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Kugel R, Schaye J, Schaller M, Helly JC, Braspenning J, Elbers W, Frenk CS, McCarthy IG, Kwan J, Salcido J, van Daalen MP, Vandenbroucke B, Bahé YM, Borrow J, Chaikin E, Huško F, Jenkins A, Lacey CG, Nobels FSJ, Vernon I. FLAMINGO: calibrating large cosmological hydrodynamical simulations with machine learning. Mon Not R Astron Soc 2023; 526:6103-6127. [PMID: 37900898 PMCID: PMC10602225 DOI: 10.1093/mnras/stad2540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/08/2023] [Accepted: 08/12/2023] [Indexed: 10/31/2023]
Abstract
To fully take advantage of the data provided by large-scale structure surveys, we need to quantify the potential impact of baryonic effects, such as feedback from active galactic nuclei (AGN) and star formation, on cosmological observables. In simulations, feedback processes originate on scales that remain unresolved. Therefore, they need to be sourced via subgrid models that contain free parameters. We use machine learning to calibrate the AGN and stellar feedback models for the FLAMINGO (Fullhydro Large-scale structure simulations with All-sky Mapping for the Interpretation of Next Generation Observations) cosmological hydrodynamical simulations. Using Gaussian process emulators trained on Latin hypercubes of 32 smaller volume simulations, we model how the galaxy stellar mass function (SMF) and cluster gas fractions change as a function of the subgrid parameters. The emulators are then fit to observational data, allowing for the inclusion of potential observational biases. We apply our method to the three different FLAMINGO resolutions, spanning a factor of 64 in particle mass, recovering the observed relations within the respective resolved mass ranges. We also use the emulators, which link changes in subgrid parameters to changes in observables, to find models that skirt or exceed the observationally allowed range for cluster gas fractions and the SMF. Our method enables us to define model variations in terms of the data that they are calibrated to rather than the values of specific subgrid parameters. This approach is useful, because subgrid parameters are typically not directly linked to particular observables, and predictions for a specific observable are influenced by multiple subgrid parameters.
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Affiliation(s)
- Roi Kugel
- Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands
| | - Joop Schaye
- Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands
| | - Matthieu Schaller
- Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands
- Lorentz Institute for Theoretical Physics, Leiden University, PO box 9506, NL-2300 RA Leiden, the Netherlands
| | - John C Helly
- Institute for Computational Cosmology, Department of Physics, University of Durham, South Road, Durham DH1 3LE, UK
| | - Joey Braspenning
- Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands
| | - Willem Elbers
- Institute for Computational Cosmology, Department of Physics, University of Durham, South Road, Durham DH1 3LE, UK
| | - Carlos S Frenk
- Institute for Computational Cosmology, Department of Physics, University of Durham, South Road, Durham DH1 3LE, UK
| | - Ian G McCarthy
- Astrophysics Research Institute, Liverpool John Moores University, Liverpool L3 5RF, UK
| | - Juliana Kwan
- Astrophysics Research Institute, Liverpool John Moores University, Liverpool L3 5RF, UK
| | - Jaime Salcido
- Astrophysics Research Institute, Liverpool John Moores University, Liverpool L3 5RF, UK
| | - Marcel P van Daalen
- Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands
| | - Bert Vandenbroucke
- Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands
| | - Yannick M Bahé
- Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands
- Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Observatoire de Sauverny, CH-1290 Versoix, Switzerland
| | - Josh Borrow
- Institute for Computational Cosmology, Department of Physics, University of Durham, South Road, Durham DH1 3LE, UK
- Department of Physics, Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Evgenii Chaikin
- Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands
| | - Filip Huško
- Institute for Computational Cosmology, Department of Physics, University of Durham, South Road, Durham DH1 3LE, UK
| | - Adrian Jenkins
- Institute for Computational Cosmology, Department of Physics, University of Durham, South Road, Durham DH1 3LE, UK
| | - Cedric G Lacey
- Institute for Computational Cosmology, Department of Physics, University of Durham, South Road, Durham DH1 3LE, UK
| | - Folkert S J Nobels
- Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands
| | - Ian Vernon
- Department of Mathematical Sciences, Durham University, Stockton Road, DH1 3LE Durham, UK
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2
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Hu B, Jiang W, Miyagi T, Sun Z, Ekström A, Forssén C, Hagen G, Holt JD, Papenbrock T, Stroberg SR, Vernon I. Author Correction: Ab initio predictions link the neutron skin of 208Pb to nuclear forces. Nat Phys 2023; 20:169. [PMID: 38239896 PMCID: PMC10791583 DOI: 10.1038/s41567-023-02324-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/12/2024]
Abstract
[This corrects the article DOI: 10.1038/s41567-022-01715-8.].
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Affiliation(s)
- Baishan Hu
- TRIUMF, Vancouver, British Columbia Canada
| | - Weiguang Jiang
- Department of Physics, Chalmers University of Technology, Gothenburg, Sweden
| | - Takayuki Miyagi
- TRIUMF, Vancouver, British Columbia Canada
- Department of Physics, Technische Universität Darmstadt, Darmstadt, Germany
- ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt, Germany
| | - Zhonghao Sun
- Department of Physics and Astronomy, University of Tennessee, Knoxville, TN USA
- Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN USA
| | - Andreas Ekström
- Department of Physics, Chalmers University of Technology, Gothenburg, Sweden
| | - Christian Forssén
- Department of Physics, Chalmers University of Technology, Gothenburg, Sweden
| | - Gaute Hagen
- TRIUMF, Vancouver, British Columbia Canada
- Department of Physics and Astronomy, University of Tennessee, Knoxville, TN USA
- Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN USA
| | - Jason D. Holt
- TRIUMF, Vancouver, British Columbia Canada
- Department of Physics, McGill University, Montreal, Quebec Canada
| | - Thomas Papenbrock
- Department of Physics and Astronomy, University of Tennessee, Knoxville, TN USA
- Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN USA
| | - S. Ragnar Stroberg
- Department of Physics, University of Washington, Seattle, WA USA
- Physics Division, Argonne National Laboratory, Lemont, IL USA
| | - Ian Vernon
- Department of Mathematical Sciences, Durham University, Durham, UK
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3
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Kondo Y, Achouri NL, Falou HA, Atar L, Aumann T, Baba H, Boretzky K, Caesar C, Calvet D, Chae H, Chiga N, Corsi A, Delaunay F, Delbart A, Deshayes Q, Dombrádi Z, Douma CA, Ekström A, Elekes Z, Forssén C, Gašparić I, Gheller JM, Gibelin J, Gillibert A, Hagen G, Harakeh MN, Hirayama A, Hoffman CR, Holl M, Horvat A, Horváth Á, Hwang JW, Isobe T, Jiang WG, Kahlbow J, Kalantar-Nayestanaki N, Kawase S, Kim S, Kisamori K, Kobayashi T, Körper D, Koyama S, Kuti I, Lapoux V, Lindberg S, Marqués FM, Masuoka S, Mayer J, Miki K, Murakami T, Najafi M, Nakamura T, Nakano K, Nakatsuka N, Nilsson T, Obertelli A, Ogata K, de Oliveira Santos F, Orr NA, Otsu H, Otsuka T, Ozaki T, Panin V, Papenbrock T, Paschalis S, Revel A, Rossi D, Saito AT, Saito TY, Sasano M, Sato H, Satou Y, Scheit H, Schindler F, Schrock P, Shikata M, Shimizu N, Shimizu Y, Simon H, Sohler D, Sorlin O, Stuhl L, Sun ZH, Takeuchi S, Tanaka M, Thoennessen M, Törnqvist H, Togano Y, Tomai T, Tscheuschner J, Tsubota J, Tsunoda N, Uesaka T, Utsuno Y, Vernon I, Wang H, Yang Z, Yasuda M, Yoneda K, Yoshida S. Publisher Correction: First observation of 28O. Nature 2023; 623:E13. [PMID: 37935927 PMCID: PMC10665181 DOI: 10.1038/s41586-023-06815-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Affiliation(s)
- Y Kondo
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan.
- RIKEN Nishina Center, Saitama, Japan.
| | - N L Achouri
- LPC Caen UMR6534, Université de Caen Normandie, ENSICAEN, CNRS/IN2P3, Caen, France
| | - H Al Falou
- Lebanese University, Beirut, Lebanon
- Lebanese-French University of Technology and Applied Sciences, Deddeh, Lebanon
| | - L Atar
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
| | - T Aumann
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
- Helmholtz Research Academy Hesse for FAIR, Darmstadt, Germany
| | - H Baba
- RIKEN Nishina Center, Saitama, Japan
| | - K Boretzky
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
| | - C Caesar
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
| | - D Calvet
- Irfu, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - H Chae
- Institute for Basic Science, Daejeon, Republic of Korea
| | - N Chiga
- RIKEN Nishina Center, Saitama, Japan
| | - A Corsi
- Irfu, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - F Delaunay
- LPC Caen UMR6534, Université de Caen Normandie, ENSICAEN, CNRS/IN2P3, Caen, France
| | - A Delbart
- Irfu, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Q Deshayes
- LPC Caen UMR6534, Université de Caen Normandie, ENSICAEN, CNRS/IN2P3, Caen, France
| | | | - C A Douma
- ESRIG, University of Groningen, Groningen, The Netherlands
| | - A Ekström
- Institutionen för Fysik, Chalmers Tekniska Högskola, Göteborg, Sweden
| | | | - C Forssén
- Institutionen för Fysik, Chalmers Tekniska Högskola, Göteborg, Sweden
| | - I Gašparić
- RIKEN Nishina Center, Saitama, Japan
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
- Ruđer Bošković Institute, Zagreb, Croatia
| | - J-M Gheller
- Irfu, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - J Gibelin
- LPC Caen UMR6534, Université de Caen Normandie, ENSICAEN, CNRS/IN2P3, Caen, France
| | - A Gillibert
- Irfu, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - G Hagen
- Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Department of Physics and Astronomy, University of Tennessee, Knoxville, TN, USA
| | - M N Harakeh
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
- ESRIG, University of Groningen, Groningen, The Netherlands
| | - A Hirayama
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
| | - C R Hoffman
- Physics Division, Argonne National Laboratory, Argonne, IL, USA
| | - M Holl
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
| | - A Horvat
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
| | - Á Horváth
- Eötvös Loránd University, Budapest, Hungary
| | - J W Hwang
- Center for Exotic Nuclear Studies, Institute for Basic Science, Daejeon, Republic of Korea
- Department of Physics and Astronomy, Seoul National University, Seoul, Republic of Korea
| | - T Isobe
- RIKEN Nishina Center, Saitama, Japan
| | - W G Jiang
- Institutionen för Fysik, Chalmers Tekniska Högskola, Göteborg, Sweden
| | - J Kahlbow
- RIKEN Nishina Center, Saitama, Japan
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
| | | | - S Kawase
- Department of Advanced Energy Engineering Science, Kyushu University, Fukuoka, Japan
| | - S Kim
- Center for Exotic Nuclear Studies, Institute for Basic Science, Daejeon, Republic of Korea
- Department of Physics and Astronomy, Seoul National University, Seoul, Republic of Korea
| | | | - T Kobayashi
- Department of Physics, Tohoku University, Miyagi, Japan
| | - D Körper
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
| | - S Koyama
- Department of Physics, The University of Tokyo, Tokyo, Japan
| | - I Kuti
- Atomki, Debrecen, Hungary
| | - V Lapoux
- Irfu, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - S Lindberg
- Institutionen för Fysik, Chalmers Tekniska Högskola, Göteborg, Sweden
| | - F M Marqués
- LPC Caen UMR6534, Université de Caen Normandie, ENSICAEN, CNRS/IN2P3, Caen, France
| | - S Masuoka
- Center for Nuclear Study, The University of Tokyo, Saitama, Japan
| | - J Mayer
- Institut für Kernphysik, Universität zu Köln, Köln, Germany
| | - K Miki
- Department of Physics, Tohoku University, Miyagi, Japan
| | - T Murakami
- Department of Physics, Kyoto University, Kyoto, Japan
| | - M Najafi
- ESRIG, University of Groningen, Groningen, The Netherlands
| | - T Nakamura
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
- RIKEN Nishina Center, Saitama, Japan
| | - K Nakano
- Department of Advanced Energy Engineering Science, Kyushu University, Fukuoka, Japan
| | - N Nakatsuka
- Department of Physics, Kyoto University, Kyoto, Japan
| | - T Nilsson
- Institutionen för Fysik, Chalmers Tekniska Högskola, Göteborg, Sweden
| | - A Obertelli
- Irfu, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - K Ogata
- Department of Physics, Kyushu University, Fukuoka, Japan
- Research Center for Nuclear Physics, Osaka University, Osaka, Japan
- Department of Physics, Osaka City University, Osaka, Japan
| | - F de Oliveira Santos
- Grand Accélérateur National d'Ions Lourds (GANIL), CEA/DRF-CNRS/IN2P3, Caen, France
| | - N A Orr
- LPC Caen UMR6534, Université de Caen Normandie, ENSICAEN, CNRS/IN2P3, Caen, France
| | - H Otsu
- RIKEN Nishina Center, Saitama, Japan
| | - T Otsuka
- RIKEN Nishina Center, Saitama, Japan
- Department of Physics, The University of Tokyo, Tokyo, Japan
| | - T Ozaki
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
| | - V Panin
- RIKEN Nishina Center, Saitama, Japan
| | - T Papenbrock
- Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Department of Physics and Astronomy, University of Tennessee, Knoxville, TN, USA
| | - S Paschalis
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
| | - A Revel
- LPC Caen UMR6534, Université de Caen Normandie, ENSICAEN, CNRS/IN2P3, Caen, France
- Grand Accélérateur National d'Ions Lourds (GANIL), CEA/DRF-CNRS/IN2P3, Caen, France
| | - D Rossi
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
| | - A T Saito
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
| | - T Y Saito
- Department of Physics, The University of Tokyo, Tokyo, Japan
| | - M Sasano
- RIKEN Nishina Center, Saitama, Japan
| | - H Sato
- RIKEN Nishina Center, Saitama, Japan
| | - Y Satou
- Department of Physics and Astronomy, Seoul National University, Seoul, Republic of Korea
| | - H Scheit
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
| | - F Schindler
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
| | - P Schrock
- Center for Nuclear Study, The University of Tokyo, Saitama, Japan
| | - M Shikata
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
| | - N Shimizu
- Center for Computational Sciences, University of Tsukuba, Ibaraki, Japan
| | - Y Shimizu
- RIKEN Nishina Center, Saitama, Japan
| | - H Simon
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
| | | | - O Sorlin
- Grand Accélérateur National d'Ions Lourds (GANIL), CEA/DRF-CNRS/IN2P3, Caen, France
| | - L Stuhl
- RIKEN Nishina Center, Saitama, Japan
- Center for Exotic Nuclear Studies, Institute for Basic Science, Daejeon, Republic of Korea
| | - Z H Sun
- Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Department of Physics and Astronomy, University of Tennessee, Knoxville, TN, USA
| | - S Takeuchi
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
| | - M Tanaka
- Department of Physics, Osaka University, Osaka, Japan
| | - M Thoennessen
- Facility for Rare Isotope Beams, Michigan State University, East Lansing, MI, USA
| | - H Törnqvist
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
| | - Y Togano
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
- Department of Physics, Rikkyo University, Tokyo, Japan
| | - T Tomai
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
| | - J Tscheuschner
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
| | - J Tsubota
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
| | - N Tsunoda
- Center for Nuclear Study, The University of Tokyo, Saitama, Japan
| | - T Uesaka
- RIKEN Nishina Center, Saitama, Japan
| | - Y Utsuno
- Advanced Science Research Center, Japan Atomic Energy Agency, Ibaraki, Japan
| | - I Vernon
- Department of Mathematical Sciences, Durham University, Durham, UK
| | - H Wang
- RIKEN Nishina Center, Saitama, Japan
| | - Z Yang
- RIKEN Nishina Center, Saitama, Japan
| | - M Yasuda
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
| | - K Yoneda
- RIKEN Nishina Center, Saitama, Japan
| | - S Yoshida
- Liberal and General Education Center, Institute for Promotion of Higher Academic Education, Utsunomiya University, Tochigi, Japan
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4
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Kondo Y, Achouri NL, Falou HA, Atar L, Aumann T, Baba H, Boretzky K, Caesar C, Calvet D, Chae H, Chiga N, Corsi A, Delaunay F, Delbart A, Deshayes Q, Dombrádi Z, Douma CA, Ekström A, Elekes Z, Forssén C, Gašparić I, Gheller JM, Gibelin J, Gillibert A, Hagen G, Harakeh MN, Hirayama A, Hoffman CR, Holl M, Horvat A, Horváth Á, Hwang JW, Isobe T, Jiang WG, Kahlbow J, Kalantar-Nayestanaki N, Kawase S, Kim S, Kisamori K, Kobayashi T, Körper D, Koyama S, Kuti I, Lapoux V, Lindberg S, Marqués FM, Masuoka S, Mayer J, Miki K, Murakami T, Najafi M, Nakamura T, Nakano K, Nakatsuka N, Nilsson T, Obertelli A, Ogata K, de Oliveira Santos F, Orr NA, Otsu H, Otsuka T, Ozaki T, Panin V, Papenbrock T, Paschalis S, Revel A, Rossi D, Saito AT, Saito TY, Sasano M, Sato H, Satou Y, Scheit H, Schindler F, Schrock P, Shikata M, Shimizu N, Shimizu Y, Simon H, Sohler D, Sorlin O, Stuhl L, Sun ZH, Takeuchi S, Tanaka M, Thoennessen M, Törnqvist H, Togano Y, Tomai T, Tscheuschner J, Tsubota J, Tsunoda N, Uesaka T, Utsuno Y, Vernon I, Wang H, Yang Z, Yasuda M, Yoneda K, Yoshida S. First observation of 28O. Nature 2023; 620:965-970. [PMID: 37648757 PMCID: PMC10630140 DOI: 10.1038/s41586-023-06352-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 06/21/2023] [Indexed: 09/01/2023]
Abstract
Subjecting a physical system to extreme conditions is one of the means often used to obtain a better understanding and deeper insight into its organization and structure. In the case of the atomic nucleus, one such approach is to investigate isotopes that have very different neutron-to-proton (N/Z) ratios than in stable nuclei. Light, neutron-rich isotopes exhibit the most asymmetric N/Z ratios and those lying beyond the limits of binding, which undergo spontaneous neutron emission and exist only as very short-lived resonances (about 10-21 s), provide the most stringent tests of modern nuclear-structure theories. Here we report on the first observation of 28O and 27O through their decay into 24O and four and three neutrons, respectively. The 28O nucleus is of particular interest as, with the Z = 8 and N = 20 magic numbers1,2, it is expected in the standard shell-model picture of nuclear structure to be one of a relatively small number of so-called 'doubly magic' nuclei. Both 27O and 28O were found to exist as narrow, low-lying resonances and their decay energies are compared here to the results of sophisticated theoretical modelling, including a large-scale shell-model calculation and a newly developed statistical approach. In both cases, the underlying nuclear interactions were derived from effective field theories of quantum chromodynamics. Finally, it is shown that the cross-section for the production of 28O from a 29F beam is consistent with it not exhibiting a closed N = 20 shell structure.
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Affiliation(s)
- Y Kondo
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan.
- RIKEN Nishina Center, Saitama, Japan.
| | - N L Achouri
- LPC Caen UMR6534, Université de Caen Normandie, ENSICAEN, CNRS/IN2P3, Caen, France
| | - H Al Falou
- Lebanese University, Beirut, Lebanon
- Lebanese-French University of Technology and Applied Sciences, Deddeh, Lebanon
| | - L Atar
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
| | - T Aumann
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
- Helmholtz Research Academy Hesse for FAIR, Darmstadt, Germany
| | - H Baba
- RIKEN Nishina Center, Saitama, Japan
| | - K Boretzky
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
| | - C Caesar
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
| | - D Calvet
- Irfu, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - H Chae
- Institute for Basic Science, Daejeon, Republic of Korea
| | - N Chiga
- RIKEN Nishina Center, Saitama, Japan
| | - A Corsi
- Irfu, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - F Delaunay
- LPC Caen UMR6534, Université de Caen Normandie, ENSICAEN, CNRS/IN2P3, Caen, France
| | - A Delbart
- Irfu, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Q Deshayes
- LPC Caen UMR6534, Université de Caen Normandie, ENSICAEN, CNRS/IN2P3, Caen, France
| | | | - C A Douma
- ESRIG, University of Groningen, Groningen, The Netherlands
| | - A Ekström
- Institutionen för Fysik, Chalmers Tekniska Högskola, Göteborg, Sweden
| | | | - C Forssén
- Institutionen för Fysik, Chalmers Tekniska Högskola, Göteborg, Sweden
| | - I Gašparić
- RIKEN Nishina Center, Saitama, Japan
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
- Ruđer Bošković Institute, Zagreb, Croatia
| | - J-M Gheller
- Irfu, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - J Gibelin
- LPC Caen UMR6534, Université de Caen Normandie, ENSICAEN, CNRS/IN2P3, Caen, France
| | - A Gillibert
- Irfu, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - G Hagen
- Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Department of Physics and Astronomy, University of Tennessee, Knoxville, TN, USA
| | - M N Harakeh
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
- ESRIG, University of Groningen, Groningen, The Netherlands
| | - A Hirayama
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
| | - C R Hoffman
- Physics Division, Argonne National Laboratory, Argonne, IL, USA
| | - M Holl
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
| | - A Horvat
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
| | - Á Horváth
- Eötvös Loránd University, Budapest, Hungary
| | - J W Hwang
- Center for Exotic Nuclear Studies, Institute for Basic Science, Daejeon, Republic of Korea
- Department of Physics and Astronomy, Seoul National University, Seoul, Republic of Korea
| | - T Isobe
- RIKEN Nishina Center, Saitama, Japan
| | - W G Jiang
- Institutionen för Fysik, Chalmers Tekniska Högskola, Göteborg, Sweden
| | - J Kahlbow
- RIKEN Nishina Center, Saitama, Japan
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
| | | | - S Kawase
- Department of Advanced Energy Engineering Science, Kyushu University, Fukuoka, Japan
| | - S Kim
- Center for Exotic Nuclear Studies, Institute for Basic Science, Daejeon, Republic of Korea
- Department of Physics and Astronomy, Seoul National University, Seoul, Republic of Korea
| | | | - T Kobayashi
- Department of Physics, Tohoku University, Miyagi, Japan
| | - D Körper
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
| | - S Koyama
- Department of Physics, The University of Tokyo, Tokyo, Japan
| | - I Kuti
- Atomki, Debrecen, Hungary
| | - V Lapoux
- Irfu, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - S Lindberg
- Institutionen för Fysik, Chalmers Tekniska Högskola, Göteborg, Sweden
| | - F M Marqués
- LPC Caen UMR6534, Université de Caen Normandie, ENSICAEN, CNRS/IN2P3, Caen, France
| | - S Masuoka
- Center for Nuclear Study, The University of Tokyo, Saitama, Japan
| | - J Mayer
- Institut für Kernphysik, Universität zu Köln, Köln, Germany
| | - K Miki
- Department of Physics, Tohoku University, Miyagi, Japan
| | - T Murakami
- Department of Physics, Kyoto University, Kyoto, Japan
| | - M Najafi
- ESRIG, University of Groningen, Groningen, The Netherlands
| | - T Nakamura
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
- RIKEN Nishina Center, Saitama, Japan
| | - K Nakano
- Department of Advanced Energy Engineering Science, Kyushu University, Fukuoka, Japan
| | - N Nakatsuka
- Department of Physics, Kyoto University, Kyoto, Japan
| | - T Nilsson
- Institutionen för Fysik, Chalmers Tekniska Högskola, Göteborg, Sweden
| | - A Obertelli
- Irfu, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - K Ogata
- Department of Physics, Kyushu University, Fukuoka, Japan
- Research Center for Nuclear Physics, Osaka University, Osaka, Japan
- Department of Physics, Osaka City University, Osaka, Japan
| | - F de Oliveira Santos
- Grand Accélérateur National d'Ions Lourds (GANIL), CEA/DRF-CNRS/IN2P3, Caen, France
| | - N A Orr
- LPC Caen UMR6534, Université de Caen Normandie, ENSICAEN, CNRS/IN2P3, Caen, France
| | - H Otsu
- RIKEN Nishina Center, Saitama, Japan
| | - T Otsuka
- RIKEN Nishina Center, Saitama, Japan
- Department of Physics, The University of Tokyo, Tokyo, Japan
| | - T Ozaki
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
| | - V Panin
- RIKEN Nishina Center, Saitama, Japan
| | - T Papenbrock
- Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Department of Physics and Astronomy, University of Tennessee, Knoxville, TN, USA
| | - S Paschalis
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
| | - A Revel
- LPC Caen UMR6534, Université de Caen Normandie, ENSICAEN, CNRS/IN2P3, Caen, France
- Grand Accélérateur National d'Ions Lourds (GANIL), CEA/DRF-CNRS/IN2P3, Caen, France
| | - D Rossi
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
| | - A T Saito
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
| | - T Y Saito
- Department of Physics, The University of Tokyo, Tokyo, Japan
| | - M Sasano
- RIKEN Nishina Center, Saitama, Japan
| | - H Sato
- RIKEN Nishina Center, Saitama, Japan
| | - Y Satou
- Department of Physics and Astronomy, Seoul National University, Seoul, Republic of Korea
| | - H Scheit
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
| | - F Schindler
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
| | - P Schrock
- Center for Nuclear Study, The University of Tokyo, Saitama, Japan
| | - M Shikata
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
| | - N Shimizu
- Center for Computational Sciences, University of Tsukuba, Ibaraki, Japan
| | - Y Shimizu
- RIKEN Nishina Center, Saitama, Japan
| | - H Simon
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
| | | | - O Sorlin
- Grand Accélérateur National d'Ions Lourds (GANIL), CEA/DRF-CNRS/IN2P3, Caen, France
| | - L Stuhl
- RIKEN Nishina Center, Saitama, Japan
- Center for Exotic Nuclear Studies, Institute for Basic Science, Daejeon, Republic of Korea
| | - Z H Sun
- Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Department of Physics and Astronomy, University of Tennessee, Knoxville, TN, USA
| | - S Takeuchi
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
| | - M Tanaka
- Department of Physics, Osaka University, Osaka, Japan
| | - M Thoennessen
- Facility for Rare Isotope Beams, Michigan State University, East Lansing, MI, USA
| | - H Törnqvist
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany
| | - Y Togano
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
- Department of Physics, Rikkyo University, Tokyo, Japan
| | - T Tomai
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
| | - J Tscheuschner
- Institut für Kernphysik, Technische Universität Darmstadt, Darmstadt, Germany
| | - J Tsubota
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
| | - N Tsunoda
- Center for Nuclear Study, The University of Tokyo, Saitama, Japan
| | - T Uesaka
- RIKEN Nishina Center, Saitama, Japan
| | - Y Utsuno
- Advanced Science Research Center, Japan Atomic Energy Agency, Ibaraki, Japan
| | - I Vernon
- Department of Mathematical Sciences, Durham University, Durham, UK
| | - H Wang
- RIKEN Nishina Center, Saitama, Japan
| | - Z Yang
- RIKEN Nishina Center, Saitama, Japan
| | - M Yasuda
- Department of Physics, Tokyo Institute of Technology, Tokyo, Japan
| | - K Yoneda
- RIKEN Nishina Center, Saitama, Japan
| | - S Yoshida
- Liberal and General Education Center, Institute for Promotion of Higher Academic Education, Utsunomiya University, Tochigi, Japan
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5
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Scarponi D, Iskauskas A, Clark RA, Vernon I, McKinley TJ, Goldstein M, Mukandavire C, Deol A, Weerasuriya C, Bakker R, White RG, McCreesh N. Demonstrating multi-country calibration of a tuberculosis model using new history matching and emulation package - hmer. Epidemics 2023; 43:100678. [PMID: 36913805 DOI: 10.1016/j.epidem.2023.100678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 02/23/2023] [Accepted: 03/06/2023] [Indexed: 03/09/2023] Open
Abstract
Infectious disease models are widely used by epidemiologists to improve the understanding of transmission dynamics and disease natural history, and to predict the possible effects of interventions. As the complexity of such models increases, however, it becomes increasingly challenging to robustly calibrate them to empirical data. History matching with emulation is a calibration method that has been successfully applied to such models, but has not been widely used in epidemiology partly due to the lack of available software. To address this issue, we developed a new, user-friendly R package hmer to simply and efficiently perform history matching with emulation. In this paper, we demonstrate the first use of hmer for calibrating a complex deterministic model for the country-level implementation of tuberculosis vaccines to 115 low- and middle-income countries. The model was fit to 9-13 target measures, by varying 19-22 input parameters. Overall, 105 countries were successfully calibrated. Among the remaining countries, hmer visualisation tools, combined with derivative emulation methods, provided strong evidence that the models were misspecified and could not be calibrated to the target ranges. This work shows that hmer can be used to simply and rapidly calibrate a complex model to data from over 100 countries, making it a useful addition to the epidemiologist's calibration tool-kit.
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Affiliation(s)
- Danny Scarponi
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK.
| | | | - Rebecca A Clark
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - Ian Vernon
- Department of Mathematical Sciences, Durham University, UK
| | | | | | - Christinah Mukandavire
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - Arminder Deol
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - Chathika Weerasuriya
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - Roel Bakker
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - Richard G White
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - Nicky McCreesh
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
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6
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Vernon I, Owen J, Aylett-Bullock J, Cuesta-Lazaro C, Frawley J, Quera-Bofarull A, Sedgewick A, Shi D, Truong H, Turner M, Walker J, Caulfield T, Fong K, Krauss F. Bayesian emulation and history matching of JUNE. Philos Trans A Math Phys Eng Sci 2022; 380:20220039. [PMID: 35965471 PMCID: PMC9376712 DOI: 10.1098/rsta.2022.0039] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 06/07/2022] [Indexed: 05/21/2023]
Abstract
We analyze JUNE: a detailed model of COVID-19 transmission with high spatial and demographic resolution, developed as part of the RAMP initiative. JUNE requires substantial computational resources to evaluate, making model calibration and general uncertainty analysis extremely challenging. We describe and employ the uncertainty quantification approaches of Bayes linear emulation and history matching to mimic JUNE and to perform a global parameter search, hence identifying regions of parameter space that produce acceptable matches to observed data, and demonstrating the capability of such methods. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- I. Vernon
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Department of Mathematical Sciences, Durham University, Durham DH13LE, UK
| | - J. Owen
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Department of Mathematical Sciences, Durham University, Durham DH13LE, UK
| | - J. Aylett-Bullock
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK
| | - C. Cuesta-Lazaro
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH13LE, UK
| | - J. Frawley
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Advanced Research Computing, Durham University, Durham DH13LE, UK
| | - A. Quera-Bofarull
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH13LE, UK
| | - A. Sedgewick
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Centre for Extragalactic Astronomy, Durham University, Durham DH13LE, UK
| | - D. Shi
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH13LE, UK
| | - H. Truong
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK
| | - M. Turner
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Advanced Research Computing, Durham University, Durham DH13LE, UK
| | - J. Walker
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK
| | - T. Caulfield
- Department of Computer Science, Durham University, Durham DH13LE, UK
| | - K. Fong
- Department of Science, Technology, Engineering and Public Policy, University College London, London WC1E6BT, UK
- Department of Anaesthesia, University College London Hospital, London NW12BU, UK
| | - F. Krauss
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK
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7
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Vernon I, Owen J, Aylett-Bullock J, Cuesta-Lazaro C, Frawley J, Quera-Bofarull A, Sedgewick A, Shi D, Truong H, Turner M, Walker J, Caulfield T, Fong K, Krauss F. Bayesian emulation and history matching of JUNE. Philos Trans A Math Phys Eng Sci 2022; 380:20210039. [PMID: 35965471 DOI: 10.1098/rsta.2021.0039] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 12/06/2021] [Indexed: 05/21/2023]
Abstract
We analyze JUNE: a detailed model of COVID-19 transmission with high spatial and demographic resolution, developed as part of the RAMP initiative. JUNE requires substantial computational resources to evaluate, making model calibration and general uncertainty analysis extremely challenging. We describe and employ the uncertainty quantification approaches of Bayes linear emulation and history matching to mimic JUNE and to perform a global parameter search, hence identifying regions of parameter space that produce acceptable matches to observed data, and demonstrating the capability of such methods. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- I Vernon
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Department of Mathematical Sciences, Durham University, Durham DH13LE, UK
| | - J Owen
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Department of Mathematical Sciences, Durham University, Durham DH13LE, UK
| | - J Aylett-Bullock
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK
| | - C Cuesta-Lazaro
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH13LE, UK
| | - J Frawley
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Advanced Research Computing, Durham University, Durham DH13LE, UK
| | - A Quera-Bofarull
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH13LE, UK
| | - A Sedgewick
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Centre for Extragalactic Astronomy, Durham University, Durham DH13LE, UK
| | - D Shi
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH13LE, UK
| | - H Truong
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK
| | - M Turner
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Advanced Research Computing, Durham University, Durham DH13LE, UK
| | - J Walker
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK
| | - T Caulfield
- Department of Computer Science, Durham University, Durham DH13LE, UK
| | - K Fong
- Department of Science, Technology, Engineering and Public Policy, University College London, London WC1E6BT, UK
- Department of Anaesthesia, University College London Hospital, London NW12BU, UK
| | - F Krauss
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK
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8
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Hu B, Jiang W, Miyagi T, Sun Z, Ekström A, Forssén C, Hagen G, Holt JD, Papenbrock T, Stroberg SR, Vernon I. Ab initio predictions link the neutron skin of 208Pb to nuclear forces. Nat Phys 2022; 18:1196-1200. [PMID: 36217363 PMCID: PMC9537109 DOI: 10.1038/s41567-022-01715-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 07/11/2022] [Indexed: 05/14/2023]
Abstract
Heavy atomic nuclei have an excess of neutrons over protons, which leads to the formation of a neutron skin whose thickness is sensitive to details of the nuclear force. This links atomic nuclei to properties of neutron stars, thereby relating objects that differ in size by orders of magnitude. The nucleus 208Pb is of particular interest because it exhibits a simple structure and is experimentally accessible. However, computing such a heavy nucleus has been out of reach for ab initio theory. By combining advances in quantum many-body methods, statistical tools and emulator technology, we make quantitative predictions for the properties of 208Pb starting from nuclear forces that are consistent with symmetries of low-energy quantum chromodynamics. We explore 109 different nuclear force parameterizations via history matching, confront them with data in select light nuclei and arrive at an importance-weighted ensemble of interactions. We accurately reproduce bulk properties of 208Pb and determine the neutron skin thickness, which is smaller and more precise than a recent extraction from parity-violating electron scattering but in agreement with other experimental probes. This work demonstrates how realistic two- and three-nucleon forces act in a heavy nucleus and allows us to make quantitative predictions across the nuclear landscape.
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Affiliation(s)
- Baishan Hu
- TRIUMF, Vancouver, British Columbia Canada
| | - Weiguang Jiang
- Department of Physics, Chalmers University of Technology, Gothenburg, Sweden
| | - Takayuki Miyagi
- TRIUMF, Vancouver, British Columbia Canada
- Department of Physics, Technische Universität Darmstadt, Darmstadt, Germany
- ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt, Germany
| | - Zhonghao Sun
- Department of Physics and Astronomy, University of Tennessee, Knoxville, TN USA
- Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN USA
| | - Andreas Ekström
- Department of Physics, Chalmers University of Technology, Gothenburg, Sweden
| | - Christian Forssén
- Department of Physics, Chalmers University of Technology, Gothenburg, Sweden
| | - Gaute Hagen
- TRIUMF, Vancouver, British Columbia Canada
- Department of Physics and Astronomy, University of Tennessee, Knoxville, TN USA
- Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN USA
| | - Jason D. Holt
- TRIUMF, Vancouver, British Columbia Canada
- Department of Physics, McGill University, Montreal, Quebec Canada
| | - Thomas Papenbrock
- Department of Physics and Astronomy, University of Tennessee, Knoxville, TN USA
- Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN USA
| | - S. Ragnar Stroberg
- Department of Physics, University of Washington, Seattle, WA USA
- Physics Division, Argonne National Laboratory, Lemont, IL USA
| | - Ian Vernon
- Department of Mathematical Sciences, Durham University, Durham, UK
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9
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Dunne M, Mohammadi H, Challenor P, Borgo R, Porphyre T, Vernon I, Firat EE, Turkay C, Torsney-Weir T, Goldstein M, Reeve R, Fang H, Swallow B. Complex model calibration through emulation, a worked example for a stochastic epidemic model. Epidemics 2022; 39:100574. [PMID: 35617882 PMCID: PMC9109972 DOI: 10.1016/j.epidem.2022.100574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 04/22/2022] [Accepted: 04/29/2022] [Indexed: 12/03/2022] Open
Abstract
Uncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertainties inherent in the modelling process of real-world complex systems. The methods are directly applicable to stochastic models in epidemiology, however they have thus far not been widely used in this context. In this paper, we provide a tutorial on uncertainty quantification of stochastic epidemic models, aiming to facilitate the use of the uncertainty quantification paradigm for practitioners with other complex stochastic simulators of applied systems. We provide a formal workflow including the important decisions and considerations that need to be taken, and illustrate the methods over a simple stochastic epidemic model of UK SARS-CoV-2 transmission and patient outcome. We also present new approaches to visualisation of outputs from sensitivity analyses and uncertainty quantification more generally in high input and/or output dimensions.
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Affiliation(s)
- Michael Dunne
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
| | - Hossein Mohammadi
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
| | - Peter Challenor
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
| | - Rita Borgo
- Department of Informatics, King's College London, London, UK
| | - Thibaud Porphyre
- Laboratoire de Biométrie et Biologie Evolutive, VetAgro Sup, Marcy l'Etoile, France
| | - Ian Vernon
- Department of Mathematical Sciences, Durham University, Durham, UK
| | - Elif E Firat
- Department of Computer Science, University of Nottingham, Nottingham, UK
| | - Cagatay Turkay
- Centre for Interdisciplinary Methodologies, University of Warwick, Coventry, UK
| | - Thomas Torsney-Weir
- VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH, Vienna, Austria
| | | | - Richard Reeve
- Boyd Orr Centre for Population and Ecosystem Health, Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Hui Fang
- Department of Computer Science, Loughborough University, Loughborough, UK
| | - Ben Swallow
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK.
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10
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Swallow B, Birrell P, Blake J, Burgman M, Challenor P, Coffeng LE, Dawid P, De Angelis D, Goldstein M, Hemming V, Marion G, McKinley TJ, Overton CE, Panovska-Griffiths J, Pellis L, Probert W, Shea K, Villela D, Vernon I. Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling. Epidemics 2022; 38:100547. [PMID: 35180542 PMCID: PMC7612598 DOI: 10.1016/j.epidem.2022.100547] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 12/22/2021] [Accepted: 02/09/2022] [Indexed: 12/15/2022] Open
Affiliation(s)
- Ben Swallow
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK; Scottish COVID-19 Response Consortium, UK.
| | - Paul Birrell
- Analytics & Data Science, UKHSA, UK; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Joshua Blake
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Mark Burgman
- Centre for Environmental Policy, Imperial College London, London, UK
| | - Peter Challenor
- The Alan Turing Institute, London, UK; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
| | - Luc E Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Philip Dawid
- Statistical Laboratory, University of Cambridge, Cambridge, UK
| | - Daniela De Angelis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK; Joint UNIversities Pandemic and Epidemiological Research, UK
| | - Michael Goldstein
- Department of Mathematical Sciences, Durham University, Stockton Road, Durham, UK
| | - Victoria Hemming
- Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, Canada
| | - Glenn Marion
- Scottish COVID-19 Response Consortium, UK; Biomathematics and Statistics Scotland, Edinburgh, UK
| | - Trevelyan J McKinley
- College of Medicine and Health, University of Exeter, Exeter, UK; Joint UNIversities Pandemic and Epidemiological Research, UK
| | - Christopher E Overton
- Department of Mathematics, University of Manchester, Manchester, UK; Clinical Data Science Unit, Manchester University NHS Foundation Trust, Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK
| | - Jasmina Panovska-Griffiths
- The Big Data Institute, University of Oxford, Oxford, UK; The Queen's College, University of Oxford, Oxford, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; The Alan Turing Institute, London, UK
| | - Will Probert
- The Big Data Institute, University of Oxford, Oxford, UK
| | - Katriona Shea
- Department of Biology and Centre for Infectious Disease Dynamics, The Pennsylvania State University, PA 16802, USA
| | - Daniel Villela
- Program of Scientific Computing, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Ian Vernon
- Department of Mathematical Sciences, Durham University, Stockton Road, Durham, UK
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11
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Aylett-Bullock J, Cuesta-Lazaro C, Quera-Bofarull A, Icaza-Lizaola M, Sedgewick A, Truong H, Curran A, Elliott E, Caulfield T, Fong K, Vernon I, Williams J, Bower R, Krauss F. June: open-source individual-based epidemiology simulation. R Soc Open Sci 2021. [PMID: 34295529 DOI: 10.5281/zenodo.4925939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
We introduce June, an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic indicators. Interactions between individuals are modelled in groups of various sizes and properties, such as households, schools and workplaces, and other social activities using social mixing matrices. June provides a suite of flexible parametrizations that describe infectious diseases, how they are transmitted and affect contaminated individuals. In this paper, we apply June to the specific case of modelling the spread of COVID-19 in England. We discuss the quality of initial model outputs which reproduce reported hospital admission and mortality statistics at national and regional levels as well as by age strata.
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Affiliation(s)
- Joseph Aylett-Bullock
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH1 3LE, UK
| | - Carolina Cuesta-Lazaro
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH1 3LE, UK
| | - Arnau Quera-Bofarull
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH1 3LE, UK
| | - Miguel Icaza-Lizaola
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH1 3LE, UK
| | - Aidan Sedgewick
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Centre for Extragalactic Astronomy, Durham University, Durham DH1 3LE, UK
| | - Henry Truong
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH1 3LE, UK
| | - Aoife Curran
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH1 3LE, UK
| | - Edward Elliott
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH1 3LE, UK
| | - Tristan Caulfield
- Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Kevin Fong
- Department of Science, Technology, Engineering and Public Policy, University College London, London WC1E 6BT, UK
- Department of Anaesthesia, University College London Hospital, London NW1 2BU, UK
| | - Ian Vernon
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Department of Mathematical Sciences, Durham University, Durham DH1 3LE, UK
| | - Julian Williams
- Institute of Hazard, Risk and Resilience, Durham University, Durham DH1 3LE, UK
| | - Richard Bower
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH1 3LE, UK
| | - Frank Krauss
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH1 3LE, UK
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Aylett-Bullock J, Cuesta-Lazaro C, Quera-Bofarull A, Icaza-Lizaola M, Sedgewick A, Truong H, Curran A, Elliott E, Caulfield T, Fong K, Vernon I, Williams J, Bower R, Krauss F. June: open-source individual-based epidemiology simulation. R Soc Open Sci 2021; 8:210506. [PMID: 34295529 PMCID: PMC8261230 DOI: 10.1098/rsos.210506] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 06/22/2021] [Indexed: 05/09/2023]
Abstract
We introduce June, an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic indicators. Interactions between individuals are modelled in groups of various sizes and properties, such as households, schools and workplaces, and other social activities using social mixing matrices. June provides a suite of flexible parametrizations that describe infectious diseases, how they are transmitted and affect contaminated individuals. In this paper, we apply June to the specific case of modelling the spread of COVID-19 in England. We discuss the quality of initial model outputs which reproduce reported hospital admission and mortality statistics at national and regional levels as well as by age strata.
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Affiliation(s)
- Joseph Aylett-Bullock
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH1 3LE, UK
| | - Carolina Cuesta-Lazaro
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH1 3LE, UK
| | - Arnau Quera-Bofarull
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH1 3LE, UK
| | - Miguel Icaza-Lizaola
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH1 3LE, UK
| | - Aidan Sedgewick
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Centre for Extragalactic Astronomy, Durham University, Durham DH1 3LE, UK
| | - Henry Truong
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH1 3LE, UK
| | - Aoife Curran
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH1 3LE, UK
| | - Edward Elliott
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH1 3LE, UK
| | - Tristan Caulfield
- Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Kevin Fong
- Department of Science, Technology, Engineering and Public Policy, University College London, London WC1E 6BT, UK
- Department of Anaesthesia, University College London Hospital, London NW1 2BU, UK
| | - Ian Vernon
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Department of Mathematical Sciences, Durham University, Durham DH1 3LE, UK
| | - Julian Williams
- Institute of Hazard, Risk and Resilience, Durham University, Durham DH1 3LE, UK
| | - Richard Bower
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH1 3LE, UK
| | - Frank Krauss
- Institute for Data Science, Durham University, Durham DH1 3LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH1 3LE, UK
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Jackson SE, Vernon I, Liu J, Lindsey K. Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching. Stat Appl Genet Mol Biol 2020; 19:sagmb-2018-0053. [PMID: 32649296 DOI: 10.1515/sagmb-2018-0053] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 05/12/2020] [Indexed: 11/15/2022]
Abstract
A major challenge in plant developmental biology is to understand how plant growth is coordinated by interacting hormones and genes. To meet this challenge, it is important to not only use experimental data, but also formulate a mathematical model. For the mathematical model to best describe the true biological system, it is necessary to understand the parameter space of the model, along with the links between the model, the parameter space and experimental observations. We develop sequential history matching methodology, using Bayesian emulation, to gain substantial insight into biological model parameter spaces. This is achieved by finding sets of acceptable parameters in accordance with successive sets of physical observations. These methods are then applied to a complex hormonal crosstalk model for Arabidopsis root growth. In this application, we demonstrate how an initial set of 22 observed trends reduce the volume of the set of acceptable inputs to a proportion of 6.1 × 10-7 of the original space. Additional sets of biologically relevant experimental data, each of size 5, reduce the size of this space by a further three and two orders of magnitude respectively. Hence, we provide insight into the constraints placed upon the model structure by, and the biological consequences of, measuring subsets of observations.
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Affiliation(s)
- Samuel E Jackson
- Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, UK
| | - Ian Vernon
- Department of Mathematical Sciences, Durham University, Durham, UK
| | - Junli Liu
- School of Biological and Biomedical Sciences, Durham University, Durham, UK
| | - Keith Lindsey
- School of Biological and Biomedical Sciences, Durham University, Durham, UK
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McCreesh N, Andrianakis I, Nsubuga RN, Strong M, Vernon I, McKinley TJ, Oakley JE, Goldstein M, Hayes R, White RG. Choice of time horizon critical in estimating costs and effects of changes to HIV programmes. PLoS One 2018; 13:e0196480. [PMID: 29768457 PMCID: PMC5955498 DOI: 10.1371/journal.pone.0196480] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 04/13/2018] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Uganda changed its antiretroviral therapy guidelines in 2014, increasing the CD4 threshold for antiretroviral therapy initiation from 350 cells/μl to 500 cells/μl. We investigate what effect this change in policy is likely to have on HIV incidence, morbidity, and programme costs, and estimate the cost-effectiveness of the change over different time horizons. METHODS We used a complex individual-based model of HIV transmission and antiretroviral therapy scale-up in Uganda. 100 model fits were generated by fitting the model to 51 demographic, sexual behaviour, and epidemiological calibration targets, varying 96 input parameters, using history matching with model emulation. An additional 19 cost and disability weight parameters were varied during the analysis of the model results. For each model fit, the model was run to 2030, with and without the change in threshold to 500 cells/μl. RESULTS The change in threshold led to a 9.7% (90% plausible range: 4.3%-15.0%) reduction in incidence in 2030, and averted 278,944 (118,452-502,790) DALYs, at a total cost of $28M (-$142M to +$195M). The cost per disability adjusted life year (DALY) averted fell over time, from $3238 (-$125 to +$29,969) in 2014 to $100 (-$499 to +$785) in 2030. The change in threshold was cost-effective (cost <3×Uganda's per capita GDP per DALY averted) by 2018, and highly cost-effective (cost CONCLUSIONS Model results suggest that the change in threshold is unlikely to have been cost-effective to date, but is likely to be highly cost-effective in Uganda by 2030. The time horizon needs to be chosen carefully when projecting intervention effects. Large amounts of uncertainty in our results demonstrates the need to comprehensively incorporate uncertainties in model parameterisation.
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Affiliation(s)
- Nicky McCreesh
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | | | - Mark Strong
- Sheffield University, Sheffield, United Kingdom
| | - Ian Vernon
- Durham University, Durham, United Kingdom
| | | | | | | | - Richard Hayes
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Richard G. White
- London School of Hygiene and Tropical Medicine, London, United Kingdom
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McKinley TJ, Vernon I, Andrianakis I, McCreesh N, Oakley JE, Nsubuga RN, Goldstein M, White RG. Approximate Bayesian Computation and Simulation-Based Inference for Complex Stochastic Epidemic Models. Stat Sci 2018. [DOI: 10.1214/17-sts618] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Vernon I, Liu J, Goldstein M, Rowe J, Topping J, Lindsey K. Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions. BMC Syst Biol 2018; 12:1. [PMID: 29291750 PMCID: PMC5748965 DOI: 10.1186/s12918-017-0484-3] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 11/09/2017] [Indexed: 11/26/2022]
Abstract
Background Many mathematical models have now been employed across every area of systems biology. These models increasingly involve large numbers of unknown parameters, have complex structure which can result in substantial evaluation time relative to the needs of the analysis, and need to be compared to observed data of various forms. The correct analysis of such models usually requires a global parameter search, over a high dimensional parameter space, that incorporates and respects the most important sources of uncertainty. This can be an extremely difficult task, but it is essential for any meaningful inference or prediction to be made about any biological system. It hence represents a fundamental challenge for the whole of systems biology. Methods Bayesian statistical methodology for the uncertainty analysis of complex models is introduced, which is designed to address the high dimensional global parameter search problem. Bayesian emulators that mimic the systems biology model but which are extremely fast to evaluate are embeded within an iterative history match: an efficient method to search high dimensional spaces within a more formal statistical setting, while incorporating major sources of uncertainty. Results The approach is demonstrated via application to a model of hormonal crosstalk in Arabidopsis root development, which has 32 rate parameters, for which we identify the sets of rate parameter values that lead to acceptable matches between model output and observed trend data. The multiple insights into the model’s structure that this analysis provides are discussed. The methodology is applied to a second related model, and the biological consequences of the resulting comparison, including the evaluation of gene functions, are described. Conclusions Bayesian uncertainty analysis for complex models using both emulators and history matching is shown to be a powerful technique that can greatly aid the study of a large class of systems biology models. It both provides insight into model behaviour and identifies the sets of rate parameters of interest. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0484-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ian Vernon
- Department of Mathematical Sciences, Durham University, South Road, Durham, DH1 3LE, UK.
| | - Junli Liu
- Department of Biosciences, Durham University, South Road, Durham, DH1 3LE, UK.
| | - Michael Goldstein
- Department of Mathematical Sciences, Durham University, South Road, Durham, DH1 3LE, UK
| | - James Rowe
- Department of Biosciences, Durham University, South Road, Durham, DH1 3LE, UK.,Current address: Department of Molecular Biology and Biotechnology, University of Sheffield, Firth Court, Western Bank, Sheffield, S10 2TN, UK
| | - Jen Topping
- Department of Biosciences, Durham University, South Road, Durham, DH1 3LE, UK
| | - Keith Lindsey
- Department of Biosciences, Durham University, South Road, Durham, DH1 3LE, UK
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McCreesh N, Andrianakis I, Nsubuga RN, Strong M, Vernon I, McKinley TJ, Oakley JE, Goldstein M, Hayes R, White RG. Improving ART programme retention and viral suppression are key to maximising impact of treatment as prevention - a modelling study. BMC Infect Dis 2017; 17:557. [PMID: 28793872 PMCID: PMC5550990 DOI: 10.1186/s12879-017-2664-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 08/01/2017] [Indexed: 02/06/2023] Open
Abstract
Background UNAIDS calls for fewer than 500,000 new HIV infections/year by 2020, with treatment-as-prevention being a key part of their strategy for achieving the target. A better understanding of the contribution to transmission of people at different stages of the care pathway can help focus intervention services at populations where they may have the greatest effect. We investigate this using Uganda as a case study. Methods An individual-based HIV/ART model was fitted using history matching. 100 model fits were generated to account for uncertainties in sexual behaviour, HIV epidemiology, and ART coverage up to 2015 in Uganda. A number of different ART scale-up intervention scenarios were simulated between 2016 and 2030. The incidence and proportion of transmission over time from people with primary infection, post-primary ART-naïve infection, and people currently or previously on ART was calculated. Results In all scenarios, the proportion of transmission by ART-naïve people decreases, from 70% (61%–79%) in 2015 to between 23% (15%–40%) and 47% (35%–61%) in 2030. The proportion of transmission by people on ART increases from 7.8% (3.5%–13%) to between 14% (7.0%–24%) and 38% (21%–55%). The proportion of transmission by ART dropouts increases from 22% (15%–33%) to between 31% (23%–43%) and 56% (43%–70%). Conclusions People who are currently or previously on ART are likely to play an increasingly large role in transmission as ART coverage increases in Uganda. Improving retention on ART, and ensuring that people on ART remain virally suppressed, will be key in reducing HIV incidence in Uganda.
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Affiliation(s)
- Nicky McCreesh
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
| | - Ioannis Andrianakis
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | | | - Mark Strong
- School of Health and Related Research, The University of Sheffield, 30 Regent Street, Sheffield, S1 4DA, UK
| | - Ian Vernon
- Department of Mathematical Sciences, Durham University, Lower Mountjoy, Stockton Road, Durham, DH1 3LE, UK
| | - Trevelyan J McKinley
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Penryn Campus, Penryn, TR10 9FE, UK
| | - Jeremy E Oakley
- School of Mathematics and Statistics, University of Sheffield, The Hicks Building, Hounsfield Road, Sheffield, S3 7RH, UK
| | - Michael Goldstein
- Department of Mathematical Sciences, Durham University, Lower Mountjoy, Stockton Road, Durham, DH1 3LE, UK
| | - Richard Hayes
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Richard G White
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
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McCreesh N, Andrianakis I, Nsubuga RN, Strong M, Vernon I, McKinley TJ, Oakley JE, Goldstein M, Hayes R, White RG. Universal test, treat, and keep: improving ART retention is key in cost-effective HIV control in Uganda. BMC Infect Dis 2017; 17:322. [PMID: 28468605 PMCID: PMC5415795 DOI: 10.1186/s12879-017-2420-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 04/25/2017] [Indexed: 12/14/2022] Open
Abstract
Background With ambitious new UNAIDS targets to end AIDS by 2030, and new WHO treatment guidelines, there is increased interest in the best way to scale-up ART coverage. We investigate the cost-effectiveness of various ART scale-up options in Uganda. Methods Individual-based HIV/ART model of Uganda, calibrated using history matching. 22 ART scale-up strategies were simulated from 2016 to 2030, comprising different combinations of six single interventions (1. increased HIV testing rates, 2. no CD4 threshold for ART initiation, 3. improved ART retention, 4. increased ART restart rates, 5. improved linkage to care, 6. improved pre-ART care). The incremental net monetary benefit (NMB) of each intervention was calculated, for a wide range of different willingness/ability to pay (WTP) per DALY averted (health-service perspective, 3% discount rate). Results For all WTP thresholds above $210, interventions including removing the CD4 threshold were likely to be most cost-effective. At a WTP of $715 (1 × per-capita-GDP) interventions to improve linkage to and retention/re-enrolment in HIV care were highly likely to be more cost-effective than interventions to increase rates of HIV testing. At higher WTP (> ~ $1690), the most cost-effective option was ‘Universal Test, Treat, and Keep’ (UTTK), which combines interventions 1–5 detailed above. Conclusions Our results support new WHO guidelines to remove the CD4 threshold for ART initiation in Uganda. With additional resources, this could be supplemented with interventions aimed at improving linkage to and/or retention in HIV care. To achieve the greatest reductions in HIV incidence, a UTTK policy should be implemented. Electronic supplementary material The online version of this article (doi:10.1186/s12879-017-2420-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nicky McCreesh
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
| | - Ioannis Andrianakis
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | | | - Mark Strong
- School of Health and Related Research, The University of Sheffield, 30 Regent Street, Sheffield, S1 4DA, UK
| | - Ian Vernon
- Department of Mathematical Sciences, Durham University, Lower Mountjoy, Stockton Road, Durham, DH1 3LE, UK
| | - Trevelyan J McKinley
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Campusm Penryn, Penryn, TR10 9FE, UK
| | - Jeremy E Oakley
- School of Mathematics and Statistics, University of Sheffield, The Hicks Building, Hounsfield Road, Sheffield, S3 7RH, UK
| | - Michael Goldstein
- Department of Mathematical Sciences, Durham University, Lower Mountjoy, Stockton Road, Durham, DH1 3LE, UK
| | - Richard Hayes
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Richard G White
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
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Andrianakis I, Vernon I, McCreesh N, McKinley TJ, Oakley JE, Nsubuga RN, Goldstein M, White RG. History matching of a complex epidemiological model of human immunodeficiency virus transmission by using variance emulation. J R Stat Soc Ser C Appl Stat 2016; 66:717-740. [PMID: 28781386 PMCID: PMC5516248 DOI: 10.1111/rssc.12198] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a (pre)calibration method that has been applied successfully to complex deterministic models. In this work, we adapt history matching to stochastic models, by emulating the variance in the model outputs, and therefore accounting for its dependence on the model's input values. The method proposed is applied to a real complex epidemiological model of human immunodeficiency virus in Uganda with 22 inputs and 18 outputs, and is found to increase the efficiency of history matching, requiring 70% of the time and 43% fewer simulator evaluations compared with a previous variant of the method. The insight gained into the structure of the human immunodeficiency virus model, and the constraints placed on it, are then discussed.
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Affiliation(s)
| | | | - N McCreesh
- London School of Hygiene and Tropical Medicine UK
| | | | | | - R N Nsubuga
- Medical Research Council Uganda Kampala Uganda
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