Literature DB >> 32383153

Modelling the impact of COVID-19 on intensive care services in New South Wales.

Gregory J Fox1,2, James M Trauer3, Emma McBryde4.   

Abstract

Entities:  

Keywords:  COVID-19; Infectious diseases; Intensive care; Models, statistical; Public health; Respiratory tract infections; Virus diseases

Mesh:

Year:  2020        PMID: 32383153      PMCID: PMC7267514          DOI: 10.5694/mja2.50606

Source DB:  PubMed          Journal:  Med J Aust        ISSN: 0025-729X            Impact factor:   7.738


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Coronavirus disease 2019 (COVID‐19) poses extraordinary challenges for health care in Australia. One of the greatest will be the pressure on hospitals to support people with severe disease. Modelling studies can provide valuable insights into the likely course of the epidemic, and can be particularly useful for anticipating resource requirements, including demand for intensive care services at the peak of the epidemic. In this report, we extrapolate the findings of the Imperial College model of the pandemic1 to the New South Wales population. We also developed a simple SEIR (susceptible–exposed/incubating–infected–removed) model to explore the effect of varying the infection reproduction number (R), which can be reduced by effective social distancing measures, on the timing of the peak of the epidemic. The two models are described in the online Supporting Information. Applying the Imperial College model, the peak demand for intensive care in NSW would be at least 6965 beds if mitigation efforts — isolation of people with confirmed COVID‐19, household quarantine of their contacts, social distancing from people over 70 years of age — are implemented, or almost eight times as many as the baseline number; without mitigation, more than three times as many ICU beds (21 283) could be required (Box 1). Estimated number of ICU beds prior to COVID‐19 epidemic: 874.3 Applying our SEIR model to a scenario without social distancing measures (R = 2.4), the number of people requiring hospitalisation in NSW would peak at 450 per 100 000 population (35 375 beds), and the number requiring critical care at 150 per 100 000 population (11 792 ICU beds, or 1349% of baseline ICU capacity). In this scenario, viral transmission would peak during late June and ICU bed occupancy in early July. About 16% of people would be potentially infectious at this point, although a smaller proportion was modelled as exhibiting symptoms (Box 2; Supporting Information, table 3). * For main curves, 10% case hospitalisation rate assumed; shaded areas show range for hospitalisation rates between 5% and 15%. In a scenario of increased social isolation (R = 1.6) and an assumed hospitalisation rate for people with confirmed COVID‐19 of 6.7%, case numbers would peak in early October and ICU occupancy in mid‐November; about 180 people per 100 000 population would require hospitalisation (14 150 beds) and 65 per 100 000 intensive care (5110 ICU beds, or 585% of baseline ICU capacity) (Box 2; Supporting Information, table 3). That is, the peak figures would be about one‐third the size of those in the no mitigation scenario. Sensitivity analyses in which the proportion of hospitalised patients was varied (5–15%) similarly found that increasing social isolation markedly reduced demand (Supporting Information, table 4). We have used two modelling methods to estimate peak demand for critical care services in NSW during the COVID‐19 epidemic. Both approaches identified that COVID‐19 would impose a major burden on the health care system, and the mismatch between the estimated numbers of ICU beds needed and their availability is stark. Our modelling shows the critical importance of effective COVID‐19 containment strategies, as well as the urgent need to invest in resources that support the surge capacity of critical care services in NSW.

Competing interests

No relevant disclosures. Supplementary methods and results Click here for additional data file.
Mitigation strategy
Population (2016)2 No mitigationClose schools, universitiesCase isolationCase isolation, household quarantineCase isolation, household quarantine, social distancing of people over 70
ICU beds needed per 100 000 population1 27524019012590
ICU beds need, by LHD
Sydney656 460180515761247821591
South Western Sydney964 3422652231418321205868
South Eastern Sydney914 021514219417371143823
Western Sydney948 5842609227718021186854
Northern Sydney914 2332514219417371143823
Illawarra Shoalhaven405 5341115973771507365
Central Coast335 309922805637419302
Other LHDs2 600 79171526242494232512341
All NSW (proportion of baseline bed number)* 7 739 27421 283 (2435%)18 574 (2125%)14 705 (1682%)9674 (1107%)6965 (797%)

Estimated number of ICU beds prior to COVID‐19 epidemic: 874.3

  9 in total

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2.  Royal Flying Doctor Service Coronavirus Disease 2019 Activity and Surge Modeling in Australia.

Authors:  Fergus W Gardiner; Hannah Johns; Lara Bishop; Leonid Churilov
Journal:  Air Med J       Date:  2020-05-16

3.  Forecasting of medical equipment demand and outbreak spreading based on deep long short-term memory network: the COVID-19 pandemic in Turkey.

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Journal:  Signal Image Video Process       Date:  2021-01-25       Impact factor: 1.583

4.  COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease.

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Review 5.  A Review of COVID-19 Modelling Strategies in Three Countries to Develop a Research Framework for Regional Areas.

Authors:  Azizur Rahman; Md Abdul Kuddus; Ryan H L Ip; Michael Bewong
Journal:  Viruses       Date:  2021-10-29       Impact factor: 5.048

6.  The implications of living with COVID-19 for intensive care in Australia.

Authors:  Raymond Raper
Journal:  Med J Aust       Date:  2021-11-11       Impact factor: 12.776

Review 7.  Key lessons from the COVID-19 public health response in Australia.

Authors:  J M Basseal; C M Bennett; P Collignon; B J Currie; D N Durrheim; J Leask; E S McBryde; P McIntyre; F M Russell; D W Smith; T C Sorrell; B J Marais
Journal:  Lancet Reg Health West Pac       Date:  2022-10-10

8.  Dynamic Panel Surveillance of COVID-19 Transmission in the United States to Inform Health Policy: Observational Statistical Study.

Authors:  Lori Ann Post; James Francis Oehmke; Charles B Moss; Lauren Nadya Singh; Theresa Bristol Oehmke
Journal:  J Med Internet Res       Date:  2020-10-05       Impact factor: 7.076

9.  Modelling the impact of relaxing COVID-19 control measures during a period of low viral transmission.

Authors:  Nick Scott; Anna Palmer; Dominic Delport; Romesh Abeysuriya; Robyn M Stuart; Cliff C Kerr; Dina Mistry; Daniel J Klein; Rachel Sacks-Davis; Katie Heath; Samuel W Hainsworth; Alisa Pedrana; Mark Stoove; David Wilson; Margaret E Hellard
Journal:  Med J Aust       Date:  2020-11-18       Impact factor: 12.776

  9 in total

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