Publications
68 results found
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.
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
MacIntyre CR, Wang Q, Cauchemez S, et al., 2011, A cluster randomized clinical trial comparing fit-tested and non-fit-tested N95 respirators to medical masks to prevent respiratory virus infection in health care workers, INFLUENZA AND OTHER RESPIRATORY VIRUSES, Vol: 5, Pages: 170-179, ISSN: 1750-2640
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- Citations: 177
Lapidus N, de Lamballerie X, Salez N, et al., 2011, Association between pandemic and seasonal influenza vaccination and haemagglutination antibody titers against A/H1N1v: a national representative survey in France, nested in the "Cohorts for Pandemic Influenza'' (CoPanFlu - France), Conference on Options for the Control of Influenza VII, Publisher: WILEY, Pages: 180-183, ISSN: 1750-2640
Cauchemez S, Bhattarai A, Marchbanks TL, et al., 2011, Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 108, Pages: 2825-2830, ISSN: 0027-8424
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- Citations: 244
Eggo RM, Cauchemez S, Ferguson NM, 2011, Spatial dynamics of the 1918 influenza pandemic in England, Wales and the United States, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 8, Pages: 233-243, ISSN: 1742-5689
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- Citations: 68
Donnelly CA, Finelli L, Cauchemez S, et al., 2011, Serial Intervals and the Temporal Distribution of Secondary Infections within Households of 2009 Pandemic Influenza A (H1N1): Implications for Influenza Control Recommendations, CLINICAL INFECTIOUS DISEASES, Vol: 52, Pages: S123-S130, ISSN: 1058-4838
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- Citations: 47
Marchbanks TL, Bhattarai A, Fagan RP, et al., 2011, An Outbreak of 2009 Pandemic Influenza A (H1N1) Virus Infection in an Elementary School in Pennsylvania, CLINICAL INFECTIOUS DISEASES, Vol: 52, Pages: S154-S160, ISSN: 1058-4838
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- Citations: 22
Egan JR, Legrand J, Hall IM, et al., 2010, Re-assessment of mitigation strategies for deliberate releases of anthrax using a real-time outbreak characterization tool, EPIDEMICS, Vol: 2, Pages: 189-194, ISSN: 1755-4365
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- Citations: 7
Killingley B, Greatorex J, Cauchemez S, et al., 2010, Virus shedding and environmental deposition of novel A (H1N1) pandemic influenza virus: interim findings, HEALTH TECHNOLOGY ASSESSMENT, Vol: 14, Pages: 237-+, ISSN: 1366-5278
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- Citations: 30
Cremin I, Nyamukapa C, Sherr L, et al., 2010, Patterns of Self-reported Behaviour Change Associated with Receiving Voluntary Counselling and Testing in a Longitudinal Study from Manicaland, Zimbabwe, AIDS AND BEHAVIOR, Vol: 14, Pages: 708-715, ISSN: 1090-7165
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- Citations: 44
Walker PGT, Cauchemez S, Metras R, et al., 2010, A Bayesian Approach to Quantifying the Effects of Mass Poultry Vaccination upon the Spatial and Temporal Dynamics of H5N1 in Northern Vietnam, PLOS COMPUTATIONAL BIOLOGY, Vol: 6, ISSN: 1553-734X
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- Citations: 26
Cauchemez S, Donnelly CA, Reed C, et al., 2009, Household Transmission of 2009 Pandemic Influenza A (H1N1) Virus in the United States, NEW ENGLAND JOURNAL OF MEDICINE, Vol: 361, Pages: 2619-2627, ISSN: 0028-4793
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- Citations: 365
Truscott J, Fraser C, Hinsley W, et al., 2009, Quantifying the transmissibility of human influenza and its seasonal variation in temperate regions., PLoS Curr, Vol: 1
Seasonal influenza has considerable impact around the world, both economically and in mortality among risk groups. The long term patterns of disease are hard to capture with simple models, while the interplay of epidemiological processes with antigenic evolution makes detailed modelling difficult and computationally intensive. We identify a number of characteristic features of flu incidence time series in temperate regions, including ranges of annual attack rates and outbreak durations. We construct pseudo-likelihoods to capture these characteristic features and examine the ability of a collection of simple models to reproduce them under seasonal variation in transmission. Results indicate that an age-structured model with non-random mixing and co-circulating strains are both required to match time series data. The extent of matching behaviour also serves to define informative ranges for parameters governing essential dynamics. Our work gives estimates of the seasonal peak basic reproduction, R0, in the range 1.7-2.1, with the degree of seasonal variation having limited impact of these estimates. We find that it is only really possible to estimate a lower bound on the degree of seasonal variation in influenza transmissibility, namely that transmissibility in the low transmission season may be only 5-10% less than the peak value. These results give some insight into the extent to which transmissibility of the H1N1pdm pandemic virus may increase in Northern Hemisphere temperate countries in winter 2009. We find that the timescale for waning of immunity to current circulating seasonal influenza strain is between 4 and 8 years, consistent with studies of the antigenic variation of influenza, and that inter-subtype cross-immunity is restricted to low levels.
Fraser C, Donnelly CA, Cauchemez S, et al., 2009, Influenza: Making Privileged Data Public Response, SCIENCE, Vol: 325, Pages: 1072-1073, ISSN: 0036-8075
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- Citations: 2
Evans D, Cauchemez S, Hayden FG, 2009, "Prepandemic" Immunization for Novel Influenza Viruses, "Swine Flu" Vaccine, Guillain-Barre Syndrome, and the Detection of Rare Severe Adverse Events, JOURNAL OF INFECTIOUS DISEASES, Vol: 200, Pages: 321-328, ISSN: 0022-1899
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- Citations: 64
Cauchemez S, Ferguson NM, Watchel C, et al., 2009, Closure of schools during an influenza pandemic, Lancet Infectious Disease, Vol: 9, Pages: 473-481
Garske T, Legrand J, Donnelly CA, et al., 2009, Assessing the severity of the novel influenza A/H1N1 pandemic, BRITISH MEDICAL JOURNAL, Vol: 339, ISSN: 0959-8146
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- Citations: 164
Lipsitch M, Riley S, Cauchemez S, et al., 2009, Managing and reducing uncertainty in an emerging influenza pandemic., N Engl J Med, Vol: 361, Pages: 112-115
Fraser C, Donnelly CA, Cauchemez S, et al., 2009, Pandemic Potential of a Strain of Influenza A (H1N1): Early Findings, SCIENCE, Vol: 324, Pages: 1557-1561, ISSN: 0036-8075
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- Citations: 1393
Legrand J, Egan JR, Hall IM, et al., 2009, Estimating the Location and Spatial Extent of a Covert Anthrax Release, PLOS COMPUTATIONAL BIOLOGY, Vol: 5
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- Citations: 19
Maclntyre CR, Cauchemez S, Dwyer DE, et al., 2009, Face Mask Use and Control of Respiratory Virus Transmission in Households, EMERGING INFECTIOUS DISEASES, Vol: 15, Pages: 233-241, ISSN: 1080-6040
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- Citations: 296
Ghani A, Baguelin M, Griffin J, et al., 2009, The early transmission dynamics of H1N1pdm influenza in the United Kingdom, PLoS currents, Vol: 1
Cauchemez S, Valleron AJ, Boelle PY, et al., 2008, Estimating the impact of school closure on influenza transmission from sentinel data, Nature, Vol: 452, Pages: 750-754
Carrat F, Vergu E, Ferguson NM, et al., 2008, Time Lines of Infection and Disease in Human Influenza: A Review of Volunteer Challenge Studies, American Journal of Epidemiology
Cauchemez S, Ferguson NM, 2007, Likelihood-based estimation of continuous-time epidemic models from time-series data: Application to measles transmission in London, Journal of the Royal Society Interface
Cauchemez S, Boelle P-Y, Thomas G, et al., 2006, Estimating in real time the efficacy of measures to control emerging communicable diseases, AMERICAN JOURNAL OF EPIDEMIOLOGY, Vol: 164, Pages: 591-597, ISSN: 0002-9262
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- Citations: 89
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