data and for understanding the dynamics of the epidemic and the impact of interventions. However, what most often informed policy decisions on a day-to-day basis was arguably not sophisticated simulation modelling, but rather, real-time statistical analyses based on mechanistic transmission models relying on available epidemiologic and virologic data.Lessons learntA key lesson was that modelling cannot substitute for data; it can only make use of available data and highlight what additional data might best inform policy. Data gaps in 2009, especially from low-resource countries, made it difficult to evaluate severity, the effects of seasonal variation on transmission and the effectiveness of non-pharmaceutical interventions. Better communication between modellers and public health practitioners is needed to manage expectations, facilitate data sharing and interpretation and reduce inconsistency in results.
Cauchemez S, Ferguson NM, 2012, Methods to infer transmission risk factors in complex outbreak data, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 9, Pages: 456-469, ISSN: 1742-5689
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- Citations: 24
Cauchemez S, Boelle PY, Donnelly CA, et al., 2012, Real-time estimates in early detection of SARS, Emerging Infectious Diseases, Vol: 12, Pages: 110-113, ISSN: 1080-6040
We propose a Bayesian statistical framework for estimating the reproduction number R early in an epidemic. This method allows for the yet-unrecorded secondary cases if the estimate is obtained before the epidemic has ended. We applied our approach to the severe acute respiratory syndrome (SARS) epidemic that started in February 2003 in Hong Kong. Temporal patterns of R estimated after 5, 10, and 20 days were similar. Ninety-five percent credible intervals narrowed when more data were available but stabilized after 10 days. Using simulation studies of SARS-like outbreaks, we have shown that the method may be used for early monitoring of the effect of control measures.
Truscott J, Fraser C, Cauchemez S, et al., 2012, Essential epidemiological mechanisms underpinning the transmission dynamics of seasonal influenza, Journal of the Royal Society Interface, Vol: 9, Pages: 304-312, ISSN: 1742-5662
Seasonal influenza has considerable impact around the world, both economically and in mortality among risk groups, but there is considerable uncertainty as to the essential mechanisms and their parametrization. In this paper, we identify a number of characteristic features of influenza incidence time series in temperate regions, including ranges of annual attack rates and outbreak durations. By constraining the output of simple models to match these characteristic features, we investigate the role played by population heterogeneity, multiple strains, cross-immunity and the rate of strain evolution in the generation of incidence time series. Results indicate that an age-structured model with non-random mixing and co-circulating strains are both required to match observed time-series data. Our work gives estimates of the seasonal peak basic reproduction number, R0, in the range 1.6–3. Estimates of R0 are strongly correlated with the timescale for waning of immunity to current circulating seasonal influenza strain, which we estimate is between 3 and 8 years. Seasonal variation in transmissibility is largely confined to 15–30% of its mean value. While population heterogeneity and cross-immunity are required mechanisms, the degree of heterogeneity and cross-immunity is not tightly constrained. We discuss our findings in the context of other work fitting to seasonal influenza data.
Killingley B, Enstone JE, Greatorex J, et al., 2012, Use of a Human Influenza Challenge Model to Assess Person-to-Person Transmission: Proof-of-Concept Study, JOURNAL OF INFECTIOUS DISEASES, Vol: 205, Pages: 35-43, ISSN: 0022-1899
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- Citations: 47
Dorigatti I, Cauchemez S, Pugliese A, et al., 2011, A new approach to characterising infectious disease transmission dynamics from sentinel surveillance: application to the Italian 2009–2010 A/H1N1 influenza pandemic, Epidemics, Vol: 4, Pages: 9-21, ISSN: 1878-0067
Syndromic and virological data are routinely collected by many countries and are often the only information available in real time. The analysis of surveillance data poses many statistical challenges that have not yet been addressed. For instance, the fraction of cases that seek healthcare and are thus detected is often unknown. Here, we propose a general statistical framework that explicitly takes into account the way the surveillance data are generated. Our approach couples a deterministic mathematical model with a statistical description of the reporting process and is applied to surveillance data collected in Italy during the 2009–2010 A/H1N1 influenza pandemic. We estimate that the reproduction number R was initially into the range 1.2–1.4 and that case detection in children was significantly higher than in adults. According to the best fit models, we estimate that school-age children experienced the highest infection rate overall. In terms of both estimated peak-incidence and overall attack rate, according to the Susceptibility and Immunity models the 5–14 years age-class was about 5 times more infected than the 65+ years old age-group and about twice more than the 15–64 years age-class. The multiplying factors are doubled using the Baseline model. Overall, the estimated attack rate was about 16% according to the Baseline model and 30% according to the Susceptibility and Immunity models.
Killingley B, Enstone J, Booy R, et al., 2011, Potential role of human challenge studies for investigation of influenza transmission, LANCET INFECTIOUS DISEASES, Vol: 11, Pages: 879-886, ISSN: 1473-3099
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- Citations: 49
Pellis L, Ferguson NM, Fraser C, 2011, Epidemic growth rate and household reproduction number in communities of households, schools and workplaces, JOURNAL OF MATHEMATICAL BIOLOGY, Vol: 63, Pages: 691-734, ISSN: 0303-6812
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- Citations: 16
Fraser C, Cummings DAT, Klinkenberg D, et al., 2011, Influenza Transmission in Households During the 1918 Pandemic, AMERICAN JOURNAL OF EPIDEMIOLOGY, Vol: 174, Pages: 505-514, ISSN: 0002-9262
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- Citations: 55
Opatowski L, Fraser C, Griffin J, et al., 2011, Transmission Characteristics of the 2009 H1N1 Influenza Pandemic: Comparison of 8 Southern Hemisphere Countries, PLOS PATHOGENS, Vol: 7, ISSN: 1553-7366
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- Citations: 55
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