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1. Quantifying model evidence for yellow fever transmission routes in Africa

Author

Katy A. M. Gaythorpe, Laurence Cibrelus, Tini Garske, Kévin Jean, Imperial College London, Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers [CNAM] (CNAM), Pasteur-Cnam Risques infectieux et émergents (PACRI), Institut Pasteur [Paris]-Conservatoire National des Arts et Métiers [CNAM] (CNAM), World Health Organisation (WHO), Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), Funding:This work was carried out as part of the Vaccine Impact Modelling Consortium (www.vaccineimpact.org),but the views expressed are those of the authors and not necessarily those of the Consortium or its funders.The funders were given the opportunity to review this paper prior to publication,but the final decision on the content of the publication was taken by the authors.We acknowledge joint Centre funding from the UK Medical Research Council and Department for International Development. The research leading to these results has received funding from the Bill & Melinda Gates foundation (OPP1117543,OPP1157270 http://www.gatesfoundation.org/)and from the Medical Research Council (MR/R015600/1https://mrc.ukri.org/)., HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), and Institut Pasteur [Paris] (IP)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)

Subjects
0301 basic medicine, Viral Diseases, Epidemiology, Force of infection, Statics, Pathology and Laboratory Medicine, law.invention, Geographical Locations, Bayes' theorem, 0302 clinical medicine, law, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Aedes, Statistics, Medicine and Health Sciences, Public and Occupational Health, Biology (General), Ecology, Physics, Yellow fever, Linear model, Classical Mechanics, Vaccination and Immunization, Geography, Transmission (mechanics), Infectious Diseases, Serology, Computational Theory and Mathematics, INFECTIONS, Modeling and Simulation, Physical Sciences, Yellow fever virus, Life Sciences & Biomedicine, Research Article, Generalized linear model, Biochemistry & Molecular Biology, Bioinformatics, QH301-705.5, Immunology, Bayesian inference, Models, Biological, Biochemical Research Methods, BAYESIAN MODEL, Infectious Disease Epidemiology, 03 medical and health sciences, Cellular and Molecular Neuroscience, Yellow Fever, Genetics, medicine, Animals, Humans, Molecular Biology, 01 Mathematical Sciences, Ecology, Evolution, Behavior and Systematics, Estimation, Science & Technology, Models, Statistical, Biology and Life Sciences, Computational Biology, Bayes Theorem, 06 Biological Sciences, medicine.disease, 030104 developmental biology, People and Places, Africa, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Mathematical & Computational Biology, 08 Information and Computing Sciences, Preventive Medicine, 030217 neurology & neurosurgery
Abstract

Yellow fever is a vector-borne disease endemic in tropical regions of Africa, where 90% of the global burden occurs, and Latin America. It is notoriously under-reported with uncertainty arising from a complex transmission cycle including a sylvatic reservoir and non-specific symptom set. Resulting estimates of burden, particularly in Africa, are highly uncertain. We examine two established models of yellow fever transmission within a Bayesian model averaging framework in order to assess the relative evidence for each model’s assumptions and to highlight possible data gaps. Our models assume contrasting scenarios of the yellow fever transmission cycle in Africa. The first takes the force of infection in each province to be static across the observation period; this is synonymous with a constant infection pressure from the sylvatic reservoir. The second model assumes the majority of transmission results from the urban cycle; in this case, the force of infection is dynamic and defined through a fixed value of R0 in each province. Both models are coupled to a generalised linear model of yellow fever occurrence which uses environmental covariates to allow us to estimate transmission intensity in areas where data is sparse. We compare these contrasting descriptions of transmission through a Bayesian framework and trans-dimensional Markov chain Monte Carlo sampling in order to assess each model’s evidence given the range of uncertainty in parameter values. The resulting estimates allow us to produce Bayesian model averaged predictions of yellow fever burden across the African endemic region. We find strong support for the static force of infection model which suggests a higher proportion of yellow fever transmission occurs as a result of infection from an external source such as the sylvatic reservoir. However, the model comparison highlights key data gaps in serological surveys across the African endemic region. As such, conclusions concerning the most prevalent transmission routes for yellow fever will be limited by the sparsity of data which is particularly evident in the areas with highest predicted transmission intensity. Our model and estimation approach provides a robust framework for model comparison and predicting yellow fever burden in Africa. However, key data gaps increase uncertainty surrounding estimates of model parameters and evidence. As more mathematical models are developed to address new research questions, it is increasingly important to compare them with established modelling approaches to highlight uncertainty in structures and data., Author summary Yellow fever (YF) is notoriously under reported due to non-specific symptom spectrum and the true burden is highly uncertain as a result of a complex transmission cycle. As such, estimates surrounding YF burden are highly uncertain and the mechanisms behind transmission are often unclear. We assess these mechanisms and the resulting uncertainty by estimating two existing models of YF transmission within a product space framework. This allows us to produce updated estimates of transmission intensity and to compare the relative support for each model given the data. We find strong support for a model assuming a static force of infection, approximating the constant infection pressure from the sylvatic reservoir of YF. We also highlight areas where data is sparse, often the same areas estimated to have especially high transmission intensity. This is the first robust multi-model approach to applied YF modelling and provides a framework that could be extended to other disease models.

Published
2019

2. Effects of waveform model systematics on the interpretation of GW150914

Author

Ligo, The Scientific Collaboration, the Virgo Collaboration, Abbott, B. P., Abbott, R., Abbott, T. D., Abernathy, M. R., Acernese, F., Ackley, K., Adams, C., Adams, T., Addesso, P., Adhikari, R. X., Adya, V. B., Affeldt, C., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Allen, B., Allocca, A., Altin, P. A., Ananyeva, A., Anderson, S. B., Anderson, W. G., Appert, S., Arai, K., Araya, M. C., Areeda, J. S., Arnaud, N., Arun, K. G., Ascenzi, S., Ashton, G., Ast, M., Aston, S. M., Astone, P., Aufmuth, P., Aulbert, C., Avila-Alvarez, A., Babak, S., Bacon, P., Bader, M. K. M., Baker, P. T., Baldaccini, F., Ballardin, G., Ballmer, S. W., Barayoga, J. C., Barclay, S. E., Barish, B. C., Barker, D., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Bartos, I., Bassiri, R., Basti, A., Batch, J. C., Baune, C., Bavigadda, V., Bazzan, M., Beer, C., Bejger, M., Belahcene, I., Belgin, M., Bell, A. S., Berger, B. K., Bergmann, G., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhagwat, S., Bhandare, R., Bilenko, I. A., Billingsley, G., Billman, C. R., Birch, J., Birney, R., Birnholtz, O., Biscans, S., Bisht, A., Bitossi, M., Biwer, C., Bizouard, M. A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Bloemen, S., Bock, O., Boer, M., Bogaert, G., Bohe, A., Bondu, F., Bonnand, R., Boom, B. A., Bork, R., Boschi, V., Bose, S., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Braginsky, V. B., Branchesi, M., Brau, J. E., Briant, T., Brillet, A., Brinkmann, M., Brisson, V., Brockill, P., Broida, J. E., Brooks, A. F., Brown, D. A., Brown, D. D., Brown, N. M., Brunett, S., Buchanan, C. C., Buikema, A., Bulik, T., Bulten, H. J., Buonanno, A., Buskulic, D., Buy, C., Byer, R. L., Cabero, M., Cadonati, L., Cagnoli, G., Cahillane, C., Calder On Bustillo, J., Callister, T. A., Calloni, E., Camp, J. B., Cannon, K. C., Cao, H., Cao, J., Capano, C. D., Capocasa, E., Carbognani, F., Caride, S., Casanueva Diaz, J., Casentini, C., Caudill, S., Cavagli A, M., Cavalier, F., Cavalieri, R., Cella, G., Cepeda, C. B., Cerboni Baiardi, L., Cerretani, G., Cesarini, E., Chamberlin, S. J., Chan, M., Chao, S., Charlton, P., Chassande-Mottin, E., Cheeseboro, B. D., Chen, H. Y., Chen, Y., Cheng, H. -P, Chincarini, A., Chiummo, A., Chmiel, T., Cho, H. S., Cho, M., Chow, J. H., Christensen, N., Chu, Q., Chua, A. J. K., Chua, S., Chung, S., Ciani, G., Clara, F., Clark, J. A., Cleva, F., Cocchieri, C., Coccia, E., Cohadon, P. -F, Colla, A., Collette, C. G., Cominsky, L., Constancio, M., Conti, L., Cooper, S. J., Corbitt, T. R., Cornish, N., Corsi, A., Cortese, S., Costa, C. A., Coughlin, M. W., Coughlin, S. B., Coulon, J. -P, Countryman, S. T., Couvares, P., Covas, P. B., Cowan, E. E., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Creighton, J. D. E., Creighton, T. D., Cripe, J., Crowder, S. G., Cullen, T. J., Cumming, A., Cunningham, L., Cuoco, E., Dal Canton, T., Danilishin, S. L., D Antonio, S., Danzmann, K., Dasgupta, A., Da Silva Costa, C. F., Dattilo, V., Dave, I., Davier, M., Davies, G. S., Davis, D., Daw, E. J., Day, B., Day, R., De, S., Debra, D., Debreczeni, G., Degallaix, J., Laurentis, M., Del Eglise, S., Del Pozzo, W., Denker, T., Dent, T., Dergachev, V., Rosa, R., Derosa, R. T., Desalvo, R., Devenson, J., Devine, R. C., Dhurandhar, S., D Iaz, M. C., Di Fiore, L., Di Giovanni, M., Di Girolamo, T., Di Lieto, A., Di Pace, S., Di Palma, I., Di Virgilio, A., Doctor, Z., Dolique, V., Donovan, F., Dooley, K. L., Doravari, S., Dorrington, I., Douglas, R., Dovale Alvarez, M., Downes, T. P., Drago, M., Drever, R. W. P., Driggers, J. C., Du, Z., Ducrot, M., Dwyer, S. E., Edo, T. B., Edwards, M. C., Effler, A., Eggenstein, H. -B, Ehrens, P., Eichholz, J., Eikenberry, S. S., Eisenstein, R. A., Essick, R. C., Etienne, Z., Etzel, T., Evans, M., Evans, T. M., Everett, R., Factourovich, M., Fafone, V., Fair, H., Fairhurst, S., Fan, X., Farinon, S., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favata, M., Fays, M., Fehrmann, H., Fejer, M. M., Fern Andez Galiana, A., Ferrante, I., Ferreira, E. C., Ferrini, F., Fidecaro, F., Fiori, I., Fiorucci, D., Fisher, R. P., Flaminio, R., Fletcher, M., Fong, H., Forsyth, S. S., Fournier, J. -D, Frasca, S., Frasconi, F., Frei, Z., Freise, A., Frey, R., Frey, V., Fries, E. M., Fritschel, P., Frolov, V. V., Fulda, P., Fyffe, M., Gabbard, H., Gadre, B. U., Gaebel, S. M., Gair, J. R., Gammaitoni, L., Gaonkar, S. G., Garufi, F., Gaur, G., Gayathri, V., Gehrels, N., Gemme, G., Genin, E., Gennai, A., George, J., Gergely, L., Germain, V., Ghonge, S., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, S., Giaime, J. A., Giardina, K. D., Giazotto, A., Gill, K., Glaefke, A., Goetz, E., Goetz, R., Gondan, L., Gonz Alez, G., Gonzalez Castro, J. M., Gopakumar, A., Gorodetsky, M. L., Gossan, S. E., Gosselin, M., Gouaty, R., Grado, A., Graef, C., Granata, M., Grant, A., Gras, S., Gray, C., Greco, G., Green, A. C., Paul Groot, Grote, H., Grunewald, S., Guidi, G. M., Guo, X., Gupta, A., Gupta, M. K., Gushwa, K. E., Gustafson, E. K., Gustafson, R., Hacker, J. J., Hall, B. R., Hall, E. D., Hammond, G., Haney, M., Hanke, M. M., Hanks, J., Hanna, C., Hannam, M. D., Hanson, J., Hardwick, T., Harms, J., Harry, G. M., Harry, I. W., Hart, M. J., Hartman, M. T., Haster, C. -J, Haughian, K., Healy, J., Heidmann, A., Heintze, M. C., Heitmann, H., Hello, P., Hemming, G., Hendry, M., Heng, I. S., Hennig, J., Henry, J., Heptonstall, A. W., Heurs, M., Hild, S., Hoak, D., Hofman, D., Holt, K., Holz, D. E., Hopkins, P., Hough, J., Houston, E. A., Howell, E. J., Hu, Y. M., Huerta, E. A., Huet, D., Hughey, B., Husa, S., Huttner, S. H., Huynh-Dinh, T., Indik, N., Ingram, D. R., Inta, R., Isa, H. N., Isac, J. -M, Isi, M., Isogai, T., Iyer, B. R., Izumi, K., Jacqmin, T., Jani, K., Jaranowski, P., Jawahar, S., Jim Enez-Forteza, F., Johnson, W. W., Jones, D. I., Jones, R., Jonker, R. J. G., Ju, L., Junker, J., Kalaghatgi, C. V., Kalogera, V., Kandhasamy, S., Kang, G., Kanner, J. B., Karki, S., Karvinen, K. S., Kasprzack, M., Katsavounidis, E., Katzman, W., Kaufer, S., Kaur, T., Kawabe, K., K Ef Elian, F., Keitel, D., Kelley, D. B., Kennedy, R., Key, J. S., Khalili, F. Y., Khan, I., Khan, S., Khan, Z., Khazanov, E. A., Kijbunchoo, N., Kim, Chunglee, Kim, J. C., Kim, Whansun, Kim, W., Kim, Y. -M, Kimbrell, S. J., King, E. J., King, P. J., Kirchhoff, R., Kissel, J. S., Klein, B., Kleybolte, L., Klimenko, S., Koch, P., Koehlenbeck, S. M., Koley, S., Kondrashov, V., Kontos, A., Korobko, M., Korth, W. Z., Kowalska, I., Kozak, D. B., Kr&quot, Amer, C., Kringel, V., Krishnan, B., Kr Olak, A., Kuehn, G., Kumar, P., Kumar, R., Kuo, L., Kutynia, A., Lackey, B. D., Landry, M., Lang, R. N., Lange, J., Lantz, B., Lanza, R. K., Lartaux-Vollard, A., Lasky, P. D., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., Lebigot, E. O., Lee, C. H., Lee, H. K., Lee, H. M., Lee, K., Lehmann, J., Lenon, A., Leonardi, M., Leong, J. R., Leroy, N., Letendre, N., Levin, Y., Li, T. G. F., Libson, A., Littenberg, T. B., Liu, J., Lockerbie, N. A., Lombardi, A. L., London, L. T., Lord, J. E., Lorenzini, M., Loriette, V., Lormand, M., Losurdo, G., Lough, J. D., Lovelace, G., Quot, L., Uck, H., Lundgren, A. P., Lynch, R., Ma, Y., Macfoy, S., Machenschalk, B., Macinnis, M., Macleod, D. M., Magana-Sandoval, F., Majorana, E., Maksimovic, I., Malvezzi, V., Man, N., Mandic, V., Mangano, V., Mansell, G. L., Manske, M., Mantovani, M., Marchesoni, F., Marion, F., M Arka, S., M Arka, Z., Markosyan, A. S., Maros, E., Martelli, F., Martellini, L., Martin, I. W., Martynov, D. V., Mason, K., Masserot, A., Massinger, T. J., Masso-Reid, M., Mastrogiovanni, S., Matichard, F., Matone, L., Mavalvala, N., Mazumder, N., Mccarthy, R., Mcclelland, D. E., Mccormick, S., Mcgrath, C., Mcguire, S. C., Mcintyre, G., Mciver, J., Mcmanus, D. J., Mcrae, T., Mcwilliams, S. T., Meacher, D., Meadors, G. D., Meidam, J., Melatos, A., Mendell, G., Mendoza-Gandara, D., Mercer, R. A., Merilh, E. L., Merzougui, M., Meshkov, S., Messenger, C., Messick, C., Metzdorff, R., Meyers, P. M., Mezzani, F., Miao, H., Michel, C., Middleton, H., Mikhailov, E. E., Milano, L., Miller, A. L., Miller, A., Miller, B. B., Miller, J., Millhouse, M., Minenkov, Y., Ming, J., Mirshekari, S., Mishra, C., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Moggi, A., Mohan, M., Mohapatra, S. R. P., Montani, M., Moore, B. C., Moore, C. J., Moraru, D., Moreno, G., Morriss, S. R., Mours, B., Mow-Lowry, C. M., Mueller, G., Muir, A. W., Mukherjee, Arunava, Mukherjee, D., Mukherjee, S., Mukund, N., Mullavey, A., Munch, J., Muniz, E. A. 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J., Sorazu, B., Sorrentino, F., Souradeep, T., Spencer, A. P., Srivastava, A. K., Staley, A., Steinke, M., Steinlechner, J., Steinlechner, S., Steinmeyer, D., Stephens, B. C., Stevenson, S. P., Stone, R., Strain, K. A., Straniero, N., Stratta, G., Strigin, S. E., Sturani, R., Stuver, A. L., Summerscales, T. Z., Sun, L., Sunil, S., Sutton, P. J., Swinkels, B. L., Szczepa Nczyk, M. J., Tacca, M., Talukder, D., Tanner, D. B., T Apai, M., Taracchini, A., Taylor, R., Theeg, T., Thomas, E. G., Thomas, M., Thomas, P., Thorne, K. A., Thrane, E., Tippens, T., Tiwari, S., Tiwari, V., Tokmakov, K. V., Toland, K., Tomlinson, C., Tonelli, M., Tornasi, Z., Torrie, C. I., Quot, T., oyr&quot, A, D., Travasso, F., Traylor, G., Trifir O, D., Trinastic, J., Tringali, M. C., Trozzo, L., Tse, M., Tso, R., Turconi, M., Tuyenbayev, D., Ugolini, D., Unnikrishnan, C. S., Urban, A. L., Usman, S. A., Vahlbruch, H., Vajente, G., Valdes, G., Bakel, N., Beuzekom, M., Den Brand, J. F. 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J., Yu, Hang, Yu, Haocun, Yvert, M., Zadro Zny, A., Zangrando, L., Zanolin, M., Zendri, J. -P, Zevin, M., Zhang, L., Zhang, M., Zhang, T., Zhang, Y., Zhao, C., Zhou, M., Zhou, Z., Zhu, S. J., Zhu, X. J., Zucker, M. E., Zweizig, J., Boyle, M., Chu, T., Hemberger, D., Hinder, I., Kidder, L. E., Ossokine, S., Scheel, M., Szilagyi, B., Teukolsky, S., Vano-Vinuales, A., AstroParticule et Cosmologie (APC (UMR_7164)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Astrophysique Relativiste Théories Expériences Métrologie Instrumentation Signaux (ARTEMIS), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Institut de Physique de Rennes (IPR), Université de Rennes (UR)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Kastler Brossel (LKB (Lhomond)), Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Kastler Brossel (LKB (Jussieu)), Laboratoire des matériaux avancés (LMA), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL), Laboratoire de l'Accélérateur Linéaire (LAL), Université Paris-Sud - Paris 11 (UP11)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), See acknowledgements of paper, LIGO, VIRGO, Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Centre National de la Recherche Scientifique (CNRS)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Sud - Paris 11 (UP11), AstroParticule et Cosmologie ( APC - UMR 7164 ), Centre National de la Recherche Scientifique ( CNRS ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Observatoire de Paris-Université Paris Diderot - Paris 7 ( UPD7 ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ), Astrophysique Relativiste Théories Expériences Métrologie Instrumentation Signaux ( ARTEMIS ), Université Nice Sophia Antipolis ( UNS ), Université Côte d'Azur ( UCA ) -Université Côte d'Azur ( UCA ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Observatoire de la Côte d'Azur, Université Côte d'Azur ( UCA ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Physique de Rennes ( IPR ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Kastler Brossel ( LKB (Lhomond) ), Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris ( FRDPENS ), Centre National de la Recherche Scientifique ( CNRS ) -École normale supérieure - Paris ( ENS Paris ) -Centre National de la Recherche Scientifique ( CNRS ) -École normale supérieure - Paris ( ENS Paris ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Kastler Brossel ( LKB (Jussieu) ), Laboratoire des matériaux avancés ( LMA ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Centre National de la Recherche Scientifique ( CNRS ) -Centre National de la Recherche Scientifique ( CNRS ), Ecole Supérieure de Physique et de Chimie Industrielles ( ESPCI ), Mairie de Paris, Laboratoire de l'Accélérateur Linéaire ( LAL ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire d'Annecy de Physique des Particules ( LAPP/Laboratoire d'Annecy-le-Vieux de Physique des Particules ), Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Université Savoie Mont Blanc ( USMB [Université de Savoie] [Université de Chambéry] ) -Centre National de la Recherche Scientifique ( CNRS ), Abbott, B. 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M, Harms, J, Martelli, F, Milano, Leopoldo, Montani, M, Piergiovanni, F, Stratta, G, Vetrano, F, Viceré, A, CALTECH, Louisiana State Univ, Amer Univ, Univ Salerno, Ist Nazl Fis Nucl, Univ Florida, LIGO Livingston Observ, Univ Savoie Mt Blanc, Univ Sannio Benevento, Max Planck Inst Gravitat Phys, Nikhef, MIT, Inst Nacl Pesquisas Espaciais, Interuniv Ctr Astron & Astrophys, Tata Inst Fundamental Res, Univ Wisconsin Milwaukee, Leibniz Univ Hannover, Univ Pisa, Australian Natl Univ, Calif State Univ Fullerton, Univ Paris Saclay, Chennai Math Inst, Univ Roma Tor Vergata, Univ Hamburg, Max Planck Gravitat Phys, Universidade de São Paulo (USP), West Virginia Univ, Univ Perugia, EGO, Syracuse Univ, Univ Glasgow, LIGO Hanford Observ, RMKI, Columbia Univ, Stanford Univ, Univ Padua, Polish Acad Sci, Georgia Inst Technol, Univ Birmingham, Univ Genoa, RRCAT, Lomonosov Moscow State Univ, Univ West Scotland, Caltech CaRT, Univ Western Australia, Radboud Univ Nijmegen, Univ Cote dAzur, Univ Rennes, Washington State Univ, Univ Urbino Carlo Bo, Univ Oregon, ENS PSL Res Univ, Carleton Coll, Warsaw Univ, Vrije Univ Amsterdam, Univ Maryland, CNRS IN2P3, Univ Claude Bernard Lyon 1, Univ Napoli Federico II, NASA, Univ Tokyo, Univ Adelaide, Tsinghua Univ, Texas Tech Univ, Univ Mississippi, Penn State Univ, Natl Tsing Hua Univ, Charles Sturt Univ, Univ Chicago, Kenyon Coll, Korea Inst Sci & Technol Informat, Univ Cambridge, Univ Roma La Sapienza, Univ Brussels, Sonoma State Univ, Montana State Univ, Northwestern Univ, Univ Illes Balears, Univ Texas Rio Grande Valley, Bellevue Coll, Inst Plasma Res, Univ Sheffield, Calif State Univ Los Angeles, Univ Trento, Cardiff Univ, Montclair State Univ, Natl Astron Observ Japan, Univ Toronto, MTA Eotvos Univ, Univ Edinburgh, Univ & Inst Adv Res, IISER TVM, Univ Szeged, Embry Riddle Aeronaut Univ, Osserv Astron Capodimonte, Univ Michigan, Rochester Inst Technol, Univ Illinois, Univ Bialystok, Univ Strathclyde, Univ Southampton, Univ Washington Bothell, Inst Appl Phys, Seoul Natl Univ, Inje Univ Gimhae, Natl Inst Math Sci, Pusan Natl Univ, NCBJ, Monash Univ, Hanyang Univ, Chinese Univ Hong Kong, Univ Alabama Huntsville, CNRS, Univ Minnesota, Univ Camerino, Southern Univ, A&M Coll, Univ Melbourne, Coll William & Mary, Universidade Estadual Paulista (Unesp), Whitman Coll, Univ Lyon, Hobart & William Smith Coll, Univ Zielona Gora, Univ London, IISER Kolkata, Indian Inst Technol, Andrews Univ, Univ Siena, Trinity Univ, Univ Washington, Abilene Christian Univ, Cornell Univ, Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Observatoire de Paris, PSL Research University (PSL)-PSL Research University (PSL)-Université Paris Diderot - Paris 7 (UPD7), Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Ecole Supérieure de Physique et de Chimie Industrielles (ESPCI), Laboratoire d'Annecy de Physique des Particules (LAPP/Laboratoire d'Annecy-le-Vieux de Physique des Particules), The LIGO Scientific Collaboration, and The Virgo Collaboration

Subjects
[PHYS.ASTR.HE]Physics [physics]/Astrophysics [astro-ph]/High Energy Astrophysical Phenomena [astro-ph.HE], Physics and Astronomy (miscellaneous), Astronomy, Bayesian probability, FOS: Physical sciences, GW150914, Binary number, Orbital eccentricity, General Relativity and Quantum Cosmology (gr-qc), Parameter space, Bayesian inference, 01 natural sciences, General Relativity and Quantum Cosmology, GRAVITATIONAL-WAVES, [ PHYS.GRQC ] Physics [physics]/General Relativity and Quantum Cosmology [gr-qc], BINARIES, waveform models, Binary black hole, gravitational waves, numerical relativity, parameter estimation, systematic errors, 0103 physical sciences, Waveform, Statistical physics, 010306 general physics, QC, QB, High Energy Astrophysical Phenomena (astro-ph.HE), Physics, INITIAL DATA, 010308 nuclear & particles physics, [ PHYS.ASTR.HE ] Physics [physics]/Astrophysics [astro-ph]/High Energy Astrophysical Phenomena [astro-ph.HE], Gravitational waves, GW150914, parameter estimation, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, [PHYS.GRQC]Physics [physics]/General Relativity and Quantum Cosmology [gr-qc], RADIATION, Astrophysics - High Energy Astrophysical Phenomena, Phenomenology (particle physics), Gravitational wave
Abstract

Parameter estimates of GW150914 were obtained using Bayesian inference, based on three semi-analytic waveform models for binary black hole coalescences. These waveform models differ from each other in their treatment of black hole spins, and all three models make some simplifying assumptions, notably to neglect sub-dominant waveform harmonic modes and orbital eccentricity. Furthermore, while the models are calibrated to agree with waveforms obtained by full numerical solutions of Einstein's equations, any such calibration is accurate only to some non-zero tolerance and is limited by the accuracy of the underlying phenomenology, availability, quality, and parameter-space coverage of numerical simulations. This paper complements the original analyses of GW150914 with an investigation of the effects of possible systematic errors in the waveform models on estimates of its source parameters. To test for systematic errors we repeat the original Bayesian analyses on mock signals from numerical simulations of a series of binary configurations with parameters similar to those found for GW150914. Overall, we find no evidence for a systematic bias relative to the statistical error of the original parameter recovery of GW150914 due to modeling approximations or modeling inaccuracies. However, parameter biases are found to occur for some configurations disfavored by the data of GW150914: for binaries inclined edge-on to the detector over a small range of choices of polarization angles, and also for eccentricities greater than $\sim$0.05. For signals with higher signal-to-noise ratio than GW150914, or in other regions of the binary parameter space (lower masses, larger mass ratios, or higher spins), we expect that systematic errors in current waveform models may impact gravitational-wave measurements, making more accurate models desirable for future observations., 29 pages, 9 figures

Published
2017

3. Subjective Contingencies and Limited Bayesian Updating

Author

Stefania Minardi, Andrei Savochkin, Haldemann, Antoine, Groupement de Recherche et d'Etudes en Gestion à HEC (GREGH), Ecole des Hautes Etudes Commerciales (HEC Paris)-Centre National de la Recherche Scientifique (CNRS), Collegio Carlo Alberto, Università degli studi di Torino = University of Turin (UNITO), and HEC Research Paper Series

Subjects
Economics and Econometrics, Computer science, media_common.quotation_subject, Bayesian probability, Bayesian inference, Separable space, Revealed preference, 0502 economics and business, State space, Relevance (information retrieval), 050207 economics, Representation (mathematics), Set (psychology), 050205 econometrics, media_common, business.industry, Welfare economics, 05 social sciences, Representation (systemics), JEL: D - Microeconomics/D.D8 - Information, Knowledge, and Uncertainty/D.D8.D81 - Criteria for Decision-Making under Risk and Uncertainty, understanding of uncertainty, subjective state space, non-Bayesian updating, Confirmation bias, [SHS.GESTION]Humanities and Social Sciences/Business administration, Artificial intelligence, business, [SHS.GESTION] Humanities and Social Sciences/Business administration
Abstract

We depart from Savage's (1954) common state space assumption and introduce a model that allows for a subjective understanding of uncertainty. Within the revealed preference paradigm, we take the analyst's perspective and uniquely identify the agent's subjective state space via her preferences conditional on incoming information. According to our representation, the agent's subjective contingencies correspond to sets of the analyst's states and, as such, are coarse. The agent uses an additively separable utility with respect to her set of contingencies; and she adopts an updating rule that follows the Bayesian spirit but is limited by her perception of uncertainty. We illustrate the relevance of our theory with applications to the confirmatory bias and correlation neglect, as well as to optimal contract design.

Published
2017