Probabilistic forecasting in infectious disease epidemiology: the 13th Armitage lecture

Statistics in Medicine - Tập 36 Số 22 - Trang 3443-3460 - 2017
Leonhard Held1, Sebastian Meyer1,2, Johannes Bracher1
1Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich 8001, Switzerland.
2Institute of Medical Informatics, Biometry, and Epidemiology; Friedrich-Alexander-Universität Erlangen-Nürnberg; Erlangen 91054 Germany

Tóm tắt

Từ khóa


Tài liệu tham khảo

Keeling, 2008, Modeling Infectious Diseases in Humans and Animals, 10.1515/9781400841035

World Health Organization, 2014, Anticipating epidemics, Weekly Epidemiological Record, 89, 244

World Health Organization (ed.), 2016, Anticipating Emerging Infectious Disease Epidemics: Meeting Report of WHO Informal Consultation

Centers for Disease Control and Prevention Flu activity forecasting website launched 2016 https://www.cdc.gov/flu/ news/flu-forecast-website-launched.htm

Hufnagel, 2004, Forecast and control of epidemics in a globalized world, Proceedings of the National Academy of Sciences of the United States of America, 101, 15 124, 10.1073/pnas.0308344101

Birrell, 2011, Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London, Proceedings of the National Academy of Sciences of the United States of America, 108, 18 238, 10.1073/pnas.1103002108

Chretien, 2014, Influenza forecasting in human populations: a scoping review, PLOS ONE, 9, e94 130, 10.1371/journal.pone.0094130

Nsoesie, 2013, PLOS Currents Outbreaks, 1

Santillana, 2015, Combining search, social media, and traditional data sources to improve influenza surveillance, PLOS Computational Biology, 11, e1004 513, 10.1371/journal.pcbi.1004513

Dukic, 2012, Tracking epidemics with Google Flu Trends data and a state-space SEIR model, Journal of the American Statistical Association, 107, 1410, 10.1080/01621459.2012.713876

Yang, 2015, Accurate estimation of influenza epidemics using Google search data via ARGO, Proceedings of the National Academy of Sciences of the United States of America, 112, 14 473, 10.1073/pnas.1515373112

Xia, 2004, Measles metapopulation dynamics: a gravity model for epidemiological coupling and dynamics, The American Naturalist, 164, 267, 10.1086/422341

Meyer, 2014, Power-law models for infectious disease spread, The Annals of Applied Statistics, 8, 1612, 10.1214/14-AOAS743

Riley, 2015, Five challenges for spatial epidemic models, Epidemics, 10, 68, 10.1016/j.epidem.2014.07.001

Höhle, 2016, Handbook of Spatial Epidemiology, 477

Baguelin, 2013, Assessing optimal target populations for influenza vaccination programmes: an evidence synthesis and modelling study, PLOS Medicine, 10, e1001 527, 10.1371/journal.pmed.1001527

Meyer, 2017, Incorporating social contact data in spatio-temporal models for infectious disease spread, Biostatistics, 18, 338

Gneiting, 2014, Probabilistic forecasting, Annual Review of Statistics and Its Application, 1, 125, 10.1146/annurev-statistics-062713-085831

Moran, 2016, Epidemic forecasting is messier than weather forecasting: the role of human behavior and internet data streams in epidemic forecast, Journal of Infectious Diseases, 214, S404, 10.1093/infdis/jiw375

Gneiting, 2007, Strictly proper scoring rules, prediction, and estimation, Journal of the American Statistical Association, 102, 359, 10.1198/016214506000001437

Czado, 2009, Predictive model assessment for count data, Biometrics, 65, 1254, 10.1111/j.1541-0420.2009.01191.x

Seillier-Moiseiwitsch, 1992, Prequential tests of model fit, Scandinavian Journal of Statistics, 19, 45

Seillier-Moiseiwitsch, 1993, On testing the validity of sequential probability forecasts, Journal of the American Statistical Association, 88, 355, 10.1080/01621459.1993.10594328

Wei, 2014, Calibration tests for count data, Test, 23, 787, 10.1007/s11749-014-0380-8

Gneiting, 2008, Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds, Test, 17, 211, 10.1007/s11749-008-0114-x

Scheuerer, 2015, Variogram-based proper scoring rules for probabilistic forecasts of multivariate quantities, Monthly Weather Review, 143, 1321, 10.1175/MWR-D-14-00269.1

Wei, 2017, Calibration tests for multivariate Gaussian forecasts, Journal of Multivariate Analysis, 154, 216, 10.1016/j.jmva.2016.11.005

Held, 2005, A statistical framework for the analysis of multivariate infectious disease surveillance counts, Statistical Modelling, 5, 187, 10.1191/1471082X05st098oa

Meyer, 2017, Spatio-temporal analysis of epidemic phenomena using the R package surveillance, Journal of Statistical Software, 77, 1, 10.18637/jss.v077.i11

Pringle, 2015, Noroviruses: epidemiology, immunity and prospects for prevention, Future Microbiology, 10, 53, 10.2217/fmb.14.102

Ahmed, 2013, A systematic review and meta-analysis of the global seasonality of norovirus, PLOS ONE, 8, e75 922, 10.1371/journal.pone.0075922

Bernard, 2014, Epidemiology of norovirus gastroenteritis in Germany 2001-2009: eight seasons of routine surveillance, Epidemiology & Infection, 142, 63, 10.1017/S0950268813000435

Paul, 2008, Multivariate modelling of infectious disease surveillance data, Statistics in Medicine, 27, 6250, 10.1002/sim.3440

Held, 2012, Modeling seasonality in space-time infectious disease surveillance data, Biometrical Journal, 54, 824, 10.1002/bimj.201200037

Brockmann, 2006, The scaling laws of human travel, Nature, 439, 462, 10.1038/nature04292

Cliff, 1975, Model building and the analysis of spatial pattern in human geography, Journal of the Royal Statistical Society Series B (Methodological), 37, 297, 10.1111/j.2517-6161.1975.tb01548.x

Mossong, 2008, Social contacts and mixing patterns relevant to the spread of infectious diseases, PLoS Medicine, 5, e74, 10.1371/journal.pmed.0050074

Wallinga, 2006, Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents, American Journal of Epidemiology, 164, 936, 10.1093/aje/kwj317

Bland, 1986, Statistical methods for assessing agreement between two methods of clinical measurement, The Lancet, 327, 307, 10.1016/S0140-6736(86)90837-8

Good, 1952, Rational decisions, Journal of the Royal Statistical Society. Series B (Methodological), 14, 107, 10.1111/j.2517-6161.1952.tb00104.x

Dawid, 1999, Coherent dispersion criteria for optimal experimental design, Annals of Statistics, 27, 65, 10.1214/aos/1018031101

Riebler, 2017, Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested Laplace approximations, Biometrical Journal, 59, 531, 10.1002/bimj.201500263

Diebold, 1998, Evaluating density forecasts with applications to financial risk management, International Economic Review, 39, 863, 10.2307/2527342

Diebold, 1995, Comparing predictive accuracy, Journal of Business & Economic Statistics, 13, 253, 10.1080/07350015.1995.10524599

Krüger F Lerch S Thorarinsdottir TL Gneiting T Probabilistic forecasting and comparative model assessment based on Markov chain Monte Carlo output 2017 https://arxiv.org/abs/1608.06802

Hemri, 2015, Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51, 7436, 10.1002/2014WR016473

Claeskens, 2008, Model Selection and Model Averaging, 10.1017/CBO9780511790485

Sloughter, 2010, Probabilistic wind speed forecasting using ensembles and Bayesian model averaging, Journal of the American Statistical Association, 105, 25, 10.1198/jasa.2009.ap08615