Publications
433 results found
Merler S, Ajelli M, Pugliese A, et al., 2011, Determinants of the Spatiotemporal Dynamics of the 2009 H1N1 Pandemic in Europe: Implications for Real-Time Modelling, PLOS COMPUTATIONAL BIOLOGY, Vol: 7, ISSN: 1553-734X
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- Citations: 89
White MT, Griffin JT, Churcher TS, et al., 2011, Modelling the impact of vector control interventions on Anopheles gambiae population dynamics, Parasites & Vectors, Vol: 4, ISSN: 1756-3305
BACKGROUND:Intensive anti-malaria campaigns targeting the Anopheles population have demonstrated substantial reductions in adult mosquito density. Understanding the population dynamics of Anopheles mosquitoes throughout their whole lifecycle is important to assess the likely impact of vector control interventions alone and in combination as well as to aid the design of novel interventions.METHODS:An ecological model of Anopheles gambiae sensu lato populations incorporating a rainfall-dependent carrying capacity and density-dependent regulation of mosquito larvae in breeding sites is developed. The model is fitted to adult mosquito catch and rainfall data from 8 villages in the Garki District of Nigeria (the 'Garki Project') using Bayesian Markov Chain Monte Carlo methods and prior estimates of parameters derived from the literature. The model is used to compare the impact of vector control interventions directed against adult mosquito stages--long-lasting insecticide treated nets (LLIN), indoor residual spraying (IRS)-- and directed against aquatic mosquito stages, alone and in combination on adult mosquito density.RESULTS:A model in which density-dependent regulation occurs in the larval stages via a linear association between larval density and larval death rates provided a good fit to seasonal adult mosquito catches. The effective mosquito reproduction number in the presence of density-dependent regulation is dependent on seasonal rainfall patterns and peaks at the start of the rainy season. In addition to killing adult mosquitoes during the extrinsic incubation period, LLINs and IRS also result in less eggs being oviposited in breeding sites leading to further reductions in adult mosquito density. Combining interventions such as the application of larvicidal or pupacidal agents that target the aquatic stages of the mosquito lifecycle with LLINs or IRS can lead to substantial reductions in adult mosquito density.CONCLUSIONS:Density-dependent regulation of anophel
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
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
Garske T, Yu H, Peng Z, et al., 2011, Travel Patterns in China, PLOS ONE, Vol: 6, ISSN: 1932-6203
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- Citations: 25
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
Ferguson N, 2011, Achieving Synergy in the Industry-Academia Relationship, COMPUTER, Vol: 44, Pages: 90-92, ISSN: 0018-9162
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- Citations: 3
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
Singh BK, Savill NJ, Ferguson NM, et al., 2010, Rapid detection of pandemic influenza in the presence of seasonal influenza, BMC PUBLIC HEALTH, Vol: 10
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- Citations: 12
Griffin JT, Hollingsworth TD, Okell LC, et al., 2010, Reducing Plasmodium falciparum malaria transmission in Africa: a model-based evaluation of intervention strategies., PLoS Med, Vol: 7, Pages: 1-27, ISSN: 1549-1676
BACKGROUND: Over the past decade malaria intervention coverage has been scaled up across Africa. However, it remains unclear what overall reduction in transmission is achievable using currently available tools. METHODS AND FINDINGS: We developed an individual-based simulation model for Plasmodium falciparum transmission in an African context incorporating the three major vector species (Anopheles gambiae s.s., An. arabiensis, and An. funestus) with parameters obtained by fitting to parasite prevalence data from 34 transmission settings across Africa. We incorporated the effect of the switch to artemisinin-combination therapy (ACT) and increasing coverage of long-lasting insecticide treated nets (LLINs) from the year 2000 onwards. We then explored the impact on transmission of continued roll-out of LLINs, additional rounds of indoor residual spraying (IRS), mass screening and treatment (MSAT), and a future RTS,S/AS01 vaccine in six representative settings with varying transmission intensity (as summarized by the annual entomological inoculation rate, EIR: 1 setting with low, 3 with moderate, and 2 with high EIRs), vector-species combinations, and patterns of seasonality. In all settings we considered a realistic target of 80% coverage of interventions. In the low-transmission setting (EIR approximately 3 ibppy [infectious bites per person per year]), LLINs have the potential to reduce malaria transmission to low levels (<1% parasite prevalence in all age-groups) provided usage levels are high and sustained. In two of the moderate-transmission settings (EIR approximately 43 and 81 ibppy), additional rounds of IRS with DDT coupled with MSAT could drive parasite prevalence below a 1% threshold. However, in the third (EIR = 46) with An. arabiensis prevailing, these interventions are insufficient to reach this threshold. In both high-transmission settings (EIR approximately 586 and 675 ibppy), either unrealistically high coverage levels (>90%) or novel tools and/or
van Kerkhove MD, Asikainen T, Becker NG, et al., 2010, Studies needed to address public health challenges of the 2009 H1N1 influenza pandemic: insights from modeling., PLoS Med, Vol: 7, Pages: e1000275-e1000275
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
Minayev P, Ferguson N, 2009, Incorporating demographic stochasticity into multi-strain epidemic models: application to influenza A, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 6, Pages: 989-996, ISSN: 1742-5689
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- Citations: 25
Pellis L, Ferguson NM, Fraser C, 2009, Threshold parameters for a model of epidemic spread among households and workplaces, Journal of The Royal Society Interface, Vol: 6, Pages: 979-987
The basic reproduction number is one of the most important concepts in modern infectious disease epidemiology. However, for more realistic and more complex models than those assuming homogeneous mixing in the population, other threshold quantities can be defined that are sometimes more useful and easily derived in terms of model parameters. In this paper, we present a model for the spread of a permanently immunizing infection in a population socially structured into households and workplaces/schools, and we propose and discuss a new household-to-household reproduction number for it. We show how overcomes some of the limitations of a previously proposed threshold parameter, and we highlight its relationship with the effort required to control an epidemic when interventions are targeted at randomly selected households.
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.
Ferguson NM, 2009, Mathematical prediction in infection., Medicine (Abingdon), Vol: 37, Pages: 507-509, ISSN: 1357-3039
It is now increasingly common for infectious disease epidemics to be analysed with mathematical models. Modelling is possible because epidemics involve relatively simple processes occurring within large populations of individuals. Modelling aims to explain and predict trends in disease incidence, prevalence, morbidity or mortality. Epidemic models give important insight into the development of an epidemic. Following disease establishment, epidemic growth is approximately exponential. The rate of growth in this phase is primarily determined by the basic reproduction number, R 0, the number of secondary cases per primary case when the population is susceptible. R 0 also determines the ease with which control policies can control an epidemic. Once a significant proportion of the population has been infected, not all contacts of an infected individual will be with susceptible people. Infection can now continue only because new births replenish the susceptible population. Eventually an endemic equilibrium is reached where every infected person infects one other individual on average. Heterogeneity in host susceptibility, infectiousness, human contact patterns and in the genetic composition of pathogen populations introduces substantial additional complexity into this picture, however - and into the models required to model real diseases realistically. This chapter concludes with a brief review of the recent application of mathematical models to a wide range of emerging human or animal epidemics, most notably the spread of HIV in Africa, the 2001 foot and mouth epidemic in British livestock, bioterrorism threats such as smallpox, the SARS epidemics in 2003 and most recently the use of modelling as a tool for influenza pandemic preparedness planning.
Lange A, Ferguson NM, 2009, Antigenic Diversity, Transmission Mechanisms, and the Evolution of Pathogens, PLOS COMPUTATIONAL BIOLOGY, Vol: 5, ISSN: 1553-734X
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- Citations: 39
Dodd PJ, Ferguson NM, 2009, A Many-Body Field Theory Approach to Stochastic Models in Population Biology, PLOS ONE, Vol: 4, ISSN: 1932-6203
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- Citations: 7
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
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
Minayev P, Ferguson N, 2009, Improving the realism of deterministic multi-strain models: implications for modelling influenza A, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 6, Pages: 509-518, ISSN: 1742-5689
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- Citations: 48
Truscott JE, Ferguson NM, 2009, Control of scrapie in the UK sheep population, EPIDEMIOLOGY AND INFECTION, Vol: 137, Pages: 775-786, ISSN: 0950-2688
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- Citations: 15
Truscott JE, Ferguson NM, 2009, Transmission dynamics and mechanisms of endemicity of scrapie in the UK sheep population, EPIDEMIOLOGY AND INFECTION, Vol: 137, Pages: 762-774, ISSN: 0950-2688
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- Citations: 5
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
Ster IC, Singh BK, Ferguson NM, 2009, Epidemiological inference for partially observed epidemics: The example of the 2001 foot and mouth epidemic in Great Britain, EPIDEMICS, Vol: 1, Pages: 21-34, ISSN: 1755-4365
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- Citations: 50
Ghani AC, Sutherland CJ, Riley EM, et al., 2009, Loss of Population Levels of Immunity to Malaria as a Result of Exposure-Reducing Interventions: Consequences for Interpretation of Disease Trends, PLOS ONE, Vol: 4, ISSN: 1932-6203
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- Citations: 120
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