ABSTRACT
Since the emergence of SARS-CoV-2 in 2019 through to mid-2021, much of the Australian population lived in a COVID-19 free environment. This followed the broadly successful implementation of a strong suppression strategy, including international border closures. With the availability of COVID-19 vaccines in early 2021, the national government sought to transition from a state of minimal incidence and strong suppression activities to one of high vaccine coverage and reduced restrictions but with still-manageable transmission. This transition is articulated in the national "re-opening" plan released in July 2021. Here we report on the dynamic modelling study that directly informed policies within the national re-opening plan including the identification of priority age groups for vaccination, target vaccine coverage thresholds and the anticipated requirements for continued public health measures -- assuming circulation of the Delta SARS-CoV-2 variant. Our findings demonstrated that adult vaccine coverage needed to be at least 70% to minimise public health and clinical impacts following the establishment of community transmission. They also supported the need for continued application of test-trace-isolate-quarantine and social measures during the vaccine roll-out phase and beyond.
ABSTRACT
As of January 2021, Australia had effectively controlled local transmission of COVID-19 despite a steady influx of imported cases and several local, but contained, outbreaks in 2020. Throughout 2020, state and territory public health responses were informed by weekly situational reports that included an ensemble forecast for each jurisdiction. We present here an analysis of one forecasting model included in this ensemble across the variety of scenarios experienced by each jurisdiction from May to October 2020. We examine how successfully the forecasts characterised future case incidence, subject to variations in data timeliness and completeness, showcase how we adapted these forecasts to support decisions of public health priority in rapidly-evolving situations, evaluate the impact of key model features on forecast skill, and demonstrate how to assess forecast skill in real-time before the ground truth is known. Conditioning the model on the most recent, but incomplete, data improved the forecast skill, emphasising the importance of developing strong quantitative models of surveillance system characteristics, such as ascertainment delay distributions. Forecast skill was highest when there were at least 10 reported cases per day, the circumstances in which authorities were most in need of forecasts to aid in planning and response.
ABSTRACT
Vaccination is an important epidemic intervention strategy. Resource limitations and an imperative for efficient use of public resources drives a need for optimal allocation of vaccines within a population. For a disease causing severe illness in particular members of a population, an effective strategy to reduce illness might be to vaccinate those vulnerable with a vaccine that reduces the chance of catching a disease. However, it is not clear that this is the best strategy, and it is generally unclear how the difference between various vaccine strategies changes depending on population characteristics, vaccine mechanisms and allocation objective. In this paper we develop a conceptual mathematical model to consider strategies for vaccine allocation, prior to the establishment of community transmission. By extending the SEIR model to incorporate a range of vaccine mechanisms and disease characteristics, we simulate the impact of vaccination on a population with two sub-groups of differing characteristics. We then compare the outcomes of optimal and suboptimal vaccination strategies for a range of public health objectives using numerical optimisation. Our comparison serves to demonstrate that the difference between vaccinating optimally and suboptimally may be dependent on vaccine mechanism, diseases characteristics, and objective considered. We find that better resources do not guarantee better outcomes. Allocating optimally with lesser vaccine resources can produce a better outcome than allocating good vaccine resources suboptimally, dependent on vaccine mechanisms, disease characteristics and objective considered. Through a principled model-based process, this work highlights the importance of designing effective vaccine allocation strategies. This design process requires models that incorporate known biological characteristics, realistic parameters based on data analysis, etc. Overall, we see that allocation of resources can be just as crucial to the success of a vaccination strategy as the strength of resources available.
ABSTRACT
AimTo estimate the length of stay distributions of hospitalised COVID-19 cases during a mixed Omicron-Delta epidemic in New South Wales, Australia (16 Dec 2021 - 7 Feb 2022), and compare these to estimates produced over a Delta-only epidemic in the same population (1 Jul 2021 - 15 Dec 2022). BackgroundThe distribution of the duration that clinical cases of COVID-19 occupy hospital beds (the length of stay) is a key factor in determining how incident caseloads translate into health system burden as measured through ward and ICU occupancy. ResultsUsing data on the hospital stays of 19,574 individuals, we performed a competing-risk survival analysis of COVID-19 clinical progression. During the mixed Omicron-Delta epidemic, we found that the mean length of stay for individuals who were discharged directly from ward without an ICU stay was, for age groups 0-39, 40-69 and 70+ respectively, 2.16 (95% CI: 2.12-2.21), 3.93 (95% CI: 3.78-4.07) and 7.61 days (95% CI: 7.31-8.01), compared to 3.60 (95% CI: 3.48-3.81), 5.78 (95% CI: 5.59-5.99) and 12.31 days (95% CI: 11.75-12.95) across the preceding Delta epidemic (15 Jul 2021 - 15 Dec 2021). We also considered data on the stays of individuals within the Hunter New England Local Health District, where it was reported that Omicron was the only circulating variant, and found mean ward-to-discharge length of stays of 2.05 (95% CI: 1.80-2.30), 2.92 (95% CI: 2.50-3.67) and 6.02 days (95% CI: 4.91-7.01) for the same age groups. ConclusionsHospital length of stay was substantially reduced across all clinical pathways during a mixed Omicron-Delta epidemic compared to a prior Delta epidemic. These changes in length of stay have contributed to lessened health system burden despite greatly increased infection burden and should be considered in future planning of response to the COVID-19 pandemic in Australia and internationally.
ABSTRACT
Against a backdrop of widespread global transmission, a number of countries have successfully brought large outbreaks of COVID-19 under control and maintained near-elimination status. A key element of epidemic response is the tracking of disease transmissibility in near real-time. During major outbreaks, the reproduction rate can be estimated from a time-series of case, hospitalisation or death counts. In low or zero incidence settings, knowing the potential for the virus to spread is a response priority. Absence of case data means that this potential cannot be estimated directly. We present a semi-mechanistic modelling framework that draws on time-series of both behavioural data and case data (when disease activity is present) to estimate the transmissibility of SARS-CoV-2 from periods of high to low - or zero - case incidence, with a coherent transition in interpretation across the changing epidemiological situations. Of note, during periods of epidemic activity, our analysis recovers the effective reproduction number, while during periods of low - or zero - case incidence, it provides an estimate of transmission risk. This enables tracking and planning of progress towards the control of large outbreaks, maintenance of virus suppression, and monitoring the risk posed by re-introduction of the virus. We demonstrate the value of our methods by reporting on their use throughout 2020 in Australia, where they have become a central component of the national COVID-19 response.
ABSTRACT
As of 18 April 2020, there had been 6,533 confirmed cases of COVID-19 in Australia [1]. Of these, 67 had died from the disease. The daily count of new confirmed cases was declining. This suggests that the collective actions of the Australian public and government authorities in response to COVID-19 were sufficiently early and assiduous to avert a public health crisis -- for now. Analysing factors, such as the intensity and timing public health interventions, that contribute to individual country experiences of COVID-19 will assist in the next stage of response planning globally. Using data from the Australian national COVID-19 database, we describe how the epidemic and public health response unfolded in Australia up to 13 April 2020. We estimate that the effective reproduction number was likely below 1 (the threshold value for control) in each Australian state since mid-March and forecast that hospital ward and intensive care unit occupancy will remain below capacity thresholds over the next two weeks.
ABSTRACT
BackgroundThe ability of global health systems to cope with increasing numbers of COVID-19 cases is of major concern. In readiness for this challenge, Australia has drawn on clinical pathway models developed over many years in preparation for influenza pandemics. These models have been used to estimate health care requirements for COVID-19 patients, in the context of broader public health measures. MethodsAn age and risk stratified transmission model of COVID-19 infection was used to simulate an unmitigated epidemic with parameter ranges reflecting uncertainty in current estimates of transmissibility and severity. Overlaid public health measures included case isolation and quarantine of contacts, and broadly applied social distancing. Clinical presentations and patient flows through the Australian health care system were simulated, including expansion of available intensive care capacity and alternative clinical assessment pathways. FindingsAn unmitigated COVID-19 epidemic would dramatically exceed the capacity of the Australian health system, over a prolonged period. Case isolation and contact quarantine alone will be insufficient to constrain case presentations within a feasible level of expansion of health sector capacity. Overlaid social restrictions will need to be applied at some level over the course of the epidemic to ensure that systems do not become overwhelmed, and that essential health sector functions, including care of COVID-19 patients, can be maintained. Attention to the full pathway of clinical care is needed to ensure access to critical care. InterpretationReducing COVID-19 morbidity and mortality will rely on a combination of measures to strengthen and extend public health and clinical capacity, along with reduction of overall infection transmission in the community. Ongoing attention to maintaining and strengthening the capacity of health care systems and workers to manage cases is needed. FundingAustralian Government Department of Health Office of Health Protection, Australian Government National Health and Medical Research Council
