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Abstract

Background

Model projections of coronavirus disease 2019 (COVID-19) incidence help policymakers about decisions to implement or lift control measures. During the pandemic, policymakers in the Netherlands were informed on a weekly basis with short-term projections of COVID-19 intensive care unit (ICU) admissions.

Aim

We aimed at developing a model on ICU admissions and updating a procedure for informing policymakers.

Method

The projections were produced using an age-structured transmission model. A consistent, incremental update procedure integrating all new surveillance and hospital data was conducted weekly. First, up-to-date estimates for most parameter values were obtained through re-analysis of all data sources. Then, estimates were made for changes in the age-specific contact rates in response to policy changes. Finally, a piecewise constant transmission rate was estimated by fitting the model to reported daily ICU admissions, with a changepoint analysis guided by Akaike's Information Criterion.

Results

The model and update procedure allowed us to make weekly projections. Most 3-week prediction intervals were accurate in covering the later observed numbers of ICU admissions. When projections were too high in March and August 2020 or too low in November 2020, the estimated effectiveness of the policy changes was adequately adapted in the changepoint analysis based on the natural accumulation of incoming data.

Conclusion

The model incorporates basic epidemiological principles and most model parameters were estimated per data source. Therefore, it had potential to be adapted to a more complex epidemiological situation with the rise of new variants and the start of vaccination.

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/content/10.2807/1560-7917.ES.2024.29.10.2300336
2024-03-07
2024-11-21
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2024.29.10.2300336
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References

  1. Fountoulakis KN, Karakatsoulis G, Abraham S, Adorjan K, Ahmed HU, Alarcón RD, et al. Results of the COVID-19 mental health international for the general population (COMET-G) study. Eur Neuropsychopharmacol. 2022;54:21-40.  https://doi.org/10.1016/j.euroneuro.2021.10.004  PMID: 34758422 
  2. McDonald SA, Lagerweij GR, de Boer P, de Melker HE, Pijnacker R, Mughini Gras L, et al. The estimated disease burden of acute COVID-19 in the Netherlands in 2020, in disability-adjusted life-years. Eur J Epidemiol. 2022;37(10):1035-47.  https://doi.org/10.1007/s10654-022-00895-0  PMID: 35951278 
  3. Ainslie KEC, Backer JA, de Boer PT, van Hoek AJ, Klinkenberg D, Korthals Altes H, et al. A scenario modelling analysis to anticipate the impact of COVID-19 vaccination in adolescents and children on disease outcomes in the Netherlands, summer 2021. Euro Surveill. 2022;27(44):2101090.  https://doi.org/10.2807/1560-7917.ES.2022.27.44.2101090  PMID: 36330824 
  4. Ferretti L, Wymant C, Kendall M, Zhao L, Nurtay A, Abeler-Dörner L, et al. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science. 2020;368(6491):eabb6936.  https://doi.org/10.1126/science.abb6936  PMID: 32234805 
  5. Birrell P, Blake J, van Leeuwen E, Gent N, De Angelis D. Real-time nowcasting and forecasting of COVID-19 dynamics in England: the first wave. Philos Trans R Soc Lond B Biol Sci. 2021;376(1829):20200279.  https://doi.org/10.1098/rstb.2020.0279  PMID: 34053254 
  6. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020;20(5):533-4.  https://doi.org/10.1016/S1473-3099(20)30120-1  PMID: 32087114 
  7. Campillo-Funollet E, Van Yperen J, Allman P, Bell M, Beresford W, Clay J, et al. Predicting and forecasting the impact of local outbreaks of COVID-19: use of SEIR-D quantitative epidemiological modelling for healthcare demand and capacity. Int J Epidemiol. 2021;50(4):1103-13.  https://doi.org/10.1093/ije/dyab106  PMID: 34244764 
  8. Cramer EY, Ray EL, Lopez VK, Bracher J, Brennen A, Castro Rivadeneira AJ, et al. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proc Natl Acad Sci USA. 2022;119(15):e2113561119.  https://doi.org/10.1073/pnas.2113561119  PMID: 35394862 
  9. Dehning J, Zierenberg J, Spitzner FP, Wibral M, Neto JP, Wilczek M, et al. Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions. Science. 2020;369(6500):eabb9789.  https://doi.org/10.1126/science.abb9789  PMID: 32414780 
  10. Krymova E, Béjar B, Thanou D, Sun T, Manetti E, Lee G, et al. Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide. Proc Natl Acad Sci USA. 2022;119(32):e2112656119.  https://doi.org/10.1073/pnas.2112656119  PMID: 35921436 
  11. Mullah MAS, Yan P. A semi-parametric mixed model for short-term projection of daily COVID-19 incidence in Canada. Epidemics. 2022;38:100537.  https://doi.org/10.1016/j.epidem.2022.100537  PMID: 35078118 
  12. Muñoz-Organero M, Queipo-Álvarez P. P. Deep spatiotemporal model for COVID-19 forecasting. Sensors (Basel). 2022;22(9):3519.  https://doi.org/10.3390/s22093519  PMID: 35591208 
  13. Paireau J, Andronico A, Hozé N, Layan M, Crépey P, Roumagnac A, et al. An ensemble model based on early predictors to forecast COVID-19 health care demand in France. Proc Natl Acad Sci USA. 2022;119(18):e2103302119.  https://doi.org/10.1073/pnas.2103302119  PMID: 35476520 
  14. Panaggio MJ, Rainwater-Lovett K, Nicholas PJ, Fang M, Bang H, Freeman J, et al. Gecko: A time-series model for COVID-19 hospital admission forecasting. Epidemics. 2022;39:100580.  https://doi.org/10.1016/j.epidem.2022.100580  PMID: 35636313 
  15. Bekker R, Uit Het Broek M, Koole G. Modeling COVID-19 hospital admissions and occupancy in the Netherlands. Eur J Oper Res. 2023;304(1):207-18.  https://doi.org/10.1016/j.ejor.2021.12.044  PMID: 35013638 
  16. Van Wees J-D, Osinga S, Van der Kuip M, Tanck M, Hanegraaf M, Pluymaekers M, et al. Forecasting hospitalization and ICU rates of the COVID-19 outbreak: an efficient SEIR model2020 7 March 2023. Available from: https://www.researchgate.net/publication/340286949.
  17. Verberk JDM, Vos RA, Mollema L, van Vliet J, van Weert JWM, de Melker HE, et al. Third national biobank for population-based seroprevalence studies in the Netherlands, including the Caribbean Netherlands. BMC Infect Dis. 2019;19(1):470.  https://doi.org/10.1186/s12879-019-4019-y  PMID: 31138148 
  18. Verelst F, Hermans L, Vercruysse S, Gimma A, Coletti P, Backer JA, et al. SOCRATES-CoMix: a platform for timely and open-source contact mixing data during and in between COVID-19 surges and interventions in over 20 European countries. BMC Med. 2021;19(1):254.  https://doi.org/10.1186/s12916-021-02133-y  PMID: 34583683 
  19. Backer JA, Bogaardt L, Beutels P, Coletti P, Edmunds WJ, Gimma A, et al. Dynamics of non-household contacts during the COVID-19 pandemic in 2020 and 2021 in the Netherlands. Sci Rep. 2023;13(1):5166.  https://doi.org/10.1038/s41598-023-32031-7  PMID: 36997550 
  20. Vos ERA, den Hartog G, Schepp RM, Kaaijk P, van Vliet J, Helm K, et al. Nationwide seroprevalence of SARS-CoV-2 and identification of risk factors in the general population of the Netherlands during the first epidemic wave. J Epidemiol Community Health. 2020;75(6):489-95.  https://doi.org/10.1136/jech-2020-215678  PMID: 33249407 
  21. Vos ERA, van Boven M, den Hartog G, Backer JA, Klinkenberg D, van Hagen CCE, et al. Associations between measures of social distancing and severe acute respiratory syndrome coronavirus 2 seropositivity: a nationwide population-based study in the Netherlands. Clin Infect Dis. 2021;73(12):2318-21.  https://doi.org/10.1093/cid/ciab264  PMID: 33772265 
  22. Ward M, Brandsema P, van Straten E, Bosman A. Electronic reporting improves timeliness and completeness of infectious disease notification, The Netherlands, 2003. Euro Surveill. 2005;10(1):7-8.  https://doi.org/10.2807/esm.10.01.00513-en  PMID: 29183539 
  23. Dongelmans DA, Termorshuizen F, Brinkman S, Bakhshi-Raiez F, Arbous MS, de Lange DW, et al. Characteristics and outcome of COVID-19 patients admitted to the ICU: a nationwide cohort study on the comparison between the first and the consecutive upsurges of the second wave of the COVID-19 pandemic in the Netherlands. Ann Intensive Care. 2022;12(1):5.  https://doi.org/10.1186/s13613-021-00978-3  PMID: 35024981 
  24. Diekmann O, Heesterbeek H, Britton T. Mathematical tools for understanding infectious disease dynamics. Princeton: Princeton University Press; 2013. p. 502.
  25. Bergstrom CT, West JD. Calling bullshit: the art of skepticism in a data-driven world. New York: Penguin Random House; 2020. p. 318.
  26. Wikipedia. Fermi Problem: Wikipedia. [Accessed: 27 Feb 2024]. Available from: https://en.wikipedia.org/wiki/Fermi_problem
  27. van de Kassteele J, Van Eijkeren J, Wallinga J. Efficient estimation of age-specific social contact rates between men and women. Ann Appl Stat. 2017;11(1):320-39.  https://doi.org/10.1214/16-AOAS1006 
  28. R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2020. Available from: https://www.R-project.org
  29. Burnham KPA, Anderson DR. Model selection and multimodel inference. A practical information-theoretic approach. New York: Springer; 2002. p. 488.
  30. van Wees J-D, Van Der Kuip M, Osinga S, Van Westerloo D, Tanck M, Hanegraaf M, et al. Performance of progressive and adaptive COVID-19 exit strategies: a stress test analysis for managing intensive care unit rates. medRxiv. 2020 .  https://doi.org/10.1101/2020.05.16.20102947 
  31. Backer JA, Klinkenberg D, Wallinga J. Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020. Euro Surveill. 2020;25(5):2000062.  https://doi.org/10.2807/1560-7917.ES.2020.25.5.2000062  PMID: 32046819 
  32. Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, et al. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Ann Intern Med. 2020;172(9):577-82.  https://doi.org/10.7326/M20-0504  PMID: 32150748 
  33. Ganyani T, Kremer C, Chen D, Torneri A, Faes C, Wallinga J, et al. Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020. Euro Surveill. 2020;25(17):2000257.  https://doi.org/10.2807/1560-7917.ES.2020.25.17.2000257  PMID: 32372755 
  34. Tindale LC, Stockdale JE, Coombe M, Garlock ES, Lau WYV, Saraswat M, et al. Evidence for transmission of COVID-19 prior to symptom onset. eLife. 2020;9:e57149.  https://doi.org/10.7554/eLife.57149  PMID: 32568070 
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