Projecting COVID-19 Intensive Care Admissions for Policy Advice, the Netherlands, February 2020 to January 2021

Overview

Journal Euro Surveill

Specialty Infectious Diseases

Date 2024 Mar 8

PMID 38456214

Authors

Don Klinkenberg

Jantien Backer

Nicolette de Keizer

Jacco Wallinga

Affiliations

Soon will be listed here.

Abstract

BackgroundModel 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.AimWe aimed at developing a model on ICU admissions and updating a procedure for informing policymakers.MethodThe 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.ResultsThe 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.ConclusionThe 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.

References

Vos E, van Boven M, Den Hartog G, Backer J, Klinkenberg D, van Hagen C . 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):2318-2321. PMC: 8083720. DOI: 10.1093/cid/ciab264. View

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. PMC: 8165585. DOI: 10.1098/rstb.2020.0279. View

Verelst F, Hermans L, Vercruysse S, Gimma A, Coletti P, Backer J . 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. PMC: 8478607. DOI: 10.1186/s12916-021-02133-y. View

Ferretti L, Wymant C, Kendall M, Zhao L, Nurtay A, Abeler-Dorner L . Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science. 2020; 368(6491). PMC: 7164555. DOI: 10.1126/science.abb6936. View

Backer J, Bogaardt L, Beutels P, Coletti P, Edmunds W, Gimma A . Dynamics of non-household contacts during the COVID-19 pandemic in 2020 and 2021 in the Netherlands. Sci Rep. 2023; 13(1):5166. PMC: 10060924. DOI: 10.1038/s41598-023-32031-7. View

Fountoulakis K, Karakatsoulis G, Abraham S, Adorjan K, Ahmed H, Alarcon R . Results of the COVID-19 mental health international for the general population (COMET-G) study. Eur Neuropsychopharmacol. 2021; 54:21-40. PMC: 8609892. DOI: 10.1016/j.euroneuro.2021.10.004. View

Panaggio M, Rainwater-Lovett K, Nicholas P, Fang M, Bang H, Freeman J . Gecko: A time-series model for COVID-19 hospital admission forecasting. Epidemics. 2022; 39:100580. PMC: 9124631. DOI: 10.1016/j.epidem.2022.100580. View

Vos E, Den Hartog G, Schepp R, Kaaijk P, van Vliet J, Helm K . 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; . PMC: 8142429. DOI: 10.1136/jech-2020-215678. View

Dehning J, Zierenberg J, Spitzner F, Wibral M, Neto J, Wilczek M . Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions. Science. 2020; 369(6500). PMC: 7239331. DOI: 10.1126/science.abb9789. View

10.

Dong E, Du H, Gardner L . An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020; 20(5):533-534. PMC: 7159018. DOI: 10.1016/S1473-3099(20)30120-1. View

11.

Campillo-Funollet E, Van Yperen J, Allman P, Bell M, Beresford W, Clay J . 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):1103-1113. PMC: 8407866. DOI: 10.1093/ije/dyab106. View

12.

Ainslie K, Backer J, de Boer P, van Hoek A, Klinkenberg D, Korthals Altes H . 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). PMC: 9635025. DOI: 10.2807/1560-7917.ES.2022.27.44.2101090. View

13.

Tindale L, Stockdale J, Coombe M, Garlock E, Lau W, Saraswat M . Evidence for transmission of COVID-19 prior to symptom onset. Elife. 2020; 9. PMC: 7386904. DOI: 10.7554/eLife.57149. View

14.

Bekker R, Uit Het Broek M, Koole G . Modeling COVID-19 hospital admissions and occupancy in the Netherlands. Eur J Oper Res. 2022; 304(1):207-218. PMC: 8730382. DOI: 10.1016/j.ejor.2021.12.044. View

15.

Krymova E, Bejar B, Thanou D, Sun T, Manetti E, Lee G . Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide. Proc Natl Acad Sci U S A. 2022; 119(32):e2112656119. PMC: 9371653. DOI: 10.1073/pnas.2112656119. View

16.

Mullah M, Yan P . A semi-parametric mixed model for short-term projection of daily COVID-19 incidence in Canada. Epidemics. 2022; 38:100537. PMC: 8767942. DOI: 10.1016/j.epidem.2022.100537. View

17.

Ganyani T, Kremer C, Chen D, Torneri A, Faes C, Wallinga J . Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020. Euro Surveill. 2020; 25(17). PMC: 7201952. DOI: 10.2807/1560-7917.ES.2020.25.17.2000257. View

18.

Backer J, 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). PMC: 7014672. DOI: 10.2807/1560-7917.ES.2020.25.5.2000062. View

19.

Dongelmans D, Termorshuizen F, Brinkman S, Bakhshi-Raiez F, Arbous M, de Lange D . 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. PMC: 8755895. DOI: 10.1186/s13613-021-00978-3. View

20.

Ward M, Brandsema P, van Straten E, Bosman A . Electronic reporting improves timeliness and completeness of infectious disease notification, The Netherlands, 2003. Euro Surveill. 2017; 10(1):7-8. DOI: 10.2807/esm.10.01.00513-en. View