key: cord-0804654-35i0mhz4 authors: Fox, Gregory J; Trauer, James M; McBryde, Emma title: Modelling the impact of COVID‐19 on intensive care services in New South Wales date: 2020-05-08 journal: Med J Aust DOI: 10.5694/mja2.50606 sha: 271627f474f7d7826771f707a36d1b89ad1d77b0 doc_id: 804654 cord_uid: 35i0mhz4 nan A modelling group at Imperial College London, a WHO Collaborating Centre for Infectious Disease Modelling, has modelled the effect of different mitigation policies upon peak healthcare demand. 1 The Imperial College model adopted a number of assumptions regarding the natural history and clinical management of the COVID-19 epidemic. We applied the outcomes of the Imperial College model to the population of NSW, accounting for local demographic distribution. 2 The age distribution between the two settings is similar, shown in Figure 1 We developed a simple SEIR-type compartmental model (susceptible (S), exposed/incubation period (E), infectious (I) and removed(R)) ( Figure 2 ). The standard model is modified to allow for pre-symptomatic transmission during the incubation period (E 2 ), a delay between the onset of symptoms and presentation to healthcare (I 1 ), diagnosed disease (I 2 ), hospitalization (H), and ICU admission (ICU). In this model, compartments E 2 , I 1 and I 2 are infectious. The force of infection is therefore given by: Where: Infectious period = 1 2 + 1 1 + 1 2 We also performed a simple SEIR (susceptible-exposed/incubating -infected-removed) model in order to explore the effect of varying the basic reproduction number (R 0 ) which may be reduced by effective social distancing measures and subsequently is called the effective reproduction number (R eff ). The modelled outcome was hospitalised cases, and ICU cases, per 100,000 population. We modelled two scenarios: (a) no intervention, with a R 0 of 2.4, and (b) social isolation policies, leading to a R eff of 1.6, both with a start prevalence on 1 March 2020 of 2 persons per million. Detailed model parameters are included in Table 1 . We conducted a partial rank correlation coefficient study of nine key model parameters; Reproduction number, probability of hospitalisation, duration of hospitalisation, probability of being admitted to ICU given hospitalisation, duration of ICU admission, time in E 1 , time in E 2 , time in I 1 , time in I 2 against four key outcomes; peak hospitalisation numbers, peak ICU numbers, time to peak hospitalisation and time to peak ICU as shown in Figure 3 . Our approach has several limitations. Modelling studies depend upon the assumptions upon which t hey are based, and parameters including the current reproduction number remain uncertain as the epidemic is still unfolding. The trajectory of the epidemic, and the magnitude of peak ICU demand will be highly dependent upon the effectiveness of mitigation strategies. The present report does not estimate the effect of more intensive suppression strategies, which would be likely to reduce the peak ICU requirement. Despite the usual limitations inherent in modelling studies, such studies have an important role in informing contingency planning, where applicable parameters are available. Further modelling is needed to inform resource planning for the COVID-19 epidemic in Australia, including for critical care services. Such models will help to inform the public debate regarding the timing, intensity and duration of mitigation strategies. This modelling study did not enrol participants, and so ethics review was not warranted. It is evident that the size of both peaks (C, D) are highly sensitive to the reproduction number (as expected) and also highly sensitive to the time spent in the hospital/ICU states of the model. The time to peak is negatively correlated with the reproduction number (as reflected in figure 1 ) and also to the length of the stages of infection, particularly E2. As of 29 March 2020, the case notification rate is lower in NSW (22.8 cases per 100,000) compared with the UK (26.2 cases per 100,000 cases). 8,9 Figure 1 Table 2 shows the estimated cumulative hospitalisations, ICU admissions and deaths in one Local Health District (Sydney LHD) under an optimal mitigation scenario comprising case isolation, household quarantine and social distancing of over 70 year-olds. The timing and magnitude of the peak demand will be strongly dependent upon the effectiveness of mitigation strategies. Ongoing surveillance of transmission in the community will be essential to allow healthcare services to anticipate the effects of national COVID-19 mitigation policies upon healthcare resource requirements. Imperial College COVID-19 Response Team. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand Population by local health district Principal projection: UK population in age groups A picture of health: Sydney Local Health District health profile 2015 Temporal variation in transmission during the COVID-19 outbreak Australia 2020 Estimates of the severity of COVID-19 disease Total UK cases: COVID-19 COVID-19 (coronavirus)