27 results on '"Mrinank Sharma"'
Search Results
2. Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe
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Mrinank Sharma, Sören Mindermann, Charlie Rogers-Smith, Gavin Leech, Benedict Snodin, Janvi Ahuja, Jonas B. Sandbrink, Joshua Teperowski Monrad, George Altman, Gurpreet Dhaliwal, Lukas Finnveden, Alexander John Norman, Sebastian B. Oehm, Julia Fabienne Sandkühler, Laurence Aitchison, Tomáš Gavenčiak, Thomas Mellan, Jan Kulveit, Leonid Chindelevitch, Seth Flaxman, Yarin Gal, Swapnil Mishra, Samir Bhatt, and Jan Markus Brauner
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Science - Abstract
European governments control resurging waves of COVID-19 using nonpharmaceutical interventions. Here, the authors estimate the effectiveness of 17 interventions in Europe’s second wave, and analyse differences to the first wave as well as implications for the future of the pandemic.
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- 2021
- Full Text
- View/download PDF
3. Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions.
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Tomáš Gavenčiak, Joshua Teperowski Monrad, Gavin Leech, Mrinank Sharma, Sören Mindermann, Samir Bhatt, Jan Brauner, and Jan Kulveit
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Biology (General) ,QH301-705.5 - Abstract
Although seasonal variation has a known influence on the transmission of several respiratory viral infections, its role in SARS-CoV-2 transmission remains unclear. While there is a sizable and growing literature on environmental drivers of COVID-19 transmission, recent reviews have highlighted conflicting and inconclusive findings. This indeterminacy partly owes to the fact that seasonal variation relates to viral transmission by a complicated web of causal pathways, including many interacting biological and behavioural factors. Since analyses of specific factors cannot determine the aggregate strength of seasonal forcing, we sidestep the challenge of disentangling various possible causal paths in favor of a holistic approach. We model seasonality as a sinusoidal variation in transmission and infer a single Bayesian estimate of the overall seasonal effect. By extending two state-of-the-art models of non-pharmaceutical intervention (NPI) effects and their datasets covering 143 regions in temperate Europe, we are able to adjust our estimates for the role of both NPIs and mobility patterns in reducing transmission. We find strong seasonal patterns, consistent with a reduction in the time-varying reproduction number R(t) (the expected number of new infections generated by an infectious individual at time t) of 42.1% (95% CI: 24.7%-53.4%) from the peak of winter to the peak of summer. These results imply that the seasonality of SARS-CoV-2 transmission is comparable in magnitude to the most effective individual NPIs but less than the combined effect of multiple interventions.
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- 2022
- Full Text
- View/download PDF
4. Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England
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Swapnil Mishra, Sören Mindermann, Mrinank Sharma, Charles Whittaker, Thomas A Mellan, Thomas Wilton, Dimitra Klapsa, Ryan Mate, Martin Fritzsche, Maria Zambon, Janvi Ahuja, Adam Howes, Xenia Miscouridou, Guy P Nason, Oliver Ratmann, Elizaveta Semenova, Gavin Leech, Julia Fabienne Sandkühler, Charlie Rogers-Smith, Michaela Vollmer, H Juliette T Unwin, Yarin Gal, Meera Chand, Axel Gandy, Javier Martin, Erik Volz, Neil M Ferguson, Samir Bhatt, Jan M Brauner, and Seth Flaxman
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SARS-CoV-2 ,Variants of concern ,Epidemiology ,Waste water monitoring ,Genomic surveillance ,Public health ,Medicine (General) ,R5-920 - Abstract
Background: Since its emergence in Autumn 2020, the SARS-CoV-2 Variant of Concern (VOC) B.1.1.7 (WHO label Alpha) rapidly became the dominant lineage across much of Europe. Simultaneously, several other VOCs were identified globally. Unlike B.1.1.7, some of these VOCs possess mutations thought to confer partial immune escape. Understanding when and how these additional VOCs pose a threat in settings where B.1.1.7 is currently dominant is vital. Methods: We examine trends in the prevalence of non-B.1.1.7 lineages in London and other English regions using passive-case detection PCR data, cross-sectional community infection surveys, genomic surveillance, and wastewater monitoring. The study period spans from 31st January 2021 to 15th May 2021. Findings: Across data sources, the percentage of non-B.1.1.7 variants has been increasing since late March 2021. This increase was initially driven by a variety of lineages with immune escape. From mid-April, B.1.617.2 (WHO label Delta) spread rapidly, becoming the dominant variant in England by late May. Interpretation: The outcome of competition between variants depends on a wide range of factors such as intrinsic transmissibility, evasion of prior immunity, demographic specificities and interactions with non-pharmaceutical interventions. The presence and rise of non-B.1.1.7 variants in March likely was driven by importations and some community transmission. There was competition between non-B.1.17 variants which resulted in B.1.617.2 becoming dominant in April and May with considerable community transmission. Our results underscore that early detection of new variants requires a diverse array of data sources in community surveillance. Continued real-time information on the highly dynamic composition and trajectory of different SARS-CoV-2 lineages is essential to future control efforts Funding: National Institute for Health Research, Medicines and Healthcare products Regulatory Agency, DeepMind, EPSRC, EA Funds programme, Open Philanthropy, Academy of Medical Sciences Bill,Melinda Gates Foundation, Imperial College Healthcare NHS Trust, The Novo Nordisk Foundation, MRC Centre for Global Infectious Disease Analysis, Community Jameel, Cancer Research UK, Imperial College COVID-19 Research Fund, Medical Research Council, Wellcome Sanger Institute.
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- 2021
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5. Is the cure really worse than the disease? The health impacts of lockdowns during COVID-19
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Gavin Yamey, Seth Flaxman, Oliver Ratmann, Samir Bhatt, Swapnil Mishra, Gideon Meyerowitz-Katz, Jan Markus Brauner, Mrinank Sharma, Sören Mindermann, Valerie Bradley, Michaela Vollmer, and Lea Merone
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Medicine (General) ,R5-920 ,Infectious and parasitic diseases ,RC109-216 - Published
- 2021
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6. Towards Understanding Sycophancy in Language Models.
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Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud, Amanda Askell, Samuel R. Bowman, Esin Durmus, Zac Hatfield-Dodds, Scott R. Johnston, Shauna Kravec, Timothy Maxwell, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, and Ethan Perez
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- 2024
7. When Do Universal Image Jailbreaks Transfer Between Vision-Language Models?
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Rylan Schaeffer, Dan Valentine, Luke Bailey, James Chua, Cristóbal Eyzaguirre, Zane Durante, Joe Benton, Brando Miranda, Henry Sleight, John Hughes, Rajashree Agrawal, Mrinank Sharma, Scott Emmons, Sanmi Koyejo, and Ethan Perez
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- 2024
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8. Incorporating Unlabelled Data into Bayesian Neural Networks.
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Mrinank Sharma, Tom Rainforth, Yee Whye Teh, and Vincent Fortuin
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- 2024
9. Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training.
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Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M. Ziegler, Tim Maxwell, Newton Cheng, Adam S. Jermyn, Amanda Askell, Ansh Radhakrishnan, Cem Anil, David Duvenaud, Deep Ganguli, Fazl Barez, Jack Clark, Kamal Ndousse, Kshitij Sachan, Michael Sellitto, Mrinank Sharma, Nova DasSarma, Roger Grosse, Shauna Kravec, Yuntao Bai, Zachary Witten, Marina Favaro, Jan Brauner, Holden Karnofsky, Paul F. Christiano, Samuel R. Bowman, Logan Graham, Jared Kaplan, Sören Mindermann, Ryan Greenblatt, Buck Shlegeris, Nicholas Schiefer, and Ethan Perez
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- 2024
- Full Text
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10. Do Bayesian Neural Networks Need To Be Fully Stochastic?
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Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, and Tom Rainforth
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- 2023
11. Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt.
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Sören Mindermann, Jan Markus Brauner, Muhammed Razzak, Mrinank Sharma, Andreas Kirsch 0002, Winnie Xu, Benedikt Höltgen, Aidan N. Gomez, Adrien Morisot, Sebastian Farquhar, and Yarin Gal
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- 2022
12. Towards Understanding Sycophancy in Language Models.
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Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud, Amanda Askell, Samuel R. Bowman, Newton Cheng, Esin Durmus, Zac Hatfield-Dodds, Scott R. Johnston, Shauna Kravec, Timothy Maxwell, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, and Ethan Perez
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- 2023
- Full Text
- View/download PDF
13. Incorporating Unlabelled Data into Bayesian Neural Networks.
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Mrinank Sharma, Tom Rainforth, Yee Whye Teh, and Vincent Fortuin
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- 2023
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14. Understanding and Controlling a Maze-Solving Policy Network.
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Ulisse Mini, Peli Grietzer, Mrinank Sharma, Austin Meek, Monte MacDiarmid, and Alexander Matt Turner
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- 2023
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15. Do Bayesian Neural Networks Need To Be Fully Stochastic?
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Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, and Tom Rainforth
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- 2022
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16. How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?
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Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomas Gavenciak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, and Yarin Gal
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- 2020
17. Prioritized training on points that are learnable, worth learning, and not yet learned.
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Sören Mindermann, Muhammed Razzak, Winnie Xu, Andreas Kirsch 0002, Mrinank Sharma, Adrien Morisot, Aidan N. Gomez, Sebastian Farquhar, Jan Markus Brauner, and Yarin Gal
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- 2021
18. On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission.
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Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomas Gavenciak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, and Yarin Gal
- Published
- 2020
19. Differentially Private Federated Variational Inference.
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Mrinank Sharma, Michael J. Hutchinson, Siddharth Swaroop, Antti Honkela, and Richard E. Turner
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- 2019
20. Effectiveness assessment of non-pharmaceutical interventions: lessons learned from the COVID-19 pandemic
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Adrian Lison, Nicolas Banholzer, Mrinank Sharma, Sören Mindermann, H Juliette T Unwin, Swapnil Mishra, Tanja Stadler, Samir Bhatt, Neil M Ferguson, Jan Brauner, and Werner Vach
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360 Social problems & social services ,Public Health, Environmental and Occupational Health ,570 Life sciences ,biology ,610 Medicine & health - Abstract
Effectiveness of non-pharmaceutical interventions (NPIs), such as school closures and stay-at-home orders, during the COVID-19 pandemic has been assessed in many studies. Such assessments can inform public health policies and contribute to evidence-based choices of NPIs during subsequent waves or future epidemics. However, methodological issues and no standardised assessment practices have restricted the practical value of the existing evidence. Here, we present and discuss lessons learned from the COVID-19 pandemic and make recommendations for standardising and improving assessment, data collection, and modelling. These recommendations could contribute to reliable and policy-relevant assessments of the effectiveness of NPIs during future epidemics., The Lancet Public Health, 8 (4), ISSN:2468-2667
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- 2023
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21. Mask wearing in community settings reduces SARS-CoV-2 transmission
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Gavin Leech, Charlie Rogers-Smith, Joshua Teperowski Monrad, Jonas B. Sandbrink, Benedict Snodin, Robert Zinkov, Benjamin Rader, John S. Brownstein, Yarin Gal, Samir Bhatt, Mrinank Sharma, Sören Mindermann, Jan M. Brauner, and Laurence Aitchison
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face masks ,Multidisciplinary ,Surveys and Questionnaires ,Masks ,Humans ,COVID-19 ,Public Policy ,epidemiology ,hierarchical modeling ,Bayesian modeling ,Interactive Artificial Intelligence CDT - Abstract
The effectiveness of mask wearing at controlling severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been unclear. While masks are known to substantially reduce disease transmission in healthcare settings [D. K. Chu et al., Lancet 395, 1973–1987 (2020); J. Howard et al., Proc. Natl. Acad. Sci. U.S.A. 118, e2014564118 (2021); Y. Cheng et al., Science eabg6296 (2021)], studies in community settings report inconsistent results [H. M. Ollila et al., medRxiv (2020); J. Brainard et al., Eurosurveillance 25, 2000725 (2020); T. Jefferson et al., Cochrane Database Syst. Rev. 11, CD006207 (2020)]. Most such studies focus on how masks impact transmission, by analyzing how effective government mask mandates are. However, we find that widespread voluntary mask wearing, and other data limitations, make mandate effectiveness a poor proxy for mask-wearing effectiveness. We directly analyze the effect of mask wearing on SARS-CoV-2 transmission, drawing on several datasets covering 92 regions on six continents, including the largest survey of wearing behavior (n= 20 million) [F. Kreuter et al., https://gisumd.github.io/COVID-19-API-Documentation (2020)]. Using a Bayesian hierarchical model, we estimate the effect of mask wearing on transmission, by linking reported wearing levels to reported cases in each region, while adjusting for mobility and nonpharmaceutical interventions (NPIs), such as bans on large gatherings. Our estimates imply that the mean observed level of mask wearing corresponds to a 19% decrease in the reproduction number R. We also assess the robustness of our results in 60 tests spanning 20 sensitivity analyses. In light of these results, policy makers can effectively reduce transmission by intervening to increase mask wearing. The effectiveness of mask wearing at controlling severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been unclear. While masks are known to substantially reduce disease transmission in healthcare settings [D. K. Chu et al., Lancet 395, 1973–1987 (2020); J. Howard et al., Proc. Natl. Acad. Sci. U.S.A. 118, e2014564118 (2021); Y. Cheng et al., Science eabg6296 (2021)], studies in community settings report inconsistent results [H. M. Ollila et al., medRxiv (2020); J. Brainard et al., Eurosurveillance 25, 2000725 (2020); T. Jefferson et al., Cochrane Database Syst. Rev. 11, CD006207 (2020)]. Most such studies focus on how masks impact transmission, by analyzing how effective government mask mandates are. However, we find that widespread voluntary mask wearing, and other data limitations, make mandate effectiveness a poor proxy for mask-wearing effectiveness. We directly analyze the effect of mask wearing on SARS-CoV-2 transmission, drawing on several datasets covering 92 regions on six continents, including the largest survey of wearing behavior (n = 20 million) [F. Kreuter et al., https://gisumd.github.io/COVID-19-API-Documentation (2020)]. Using a Bayesian hierarchical model, we estimate the effect of mask wearing on transmission, by linking reported wearing levels to reported cases in each region, while adjusting for mobility and nonpharmaceutical interventions (NPIs), such as bans on large gatherings. Our estimates imply that the mean observed level of mask wearing corresponds to a 19% decrease in the reproduction number R. We also assess the robustness of our results in 60 tests spanning 20 sensitivity analyses. In light of these results, policy makers can effectively reduce transmission by intervening to increase mask wearing.
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- 2022
22. Seasonal variation in SARS-CoV-2 transmission in temperate climates:A Bayesian modelling study in 143 European regions
- Author
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Tomáš Gavenčiak, Joshua Teperowski Monrad, Gavin Leech, Mrinank Sharma, Sören Mindermann, Samir Bhatt, Jan Brauner, and Jan Kulveit
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DYNAMICS ,Ecology ,SARS-CoV-2 ,Climate ,COVID-19 ,Bayes Theorem ,HV Social pathology. Social and public welfare. Criminology ,Cellular and Molecular Neuroscience ,Computational Theory and Mathematics ,Modeling and Simulation ,RA0421 Public health. Hygiene. Preventive Medicine ,Genetics ,Humans ,VIRUS ,Seasons ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,INFLUENZA SEASONALITY - Abstract
Although seasonal variation has a known influence on the transmission of several respiratory viral infections, its role in SARS-CoV-2 transmission remains unclear. While there is a sizable and growing literature on environmental drivers of COVID-19 transmission, recent reviews have highlighted conflicting and inconclusive findings. This indeterminacy partly owes to the fact that seasonal variation relates to viral transmission by a complicated web of causal pathways, including many interacting biological and behavioural factors. Since analyses of specific factors cannot determine the aggregate strength of seasonal forcing, we sidestep the challenge of disentangling various possible causal paths in favor of a holistic approach. We model seasonality as a sinusoidal variation in transmission and infer a single Bayesian estimate of the overall seasonal effect. By extending two state-of-the-art models of non-pharmaceutical intervention (NPI) effects and their datasets covering 143 regions in temperate Europe, we are able to adjust our estimates for the role of both NPIs and mobility patterns in reducing transmission. We find strong seasonal patterns, consistent with a reduction in the time-varying reproduction number R(t) (the expected number of new infections generated by an infectious individual at time t) of 42.1% (95% CI: 24.7%—53.4%) from the peak of winter to the peak of summer. These results imply that the seasonality of SARS-CoV-2 transmission is comparable in magnitude to the most effective individual NPIs but less than the combined effect of multiple interventions. Although seasonal variation has a known influence on the transmission of several respiratory viral infections, its role in SARS-CoV-2 transmission remains unclear. While there is a sizable and growing literature on environmental drivers of COVID-19 transmission, recent reviews have highlighted conflicting and inconclusive findings. This indeterminacy partly owes to the fact that seasonal variation relates to viral transmission by a complicated web of causal pathways, including many interacting biological and behavioural factors. Since analyses of specific factors cannot determine the aggregate strength of seasonal forcing, we sidestep the challenge of disentangling various possible causal paths in favor of a holistic approach. We model seasonality as a sinusoidal variation in transmission and infer a single Bayesian estimate of the overall seasonal effect. By extending two state-of-the-art models of non-pharmaceutical intervention (NPI) effects and their datasets covering 143 regions in temperate Europe, we are able to adjust our estimates for the role of both NPIs and mobility patterns in reducing transmission. We find strong seasonal patterns, consistent with a reduction in the time-varying reproduction number R(t) (the expected number of new infections generated by an infectious individual at time t) of 42.1% (95% CI: 24.7%-53.4%) from the peak of winter to the peak of summer. These results imply that the seasonality of SARS-CoV-2 transmission is comparable in magnitude to the most effective individual NPIs but less than the combined effect of multiple interventions.
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- 2022
23. Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe
- Author
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Joshua Teperowski Monrad, Lukas Finnveden, Charlie Rogers-Smith, Julia Fabienne Sandkühler, Thomas A. Mellan, Janvi Ahuja, Jan Markus Brauner, Mrinank Sharma, Seth Flaxman, Gavin Leech, Gurpreet Dhaliwal, Jan Kulveit, Sören Mindermann, Yarin Gal, Sebastian B. Oehm, Laurence Aitchison, Benedict E. K. Snodin, Samir Bhatt, George T. Altman, Jonas B. Sandbrink, Leonid Chindelevitch, Swapnil Mishra, Tomáš Gavenčiak, Alexander John Norman, Sharma, Mrinank [0000-0002-4304-7963], Mindermann, Sören [0000-0002-0315-9821], Leech, Gavin [0000-0002-9298-1488], Monrad, Joshua Teperowski [0000-0002-7377-2074], Oehm, Sebastian B [0000-0002-7099-0578], Flaxman, Seth [0000-0002-2477-4217], Gal, Yarin [0000-0002-2733-2078], Mishra, Swapnil [0000-0002-8759-5902], Bhatt, Samir [0000-0002-0891-4611], Brauner, Jan Markus [0000-0002-1588-5724], Apollo - University of Cambridge Repository, Medical Research Council (MRC), Imperial College Healthcare NHS Trust- BRC Funding, The Academy of Medical Sciences, National Institute for Health Research, UK Research and Innovation, and Oehm, Sebastian B. [0000-0002-7099-0578]
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141 ,Time Factors ,Coronavirus disease 2019 (COVID-19) ,Epidemiology ,Science ,media_common.quotation_subject ,Control (management) ,Psychological intervention ,Basic Reproduction Number ,General Physics and Astronomy ,HM ,Educational institution ,HV Social pathology. Social and public welfare. Criminology ,General Biochemistry, Genetics and Molecular Biology ,RA0421 Public health. Hygiene. Preventive Medicine ,SAFER ,Humans ,692/308/174 ,media_common ,Government ,Science & Technology ,Multidisciplinary ,Public economics ,SARS-CoV-2 ,Closing (real estate) ,article ,COVID-19 ,General Chemistry ,Wirtschaftswissenschaften ,JN Political institutions (Europe) ,Models, Theoretical ,Public life ,692/700/478/174 ,Interactive Artificial Intelligence CDT ,Multidisciplinary Sciences ,MODEL ,Europe ,Science & Technology - Other Topics ,Business ,119 ,RA - Abstract
Funder: European and Developing Countries Clinical Trials Partnership (EDCTP); doi: https://doi.org/10.13039/501100001713, Funder: MRC Centre for Global Infectious Disease Analysis (MR/R015600/1), jointly funded by the U.K. Medical Research Council (MRC) and the U.K. Foreign, Commonwealth and Development Office (FCDO), under the MRC/FCDO Concordat agreement. Community Jameel. The UK Research and Innovation (MR/V038109/1), the Academy of Medical Sciences Springboard Award (SBF004/1080), The MRC (MR/R015600/1), The BMGF (OPP1197730), Imperial College Healthcare NHS Trust- BRC Funding (RDA02), The Novo Nordisk Young Investigator Award (NNF20OC0059309) and The NIHR Health Protection Research Unit in Modelling Methodology. S. Bhatt thanks Microsoft AI for Health and Amazon AWS for computational credits., Funder: EA Funds, Funder: University of Oxford (Oxford University); doi: https://doi.org/10.13039/501100000769, Funder: DeepMind, Funder: OpenPhilanthropy, Funder: UKRI Centre for Doctoral Training in Interactive Artificial Intelligence (EP/S022937/1), Funder: Augustinus Fonden (Augustinus Foundation); doi: https://doi.org/10.13039/501100004954, Funder: Knud Højgaards Fond (Knud Højgaard Fund); doi: https://doi.org/10.13039/501100009938, Funder: Kai Lange og Gunhild Kai Langes Fond (Kai Lange and Gunhild Kai Lange Foundation); doi: https://doi.org/10.13039/501100008206, Funder: Aage og Johanne Louis-Hansens Fond (Aage and Johanne Louis-Hansen Foundation); doi: https://doi.org/10.13039/501100010344, Funder: William Demant Foundation, Funder: Boehringer Ingelheim Fonds (Stiftung für medizinische Grundlagenforschung); doi: https://doi.org/10.13039/501100001645, Funder: Imperial College COVID-19 Research Fund, Funder: Cancer Research UK (CRUK); doi: https://doi.org/10.13039/501100000289, European governments use non-pharmaceutical interventions (NPIs) to control resurging waves of COVID-19. However, they only have outdated estimates for how effective individual NPIs were in the first wave. We estimate the effectiveness of 17 NPIs in Europe’s second wave from subnational case and death data by introducing a flexible hierarchical Bayesian transmission model and collecting the largest dataset of NPI implementation dates across Europe. Business closures, educational institution closures, and gathering bans reduced transmission, but reduced it less than they did in the first wave. This difference is likely due to organisational safety measures and individual protective behaviours—such as distancing—which made various areas of public life safer and thereby reduced the effect of closing them. Specifically, we find smaller effects for closing educational institutions, suggesting that stringent safety measures made schools safer compared to the first wave. Second-wave estimates outperform previous estimates at predicting transmission in Europe’s third wave.
- Published
- 2021
24. Mass mask-wearing notably reduces COVID-19 transmission
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Benjamin Rader, Mrinank Sharma, Samir Bhatt, Jonas B. Sandbrink, Gavin Leech, John S. Brownstein, Sören Mindermann, Laurence Aitchison, Charlie Rogers-Smith, Benedict E. K. Snodin, Yarin Gal, Robert Zinkov, and Jan Markus Brauner
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Entire population ,Median ,Geography ,Transmission (mechanics) ,Coronavirus disease 2019 (COVID-19) ,law ,Statistics ,Healthcare settings ,Community setting ,Proxy (statistics) ,Disease transmission ,law.invention - Abstract
Mask-wearing has been a controversial measure to control the COVID-19 pandemic. While masks are known to substantially reduce disease transmission in healthcare settings [1–3], studies in community settings report inconsistent results [4–6].Investigating the inconsistency within epidemiological studies, we find that a commonly used proxy, government mask mandates, does not correlate with large increases in mask-wearing in our window of analysis. We thus analyse the effect of mask-wearing on transmission instead, drawing on several datasets covering 92 regions on 6 continents, including the largest survey of individual-level wearing behaviour (n=20 million) [7]. Using a hierarchical Bayesian model, we estimate the effect of both mask-wearing and mask-mandates on transmission by linking wearing levels (or mandates) to reported cases in each region, adjusting for mobility and non-pharmaceutical interventions.We assess the robustness of our results in 123 experiments spanning 22 sensitivity analyses. Across these analyses, we find that an entire population wearing masks in public leads to a median reduction in the reproduction number R of 25.8%, with 95% of the medians between 22.2% and 30.9%. In our window of analysis, the median reduction in R associated with the wearing level observed in each region was 20.4% [2.0%, 23.3%]1. We do not find evidence that mandating mask-wearing reduces transmission. Our results suggest that mask-wearing is strongly affected by factors other than mandates.We establish the effectiveness of mass mask-wearing, and highlight that wearing data, not mandate data, are necessary to infer this effect.
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- 2021
25. Seasonal variation in SARS-CoV-2 transmission in temperate climates
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Jan Kulveit, Mrinank Sharma, Samir Bhatt, Joshua Teperowski Monrad, Tomáš Gavenčiak, Gavin Leech, Sören Mindermann, and Jan Markus Brauner
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2019-20 coronavirus outbreak ,Transmission (mechanics) ,Coronavirus disease 2019 (COVID-19) ,law ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Climatology ,medicine ,Temperate climate ,Seasonality ,Biology ,medicine.disease ,law.invention - Abstract
While seasonal variation has a known influence on the transmission of several respiratory viral infections, its role in SARS-CoV-2 transmission remains unclear. As previous analyses have not accounted for the implementation of non-pharmaceutical interventions (NPIs) in the first year of the pandemic, they may yield biased estimates of seasonal effects. Building on two state-of-the-art observational models and datasets, we adapt a fully Bayesian method for estimating the association between seasonality and transmission in 143 temperate European regions. We find strong seasonal patterns, consistent with a reduction in the time-variableRtof 42.1% (95% CI: 24.7% – 53.4%) from the peak of winter to the peak of summer. These results imply that the seasonality of SARS-CoV-2 transmission is comparable in magnitude to the most effective individual NPIs but less than the combined effect of multiple interventions.
- Published
- 2021
26. Understanding the effectiveness of government interventions in Europe’s second wave of COVID-19
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Sharmistha Mishra, George T. Altman, Leonid Chindelevitch, Lukas Finnveden, Yarin Gal, Joshua Teperowski Monrad, Seth Flaxman, Jan Kulveit, Gavin Leech, Charlie Rogers-Smith, Mrinank Sharma, Benedict E. K. Snodin, Jonas B. Sandbrink, Oehm Sb, Thomas A. Mellan, Samir Bhatt, Sören Mindermann, Alexander John Norman, Sandkühler Jf, Jan Markus Brauner, Dhaliwal G, and Ahuja J
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Government ,Coronavirus disease 2019 (COVID-19) ,Computer science ,SAFER ,Bayesian probability ,Econometrics ,Psychological intervention ,Set (psychology) ,Robustness (economics) ,Diversity (business) - Abstract
As European governments face resurging waves of COVID-19, non-pharmaceutical interventions (NPIs) continue to be the primary tool for infection control. However, updated estimates of their relative effectiveness have been absent for Europe’s second wave, largely due to a lack of collated data that considers the increased subnational variation and diversity of NPIs. We collect the largest dataset of NPI implementation dates in Europe, spanning 114 subnational areas in 7 countries, with a systematic categorisation of interventions tailored to the second wave. Using a hierarchical Bayesian transmission model, we estimate the effectiveness of 17 NPIs from local case and death data. We manually validate the data, address limitations in modelling from previous studies, and extensively test the robustness of our estimates. The combined effect of all NPIs was smaller relative to estimates from the first half of 2020, indicating the strong influence of safety measures and individual protective behaviours--such as distancing--that persisted after the first wave. Closing specific businesses was highly effective. Gathering restrictions were highly effective but only for the strictest limits. We find smaller effects for closing educational institutions compared to the first wave, suggesting that safer operation of schools was possible with a set of stringent safety measures including testing and tracing, preventing mixing, and smaller classes. These results underscore that effectiveness estimates from the early stage of an epidemic are measured relative to pre-pandemic behaviour. Updated estimates are required to inform policy in an ongoing pandemic.
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- 2021
27. The effectiveness of eight nonpharmaceutical interventions against COVID-19 in 41 countries
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Jan M. Brauner, Sören Mindermann, Mrinank Sharma, Anna B. Stephenson, Tomáš Gavenčiak, David Johnston, Gavin Leech, John Salvatier, George Altman, Alexander John Norman, Joshua Teperowski Monrad, Tamay Besiroglu, Hong Ge, Vladimir Mikulik, Meghan A. Hartwick, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal, and Jan Kulveit
- Subjects
education.field_of_study ,Coronavirus disease 2019 (COVID-19) ,business.industry ,Population ,Psychological intervention ,law.invention ,Transmission (mechanics) ,Policy decision ,law ,Data quality ,Credible interval ,Bayesian hierarchical modeling ,Medicine ,education ,business ,Demography - Abstract
BackgroundGovernments are attempting to control the COVID-19 pandemic with nonpharmaceutical interventions (NPIs). However, it is still largely unknown how effective different NPIs are at reducing transmission. Data-driven studies can estimate the effectiveness of NPIs while minimizing assumptions, but existing analyses lack sufficient data and validation to robustly distinguish the effects of individual NPIs.MethodsWe collect chronological data on NPIs in 41 countries between January and May 2020, using independent double entry by researchers to ensure high data quality. We estimate NPI effectiveness with a Bayesian hierarchical model, by linking NPI implementation dates to national case and death counts. To our knowledge, this is the largest and most thoroughly validated data-driven study of NPI effectiveness to date.ResultsWe model each NPI’s effect as a multiplicative (percentage) reduction in the reproduction number R. We estimate the mean reduction in R across the countries in our data for eight NPIs: mandating mask-wearing in (some) public spaces (2%; 95% CI: −14%–16%), limiting gatherings to 1000 people or less (2%; −20%–22%), to 100 people or less (21%; 1%–39%), to 10 people or less (36%; 16%–53%), closing some high-risk businesses (31%; 13%–46%), closing most nonessential businesses (40%; 22%–55%), closing schools and universities (39%; 21%–55%), and issuing stay-at-home orders (18%; 4%–31%). These results are supported by extensive empirical validation, including 15 sensitivity analyses.ConclusionsOur results suggest that, by implementing effective NPIs, many countries can reduce R below 1 without issuing a stay-at-home order. We find a surprisingly large role for school and university closures in reducing COVID-19 transmission, a contribution to the ongoing debate about the relevance of asymptomatic carriers in disease spread. Banning gatherings and closing high-risk businesses can be highly effective in reducing transmission, but closing most businesses only has limited additional benefit.
- Published
- 2020
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