Nowcasting by Bayesian smoothing: A flexible, generalizable model for real-time epidemic tracking

PLoS Computational Biology

Volumn 16, Issue 4, 2020, Pages

  McGough, Sarah F ^a   Johansson, Michael A ^b   Lipsitch, Marc ^a   Menzies, Nicolas A ^a  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b CENTERS FOR DISEASE CONTROL AND PREVENTION   (Puerto Rico)

Author keywords

[No Author keywords available]

Indexed keywords

AIR NAVIGATION; APPLICATION PROGRAMS; BAYESIAN NETWORKS; ECONOMIC AND SOCIAL EFFECTS; STATISTICAL TESTS;

BAYESIAN; CASE REPORTS; COMMON FEATURES; DELAY DISTRIBUTIONS; INFECTIOUS DISEASE; NOWCAST; NOWCASTING; REAL- TIME; TIME VARYING; TIMES SERIES;

DISEASES;

ARTICLE; BAYES THEOREM; DENGUE; DISEASE SURVEILLANCE; EPIDEMIC; FLU LIKE SYNDROME; FORECASTING; MEASUREMENT ACCURACY; PUBLIC HEALTH; PUBLISHING; PUERTO RICO; SOFTWARE; UNCERTAINTY; UNITED STATES; BIOLOGY; HUMAN; INFLUENZA; PROCEDURES; STATISTICAL MODEL;

BAYES THEOREM; COMPUTATIONAL BIOLOGY; DENGUE; EPIDEMICS; HUMANS; INFLUENZA, HUMAN; MODELS, STATISTICAL; PUERTO RICO; SOFTWARE; UNITED STATES;

EID: 85083620166     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1007735     Document Type: Article

References (23)

1. Lipsitch M, Finelli L, Heffernan RT, Leung GM, Redd SC, 2009 H1n1 Surveillance Group. Improving the evidence base for decision making during a pandemic: the example of 2009 influenza A/H1N1. Biosecur Bioterror. 2011; 9: 89–115. https://doi.org/10.1089/bsp.2011.0007 PMID: 21612363
2. Thacker SB, Berkelman RL, Stroup DF. The Science of Public Health Surveillance. J Public Health Policy. 1989; 10: 187. PMID: 2745711
3. Gikas A, Sotiropoulos A, Panagiotakos D, Peppas T, Skliros E, Pappas S. Prevalence, and associated risk factors, of self-reported diabetes mellitus in a sample of adult urban population in Greece: MEDICAL Exit Poll Research in Salamis (MEDICAL EXPRESS 2002). BMC Public Health. 2004; 4: 2. https://doi.org/10.1186/1471-2458-4-2 PMID: 15028111
4. Kaminsky KS. Prediction of IBNR claim counts by modelling the distribution of report lags. Insur Math Econ. 1987; 6: 151–159.
5. Lawless JF. Adjustments for reporting delays and the prediction of occurred but not reported events. Can J Stat. 1994; 22: 15–31.
6. Pagano M, Tu XM, De Gruttola V, MaWhinney S. Regression Analysis of Censored and Truncated Data: Estimating Reporting- Delay Distributions and AIDS Incidence from Surveillance Data [Internet]. Biometrics. 1994. p. 1203. https://doi.org/10.2307/2533459 PMID: 7787003
7. Comiskey CM, Ruskin HJ. AIDS in Ireland: the reporting delay distribution and the implementation of integral equation models. Comput Appl Biosci. 1992; 8: 579–581. https://doi.org/10.1093/bioinformatics/8.6.579 PMID: 1468015
8. Cui J, Kaldor J. Changing pattern of delays in reporting AIDS diagnoses in Australia. Aust N Z J Public Health. 1998; 22: 432–435. https://doi.org/10.1111/j.1467-842x.1998.tb01409.x PMID: 9659768
9. Höhle M, an der Heiden M. Bayesian nowcasting during the STEC O104:H4 outbreak in Germany, 2011. Biometrics. 2014; 70: 993–1002. https://doi.org/10.1111/biom.12194 PMID: 24930473
10. Salmon M, Schumacher D, Stark K, Höhle M. Bayesian outbreak detection in the presence of reporting delays. Biom J. 2015; 57: 1051–1067. https://doi.org/10.1002/bimj.201400159 PMID: 26250543
11. Lawless JF. Adjustments for reporting delays and the prediction of occurred but not reported events [Internet]. Canadian Journal of Statistics. 1994. pp. 15–31. https://doi.org/10.2307/3315826.n1
12. Kalbfleisch JD, Lawless JF. Regression Models for Right Truncated Data with Applications to AIDS incubation Times and Reporting Lags. Stat Sin. 1991; 1: 19–32.
13. Kremer E. IBNR-claims and the two-way model of ANOVA [Internet]. Scandinavian Actuarial Journal. 1982. pp. 47–55. https://doi.org/10.1080/03461238.1982.10405432
14. Verrall RJ. A state space representation of the chain ladder linear model [Internet]. Journal of the Institute of Actuaries. 1989. pp. 589–609. https://doi.org/10.1017/s0020268100036714
15. Verrrall R. Statistical methods for the chain ladder technique [Internet]. Insurance: Mathematics and Economics. 1994. p. 82. https://doi.org/10.1016/0167-6687(94)90725-0
16. Bastos L, Economou T, Gomes M, Villela D, Bailey T, Codeço C. Modelling reporting delays for disease surveillance data. arXiv:170909150. 2017; Available: https://arxiv.org/abs/1709.09150
17. Noufaily A, Ghebremichael-Weldeselassie Y, Enki DG, Garthwaite P, Andrews N, Charlett A, et al. Modelling reporting delays for outbreak detection in infectious disease data. J R Stat Soc A. 2015; 178: 205–222.
18. Wallinga J, Teunis P. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am J Epidemiol. 2004; 160: 509–516. https://doi.org/10.1093/aje/kwh255 PMID: 15353409
19. Johansson MA, Reich NG, Hota A, Brownstein JS, Santillana M. Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico. Sci Rep. 2016; 6: 33707. https://doi.org/10.1038/srep33707 PMID: 27665707
20. Yang S, Santillana M, Kou SC. Accurate estimation of influenza epidemics using Google search data via ARGO. Proc Natl Acad Sci U S A. 2015; 112: 14473–14478. https://doi.org/10.1073/pnas. 1515373112 PMID: 26553980
21. van de Kassteele J, Eilers P, Wallinga J. Nowcasting the number of new symptomatic cases during infectious disease outbreaks using constrained P-spline smoothing. Epidemiology. 2019;
22. Reich NG, Brooks LC, Fox SJ, Kandula S, McGowan CJ, Moore E, et al. A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States. Proc Natl Acad Sci U S A. 2019; 116: 3146–3154. https://doi.org/10.1073/pnas.1812594116 PMID: 30647115
23. McGowan CJ, Biggerstaff M, Johansson M, Apfeldorf KM, Ben-Nun M, Brooks L, et al. Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016. Sci Rep. 2019; 9: 683. https://doi.org/10.1038/s41598-018-36361-9 PMID: 30679458