Key challenges for the surveillance of respiratory viruses: transitioning out of the acute phase of the SARS-CoV-2 pandemic (preprint)

To support the ongoing management of viral respiratory diseases, many countries are moving towards an integrated model of surveillance for SARS-CoV-2, influenza, and other respiratory pathogens. While many surveillance approaches catalysed by the COVID-19 pandemic provide novel epidemiological insight, continuing them as implemented during the pandemic is unlikely to be feasible for non-emergency surveillance, and many have already been scaled back. Furthermore, given anticipated co-circulation of SARS-CoV-2 and influenza, surveillance activities in place prior to the pandemic require review and adjustment to ensure their ongoing value for public health. In this perspective, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies, their contribution to epidemiological assessment, forecasting, and public health decision making.

The role of the mathematical sciences in supporting the COVID-19 response in Australia and New Zealand (preprint)

Mathematical modelling has been used to support the response to the COVID-19 pandemic in countries around the world including Australia and New Zealand. Both these countries have followed similar pandemic response strategies, using a combination of strict border measures and community interventions to minimise infection rates until high vaccine coverage was achieved. This required a different set of modelling tools to those used in countries that experienced much higher levels of prevalence throughout the pandemic. In this article, we provide an overview of some of the mathematical modelling and data analytics work that has helped to inform the policy response to the pandemic in Australia and New Zealand. This is a reflection on our experiences working at the modelling-policy interface and the impact this has had on the pandemic response. We outline the various types of model outputs, from short-term forecasts to longer-term scenario models, that have been used in different contexts. We discuss issues relating to communication between mathematical modellers and stakeholders such as health officials and policymakers. We conclude with some future challenges and opportunities in this area.

Estimating the impact of test-trace-isolate-quarantine systems on SARS-CoV-2 transmission in Australia (preprint)

We report on an analysis of Australian COVID-19 case data to estimate the impact of TTIQ systems on SARS-CoV-2 transmission in 2020-21. We estimate that in a low prevalence period in the state of New South Wales (tens of cases per day), TTIQ contributed to a 54% reduction in transmission. In a higher prevalence period in the state of Victoria (hundreds of cases per day), TTIQ contributed to a 42% reduction in transmission. Our results also suggest that case-initiated contact tracing can support timely quarantine in times of system stress. Contact tracing systems for COVID-19 in Australia were highly effective and adaptable in supporting the national suppression strategy through 2020 and 2021.

COVID-19 vaccine coverage targets to inform reopening plans in a low incidence setting (preprint)

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.

Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020 (preprint)

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.

Understanding the impact of disease and vaccine mechanisms on the importance of optimal vaccine allocation (preprint)

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 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 study the impact of vaccination on a population comprised of individuals at high and low risk of infection. We then compare the outcomes of optimal and suboptimal vaccination strategies for a range of public health objectives using numerical optimisation. Our comparison shows that the difference between vaccinating optimally and suboptimally is dependent on vaccine mechanism, diseases characteristics, and objective considered. Even when sufficient vaccine resources are available, allocation strategy remains important as allocating suboptimally could result in a worse outcome than allocating limited vaccine resources optimally. This work highlights the importance of designing effective vaccine allocation strategies, as allocation of resources can be just as crucial to the success of the overall strategy as total resources available.

Hospital length of stay in a mixed Omicron and Delta epidemic in New South Wales, Australia (preprint)

Aim: To 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). Background: The 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. Results: Using 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.

Estimating the transmissibility of SARS-CoV-2 during periods of high, low and zero case incidence (preprint)

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.

From climate change to pandemics: decision science can help scientists have impact (preprint)

Scientific knowledge and advances are a cornerstone of modern society. They improve our understanding of the world we live in and help us navigate global challenges including emerging infectious diseases, climate change and the biodiversity crisis. For any scientist, whether they work primarily in fundamental knowledge generation or in the applied sciences, it is important to understand how science fits into a decision-making framework. Decision science is a field that aims to pinpoint evidence-based management strategies. It provides a framework for scientists to directly impact decisions or to understand how their work will fit into a decision process. Decision science is more than undertaking targeted and relevant scientific research or providing tools to assist policy makers; it is an approach to problem formulation, bringing together mathematical modelling, stakeholder values and logistical constraints to support decision making. In this paper we describe decision science, its use in different contexts, and highlight current gaps in methodology and application. The COVID-19 pandemic has thrust mathematical models into the public spotlight, but it is one of innumerable examples in which modelling informs decision making. Other examples include models of storm systems (eg. cyclones, hurricanes) and climate change. Although the decision timescale in these examples differs enormously (from hours to decades), the underlying decision science approach is common across all problems. Bridging communication gaps between different groups is one of the greatest challenges for scientists. However, by better understanding and engaging with the decision-making processes, scientists will have greater impact and make stronger contributions to important societal problems.

Key Questions for Modelling COVID-19 Exit Strategies (preprint)

Combinations of intense non-pharmaceutical interventions ('lockdowns') were introduced in countries worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement lockdown exit strategies that allow restrictions to be relaxed while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, will allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. The roadmap requires a global collaborative effort from the scientific community and policy-makers, and is made up of three parts: i) improve estimation of key epidemiological parameters; ii) understand sources of heterogeneity in populations; iii) focus on requirements for data collection, particularly in Low-to-Middle-Income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Early analysis of the Australian COVID-19 epidemic (preprint)

As of 18 April 2020, there had been 6,533 confirmed cases of COVID-19 in Australia. 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.

Modelling the impact of COVID-19 in Australia to inform transmission reducing measures and health system preparedness (preprint)

Background The 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. Methods An 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. Findings An 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. Interpretation Reducing 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.