Research Article
Probabilistic forecasting in infectious disease epidemiology: the 13th Armitage lecture
Abstract
Routine surveillance of notifiable infectious diseases gives rise to daily or weekly counts of reported cases stratified by region and age group. From a public health perspective, forecasts of infectious disease spread are of central importance. We argue that such forecasts need to properly incorporate the attached uncertainty, so they should be probabilistic in nature. However, forecasts also need to take into account temporal dependencies inherent to communicable diseases, spatial dynamics through human travel and social contact patterns between age groups. We describe a multivariate time series model for weekly surveillance counts on norovirus gastroenteritis from the 12 city districts of Berlin, in six age groups, from week 2011/27 to week 2015/26. The following year (2015/27 to 2016/26) is used to assess the quality of the predictions. Probabilistic forecasts of the total number of cases can be derived through Monte Carlo simulation, but first and second moments are also available analytically. Final size forecasts as well as multivariate forecasts of the total number of cases by age group, by district and by week are compared across different models of varying complexity. This leads to a more general discussion of issues regarding modelling, prediction and evaluation of public health surveillance data. Copyright © 2017 John Wiley & Sons, Ltd.
Citing Literature
Number of times cited according to CrossRef: 24
- Rebecca Nalule Muhumuza, Olha Bodnar, Joseph Nzabanita, Rebecca N. Nsubuga, Determining Influential Factors in Spatio-temporal Models, Demography of Population Health, Aging and Health Expenditures, 10.1007/978-3-030-44695-6_22, (347-357), (2020).
- Johannes Bracher, Leonhard Held, Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction, International Journal of Forecasting, 10.1016/j.ijforecast.2020.07.002, (2020).
- Roman Řemínek, František Foret, Capillary electrophoretic methods for quality control analyses of pharmaceuticals: A review, ELECTROPHORESIS, 10.1002/elps.202000185, 0, 0, (2020).
- Emily Sara Nightingale, Lloyd A. C. Chapman, Sridhar Srikantiah, Swaminathan Subramanian, Purushothaman Jambulingam, Johannes Bracher, Mary M. Cameron, Graham F. Medley, A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India, PLOS Neglected Tropical Diseases, 10.1371/journal.pntd.0008422, 14, 7, (e0008422), (2020).
- Junyi Lu, Sebastian Meyer, Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model, International Journal of Environmental Research and Public Health, 10.3390/ijerph17041381, 17, 4, (1381), (2020).
- Johannes Bracher, Leonhard Held, A marginal moment matching approach for fitting endemic‐epidemic models to underreported disease surveillance counts, Biometrics, 10.1111/biom.13371, 0, 0, (2020).
- Xanthi Pedeli, Dimitris Karlis, An integer‐valued time series model for multivariate surveillance, Statistics in Medicine, 10.1002/sim.8453, 39, 7, (940-954), (2019).
- Jonathan A. Polonsky, Amrish Baidjoe, Zhian N. Kamvar, Anne Cori, Kara Durski, W. John Edmunds, Rosalind M. Eggo, Sebastian Funk, Laurent Kaiser, Patrick Keating, Olivier le Polain de Waroux, Michael Marks, Paula Moraga, Oliver Morgan, Pierre Nouvellet, Ruwan Ratnayake, Chrissy H. Roberts, Jimmy Whitworth, Thibaut Jombart, Outbreak analytics: a developing data science for informing the response to emerging pathogens, Philosophical Transactions of the Royal Society B: Biological Sciences, 10.1098/rstb.2018.0276, 374, 1776, (20180276), (2019).
- Tao Zhang, Yue Ma, Xiong Xiao, Yun Lin, Xingyu Zhang, Fei Yin, Xiaosong Li, Dynamic Bayesian network in infectious diseases surveillance: a simulation study, Scientific Reports, 10.1038/s41598-019-46737-0, 9, 1, (2019).
- Marwah Soliman, Vyacheslav Lyubchich, Yulia R. Gel, Complementing the power of deep learning with statistical model fusion: Probabilistic forecasting of influenza in Dallas County, Texas, USA, Epidemics, 10.1016/j.epidem.2019.05.004, (100345), (2019).
- Nicholas G. Reich, Craig J. McGowan, Teresa K. Yamana, Abhinav Tushar, Evan L. Ray, Dave Osthus, Sasikiran Kandula, Logan C. Brooks, Willow Crawford-Crudell, Graham Casey Gibson, Evan Moore, Rebecca Silva, Matthew Biggerstaff, Michael A. Johansson, Roni Rosenfeld, Jeffrey Shaman, Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S., PLOS Computational Biology, 10.1371/journal.pcbi.1007486, 15, 11, (e1007486), (2019).
- Michael A. Johansson, Karyn M. Apfeldorf, Scott Dobson, Jason Devita, Anna L. Buczak, Benjamin Baugher, Linda J. Moniz, Thomas Bagley, Steven M. Babin, Erhan Guven, Teresa K. Yamana, Jeffrey Shaman, Terry Moschou, Nick Lothian, Aaron Lane, Grant Osborne, Gao Jiang, Logan C. Brooks, David C. Farrow, Sangwon Hyun, Ryan J. Tibshirani, Roni Rosenfeld, Justin Lessler, Nicholas G. Reich, Derek A. T. Cummings, Stephen A. Lauer, Sean M. Moore, Hannah E. Clapham, Rachel Lowe, Trevor C. Bailey, Markel García-Díez, Marilia Sá Carvalho, Xavier Rodó, Tridip Sardar, Richard Paul, Evan L. Ray, Krzysztof Sakrejda, Alexandria C. Brown, Xi Meng, Osonde Osoba, Raffaele Vardavas, David Manheim, Melinda Moore, Dhananjai M. Rao, Travis C. Porco, Sarah Ackley, Fengchen Liu, Lee Worden, Matteo Convertino, Yang Liu, Abraham Reddy, Eloy Ortiz, Jorge Rivero, Humberto Brito, Alicia Juarrero, Leah R. Johnson, Robert B. Gramacy, Jeremy M. Cohen, Erin A. Mordecai, Courtney C. Murdock, Jason R. Rohr, Sadie J. Ryan, Anna M. Stewart-Ibarra, Daniel P. Weikel, Antarpreet Jutla, Rakibul Khan, Marissa Poultney, Rita R. Colwell, Brenda Rivera-García, Christopher M. Barker, Jesse E. Bell, Matthew Biggerstaff, David Swerdlow, Luis Mier-y-Teran-Romero, Brett M. Forshey, Juli Trtanj, Jason Asher, Matt Clay, Harold S. Margolis, Andrew M. Hebbeler, Dylan George, Jean-Paul Chretien, An open challenge to advance probabilistic forecasting for dengue epidemics, Proceedings of the National Academy of Sciences, 10.1073/pnas.1909865116, (201909865), (2019).
- Johannes Bracher, On the multibin logarithmic score used in the FluSight competitions, Proceedings of the National Academy of Sciences, 10.1073/pnas.1912147116, (201912147), (2019).
- Sarah C. Kramer, Jeffrey Shaman, Development and validation of influenza forecasting for 64 temperate and tropical countries, PLOS Computational Biology, 10.1371/journal.pcbi.1006742, 15, 2, (e1006742), (2019).
- Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind M. Eggo, W. John Edmunds, Assessing the performance of real-time epidemic forecasts: A case study of Ebola in the Western Area region of Sierra Leone, 2014-15, PLOS Computational Biology, 10.1371/journal.pcbi.1006785, 15, 2, (e1006785), (2019).
- Benjamin D. Dalziel, Stephen Kissler, Julia R. Gog, Cecile Viboud, Ottar N. Bjørnstad, C. Jessica E. Metcalf, Bryan T. Grenfell, Urbanization and humidity shape the intensity of influenza epidemics in U.S. cities, Science, 10.1126/science.aat6030, 362, 6410, (75-79), (2018).
- Jacco Wallinga, Metropolitan versus small-town influenza, Science, 10.1126/science.aav1003, 362, 6410, (29-30), (2018).
- Yirong Chen, Collins Wenhan Chu, Mark I.C. Chen, Alex R. Cook, The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison, Journal of Biomedical Informatics, 10.1016/j.jbi.2018.02.014, 81, (16-30), (2018).
- Theresa Stocks, Tom Britton, Michael Höhle, Model selection and parameter estimation for dynamic epidemic models via iterated filtering: application to rotavirus in Germany, Biostatistics, 10.1093/biostatistics/kxy057, (2018).
- Ibrahim Musa, Hyun Park, Lkhagvadorj Munkhdalai, Keun Ryu, Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization, Sustainability, 10.3390/su10103414, 10, 10, (3414), (2018).
- M. U. G. Kraemer, D. A. T. Cummings, S. Funk, R. C. Reiner, N. R. Faria, O. G. Pybus, S. Cauchemez, Reconstruction and prediction of viral disease epidemics, Epidemiology and Infection, 10.1017/S0950268818002881, (1-7), (2018).
- Fabian Krüger, Sebastian Lerch, Thordis Thorarinsdottir, Tilmann Gneiting, Predictive Inference Based on Markov Chain Monte Carlo Output, International Statistical Review, 10.1111/insr.12405, 0, 0, (undefined).
- Alberto Martini, Davide Azzolini, Barbara Romano, Loris Vergolini, Increasing College Going by Incentivizing Savings: Evidence from a Randomized Controlled Trial in Italy, Journal of Policy Analysis and Management, 10.1002/pam.22260, 0, 0, (undefined).
- Jianqi Sun, Caohua He, Xiangming Yao, Anqi Song, Yaogang Li, Qinghong Zhang, Chengyi Hou, Qiuwei Shi, Hongzhi Wang, Hierarchical Composite‐Solid‐Electrolyte with High Electrochemical Stability and Interfacial Regulation for Boosting Ultra‐Stable Lithium Batteries, Advanced Functional Materials, 10.1002/adfm.202006381, 0, 0, (undefined).
