Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Nicholas G. Davies"'
Autor:
Rosanna C. Barnard, Nicholas G. Davies, Centre for Mathematical Modelling of Infectious Diseases COVID-19 working group, Mark Jit, W. John Edmunds
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-15 (2022)
This mathematical modelling study projects the dynamics of SARS-CoV-2 in England until the end of 2022 assuming that the Omicron BA.2 sublineage remains dominant. They show that booster vaccination was highly effective in mitigating severe outcomes a
Externí odkaz:
https://doaj.org/article/79412c1516274626bdbcb72ccfb6ea63
Autor:
Alicia Rosello, Rosanna C. Barnard, David R. M. Smith, Stephanie Evans, Fiona Grimm, Nicholas G. Davies, Centre for Mathematical Modelling of Infectious Diseases COVID-19 Modelling Working Group, Sarah R. Deeny, Gwenan M. Knight, W. John Edmunds
Publikováno v:
BMC Infectious Diseases, Vol 22, Iss 1, Pp 1-13 (2022)
Abstract Background COVID-19 outbreaks still occur in English care homes despite the interventions in place. Methods We developed a stochastic compartmental model to simulate the spread of SARS-CoV-2 within an English care home. We quantified the out
Externí odkaz:
https://doaj.org/article/b3e40a3916e84cb2af7e6ad4855513ca
Autor:
The OpenSAFELY Collaborative, Elizabeth J. Williamson, John Tazare, Krishnan Bhaskaran, Helen I. McDonald, Alex J. Walker, Laurie Tomlinson, Kevin Wing, Sebastian Bacon, Chris Bates, Helen J. Curtis, Harriet J. Forbes, Caroline Minassian, Caroline E. Morton, Emily Nightingale, Amir Mehrkar, David Evans, Brian D. Nicholson, David A. Leon, Peter Inglesby, Brian MacKenna, Nicholas G. Davies, Nicholas J. DeVito, Henry Drysdale, Jonathan Cockburn, William J. Hulme, Jessica Morley, Ian Douglas, Christopher T. Rentsch, Rohini Mathur, Angel Wong, Anna Schultze, Richard Croker, John Parry, Frank Hester, Sam Harper, Richard Grieve, David A. Harrison, Ewout W. Steyerberg, Rosalind M. Eggo, Karla Diaz-Ordaz, Ruth Keogh, Stephen J. W. Evans, Liam Smeeth, Ben Goldacre
Publikováno v:
Diagnostic and Prognostic Research, Vol 6, Iss 1, Pp 1-15 (2022)
Abstract Background Obtaining accurate estimates of the risk of COVID-19-related death in the general population is challenging in the context of changing levels of circulating infection. Methods We propose a modelling approach to predict 28-day COVI
Externí odkaz:
https://doaj.org/article/90a8702465db42f89b6f83fd9b25c26d
Autor:
Kevin van Zandvoort, Christopher I. Jarvis, Carl A. B. Pearson, Nicholas G. Davies, CMMID COVID-19 working group, Ruwan Ratnayake, Timothy W. Russell, Adam J. Kucharski, Mark Jit, Stefan Flasche, Rosalind M. Eggo, Francesco Checchi
Publikováno v:
BMC Medicine, Vol 18, Iss 1, Pp 1-19 (2020)
Abstract Background The health impact of COVID-19 may differ in African settings as compared to countries in Europe or China due to demographic, epidemiological, environmental and socio-economic factors. We evaluated strategies to reduce SARS-CoV-2 b
Externí odkaz:
https://doaj.org/article/f7cc02bd34c046c6a69c81f106b8cc23
Publikováno v:
Emerging Infectious Diseases, Vol 26, Iss 1, Pp 138-142 (2020)
Vaccines against viral infections have been proposed to reduce prescribing of antibiotics and thereby help control resistant bacterial infections. However, by combining published data sources, we predict that pediatric live attenuated influenza vacci
Externí odkaz:
https://doaj.org/article/efbc935eacb548f48891c38b63156ae6
Autor:
Abdihamid Warsame, Mihaly Koltai, Terri Freemantle, Farah Bashiir, Chris Williams, Nicholas G. Davies, Chris Reeve, Mark Jit, Stefan Flasche, Mohamed Ahmed, Ahmed Aweis, Francesco Checchi, Abdirisak Dalmar
Publikováno v:
Wellcome Open Research, Vol 6 (2022)
Background: In countries with weak surveillance systems, confirmed coronavirus disease 2019 (COVID-19) deaths are likely to underestimate the pandemic’s death toll. Many countries also have incomplete vital registration systems, hampering excess mo
Externí odkaz:
https://doaj.org/article/d7b206aff5fa438ab000dfc95e065016
Autor:
Gwenan M. Knight, Nicholas G. Davies, Caroline Colijn, Francesc Coll, Tjibbe Donker, Danna R. Gifford, Rebecca E. Glover, Mark Jit, Elizabeth Klemm, Sonja Lehtinen, Jodi A. Lindsay, Marc Lipsitch, Martin J. Llewelyn, Ana L. P. Mateus, Julie V. Robotham, Mike Sharland, Dov Stekel, Laith Yakob, Katherine E. Atkins
Publikováno v:
BMC Infectious Diseases, Vol 19, Iss 1, Pp 1-9 (2019)
Abstract Background Antibiotics remain the cornerstone of modern medicine. Yet there exists an inherent dilemma in their use: we are able to prevent harm by administering antibiotic treatment as necessary to both humans and animals, but we must be mi
Externí odkaz:
https://doaj.org/article/d28eeb4aec5a465e8f6ad8ee4343de81
Publikováno v:
Evolution Letters, Vol 3, Iss 2, Pp 173-184 (2019)
Abstract Haplodiploidy has evolved repeatedly among invertebrates, and appears to be associated with inbreeding. Evolutionary biologists have long debated the possible benefits for females in diplodiploid species to produce haploid sons–beginning t
Externí odkaz:
https://doaj.org/article/c1a2aaeec1c642d5b435784721419b76
Autor:
Trystan Leng, Connor White, Joe Hilton, Adam Kucharski, Lorenzo Pellis, Helena Stage, Nicholas G. Davies, Centre for Mathematical Modelling of Infectious Disease 2019 nCoV Working Group, Matt J. Keeling, Stefan Flasche
Publikováno v:
Wellcome Open Research, Vol 5 (2020)
Background: During the coronavirus disease 2019 (COVID-19) lockdown, contact clustering in social bubbles may allow extending contacts beyond the household at minimal additional risk and hence has been considered as part of modified lockdown poli
Externí odkaz:
https://doaj.org/article/0026b5b5719b462facd69d6ea5009778
Autor:
Nicholas G. Davies, Andy Gardner
Publikováno v:
Royal Society Open Science, Vol 5, Iss 5 (2018)
Monogamy is associated with sibling-directed altruism in multiple animal taxa, including insects, birds and mammals. Inclusive-fitness theory readily explains this pattern by identifying high relatedness as a promoter of altruism. In keeping with thi
Externí odkaz:
https://doaj.org/article/be77de7a31234d33943f2b232d489201