Coronavirus Disease Model to Inform Transmission-Reducing Measures and Health System Preparedness, Australia

Impact of a general medicine consultant-led ward round in the emergency department

BACKGROUND

Patients requiring admission to the general medicine wards in a public hospital are usually assessed by a medical registrar. This study is based at a metropolitan public hospital in Melbourne where the majority of general medicine patients in the emergency department (ED) are not seen by a consultant physician until they are transferred to the ward.

AIMS

To assess the impact of general medicine consultant-led ward rounds (CWRs) in the ED on patient length of stay (LOS).

METHODS

One-month audit was conducted of all patients admitted to general medicine and awaiting transfer to ward from ED at a metropolitan public hospital in Melbourne. A general medicine CWR was then implemented in the ED, followed by another 1-month audit, with the primary outcome being LOS and the secondary outcome being 30-day readmission rate. Additionally, admitting medical registrars were invited to complete a survey before and after the implementation of CWRs to assess satisfaction rate.

RESULTS

Data from electronic medical records were analysed for 162 patients (90 preimplementation group and 72 postimplementation group). The median LOS was 6 days in the preimplementation group and 4 days in the postimplementation group (P = 0.014). There was no significant difference in 30-day readmission rates. Surveys showed admitting medical registrars reported a reduced level of stress and fewer barriers to seeking consultant input following implementation.

CONCLUSIONS

A CWR in the ED has led to decreased LOS for general medicine patients and improved satisfaction among junior medical staff.

SARS-CoV-2 Delta and Omicron community transmission networks as added value to contact tracing

OBJECTIVES

Calculations of SARS-CoV-2 transmission networks at a population level have been limited. Networks that estimate infections between individuals and whether this results in a mutation, can be a way to evaluate fitness of a mutational clone by how much it expands in number as well as determining the likelihood a transmission results in a new variant.

METHODS

Australian Delta and Omicron SARS-CoV-2 sequences were downloaded from GISAID. Transmission networks of infection between individuals were estimated using a novel mathematical method.

RESULTS

Many of the sequences were identical, with clone sizes following power law distributions driven by negative binomial probability distributions for both the number of infections per individual and the number of mutations per transmission (median 0.74 nucleotide changes for Delta and 0.71 for Omicron). Using these distributions, an agent-based model was able to replicate the observed clonal network structure, providing a basis for more detailed COVID-19 modelling. Possible recombination events, tracked by insertion/deletion (indel) patterns, were identified for each variant in these outbreaks.

CONCLUSIONS

This modelling approach reveals key transmission characteristics of SARS-CoV-2 and may complement traditional contact tracing. This methodology can also be applied to other diseases as genetic sequencing of viruses becomes more commonplace.

Predictions from standard epidemiological models of consequences of segregating and isolating vulnerable people into care facilities

OBJECTIVES

Since the declaration of the COVID-19 pandemic, many governments have imposed policies to reduce contacts between people who are presumed to be particularly vulnerable to dying from respiratory illnesses and the rest of the population. These policies typically address vulnerable individuals concentrated in centralized care facilities and entail limiting social contacts with visitors, staff members, and other care home residents. We use a standard epidemiological model to investigate the impact of such circumstances on the predicted infectious disease attack rates, for interacting robust and vulnerable populations.

METHODS

We implement a general susceptible-infectious-recovered (SIR) compartmental model with two populations: robust and vulnerable. The key model parameters are the per-individual frequencies of within-group (robust-robust and vulnerable-vulnerable) and between-group (robust-vulnerable and vulnerable-robust) infectious-susceptible contacts and the recovery times of individuals in the two groups, which can be significantly longer for vulnerable people.

RESULTS

Across a large range of possible model parameters including degrees of segregation versus intermingling of vulnerable and robust individuals, we find that concentrating the most vulnerable into centralized care facilities virtually always increases the infectious disease attack rate in the vulnerable group, without significant benefit to the robust group.

CONCLUSIONS

Isolated care homes of vulnerable residents are predicted to be the worst possible mixing circumstances for reducing harm in epidemic or pandemic conditions.

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

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 60% to minimize 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

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 of daily COVID-19 cases 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.

Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19

BACKGROUND

During the early stages of the COVID-19 pandemic, there was considerable uncertainty surrounding epidemiological and clinical aspects of SARS-CoV-2. Governments around the world, starting from varying levels of pandemic preparedness, needed to make decisions about how to respond to SARS-CoV-2 with only limited information about transmission rates, disease severity and the likely effectiveness of public health interventions. In the face of such uncertainties, formal approaches to quantifying the value of information can help decision makers to prioritise research efforts.

METHODS

In this study we use Value of Information (VoI) analysis to quantify the likely benefit associated with reducing three key uncertainties present in the early stages of the COVID-19 pandemic: the basic reproduction number ([Formula: see text]), case severity (CS), and the relative infectiousness of children compared to adults (CI). The specific decision problem we consider is the optimal level of investment in intensive care unit (ICU) beds. Our analysis incorporates mathematical models of disease transmission and clinical pathways in order to estimate ICU demand and disease outcomes across a range of scenarios.

RESULTS

We found that VoI analysis enabled us to estimate the relative benefit of resolving different uncertainties about epidemiological and clinical aspects of SARS-CoV-2. Given the initial beliefs of an expert, obtaining more information about case severity had the highest parameter value of information, followed by the basic reproduction number [Formula: see text]. Resolving uncertainty about the relative infectiousness of children did not affect the decision about the number of ICU beds to be purchased for any COVID-19 outbreak scenarios defined by these three parameters.

CONCLUSION

For the scenarios where the value of information was high enough to justify monitoring, if CS and [Formula: see text] are known, management actions will not change when we learn about child infectiousness. VoI is an important tool for understanding the importance of each disease factor during outbreak preparedness and can help to prioritise the allocation of resources for relevant information.

COVID-19 Vaccination and Healthcare Demand

One of the driving concerns during any epidemic is the strain on the healthcare system. As we have seen many times over the globe with the COVID-19 pandemic, hospitals and ICUs can quickly become overwhelmed by cases. While strict periods of public health mitigation have certainly helped decrease incidence and thus healthcare demand, vaccination is the only clear long-term solution. In this paper, we develop a two-module model to forecast the effects of relaxation of non-pharmaceutical intervention and vaccine uptake on daily incidence, and the cascade effects on healthcare demand. The first module is a simple epidemiological model which incorporates non-pharmaceutical intervention, the relaxation of such measures and vaccination campaigns to predict caseloads into the Fall of 2021. This module is then fed into a healthcare module which can forecast the number of doctor visits, the number of occupied hospital beds, number of occupied ICU beds and any excess demand of these. From this module, we can also estimate the length of stay of individuals in ICU. For model verification and forecasting, we use the four most populous Canadian provinces as a case study.

Factors associated with changes in healthy lifestyle behaviors among hematological cancer patients during the COVID-19 pandemic

Background

There is a paucity of research examining the effects of the COVID-19 pandemic on the healthy lifestyle behaviors of hematological cancer patients. We examined changes in healthy lifestyle behaviors since the pandemic and identified factors associated with these changes among members of this high-risk population.

Methods

Hematological cancer patients (n = 394) completed a self-report online survey from July to August 2020. The survey assessed pandemic-related changes in exercise, alcohol consumption, and consumption of fruit, vegetables, and wholegrains. Information relating to several demographic, clinical, and psychological factors was also collected. Factors associated with changes in healthy lifestyle behaviors were analyzed using logistic regression.

Results

Just 14% of patients surveyed reported exercising more during the pandemic (39% exercised less). Only a quarter (24%) improved their diet, while nearly half (45%) reported eating less fruit, vegetables, and wholegrains. Just over a quarter (28%) consumed less alcohol (17% consumed more alcohol). Fear of contracting COVID-19 and psychological distress were significantly associated with reduced exercise. Younger age was significantly associated with both increased alcohol consumption and increased exercise. Being a woman was significantly associated with unfavorable changes in diet and being married was significantly associated with decreased alcohol consumption.

Conclusion

A substantial proportion of hematological cancer patients reported unfavorable changes in healthy lifestyle behaviors during the pandemic. Results highlight the importance of supporting healthy lifestyle practices among this particularly vulnerable group to ensure health is optimized while undergoing treatment and when in remission, particularly during crisis times like the COVID-19 pandemic.

Conceptual Framework for Cancer Care During a Pandemic Incorporating Evidence From the COVID-19 Pandemic

PURPOSE

With successive infection waves and the spread of more infectious variants, the COVID-19 pandemic continues to have major impacts on health care. To achieve best outcomes for patients with cancer during a pandemic, efforts to minimize the increased risk of severe pandemic infection must be carefully balanced against unintended adverse impacts of the pandemic on cancer care, with consideration to available health system capacity. Cancer Australia's conceptual framework for cancer care during a pandemic provides a planning resource for health services and policy-makers that can be broadly applied globally and to similar pandemics.

METHODS

Evidence on the impact of the COVID-19 pandemic on cancer care and health system capacity to June 2021 was reviewed, and the conceptual framework was developed and updated.

RESULTS

Components of health system capacity vary during a pandemic, and capacity relative to pandemic numbers and severity affects resources available for cancer care delivery. The challenges of successive pandemic waves and high numbers of pandemic cases necessitate consideration of changing health system capacity in decision making about cancer care. Cancer Australia’s conceptual framework provides guidance on continuation of care across the cancer pathway, in the face of challenges to health systems, while minimizing infection risk for patients with cancer and unintended consequences of delays in screening, diagnosis, and cancer treatment and backlogs because of service interruption.

CONCLUSION

Evidence from the COVID-19 pandemic supports continuation of cancer care wherever possible during similar pandemics. Cancer Australia's conceptual framework, underpinned by principles for optimal cancer care, informs decision making across the cancer care continuum. It incorporates consideration of changes in health system capacity and capacity for cancer care, in relation to pandemic progression, enabling broad applicability to different global settings.

Associations between the COVID-19 Pandemic and Hospital Infrastructure Adaptation and Planning—A Scoping Review

The SARS-CoV-2 pandemic has put unprecedented pressure on the hospital sector around the world. It has shown the importance of preparing and planning in the future for an outbreak that overwhelms every aspect of a hospital on a rapidly expanding scale. We conducted a scoping review to identify, map, and systemize existing knowledge about the relationships between COVID-19 and hospital infrastructure adaptation and capacity planning worldwide. We searched the Web of Science, Scopus, and PubMed and hand-searched gray papers published in English between December 2019 and December 2021. A total of 106 papers were included: 102 empirical studies and four technical reports. Empirical studies entailed five reviews, 40 studies focusing on hospital infrastructure adaptation and planning during the pandemics, and 57 studies on modeling the hospital capacity needed, measured mostly by the number of beds. The majority of studies were conducted in high-income countries and published within the first year of the pandemic. The strategies adopted by hospitals can be classified into short-term (repurposing medical and non-medical buildings, remote adjustments, and establishment of de novo structures) and long-term (architectural and engineering modifications, hospital networks, and digital approaches). More research is needed, focusing on specific strategies and the quality assessment of the evidence.

Modelling the Effect of COVID-19 Mass Vaccination on Acute Hospital Admissions

BACKGROUND

Managing high levels of acute COVID-19 bed occupancy can affect the quality of care provided to both affected patients and those requiring other hospital services. Mass vaccination has offered a route to reduce societal restrictions while protecting hospitals from being overwhelmed. Yet, early in the mass vaccination effort, the possible impact on future bed pressures remained subject to considerable uncertainty. The aim of this study was to model the effect of vaccination on projections of acute and intensive care bed demand within a one million resident healthcare system located in South West England.

METHODS

An age-structured epidemiological model of the Susceptible-Exposed-Infectious-Recovered (SEIR) type was fitted to local data up to the time of the study, in early March 2021. Model parameters and vaccination scenarios were calibrated through a system-wide multi-disciplinary working group, comprising public health intelligence specialists, healthcare planners, epidemiologists, and academics. Scenarios assumed incremental relaxations to societal restrictions according to the envisaged UK Government timeline, with all restrictions to be removed by 21 June 2021.

RESULTS

Achieving 95% vaccine uptake in adults by 31 July 2021 would not avert a third wave in autumn 2021 but would produce a median peak bed requirement approximately 6% (IQR: 1% to 24%) of that experienced during the second wave (January 2021). A two-month delay in vaccine rollout would lead to significantly higher peak bed occupancy, at 66% (11% to 146%) of that of the second wave. If only 75% uptake was achieved (the amount typically associated with vaccination campaigns) then the second wave peak for acute and intensive care beds would be exceeded by 4% and 19% respectively, an amount which would seriously pressure hospital capacity.

CONCLUSION

Modelling influenced decision making among senior managers in setting COVID-19 bed capacity levels, as well as highlighting the importance of public health in promoting high vaccine uptake among the population. Forecast accuracy has since been supported by actual data collected following the analysis, with observed peak bed occupancy falling comfortably within the inter-quartile range of modelled projections.

COVID-19 risk perceptions and precautions among the elderly: A study of CALD adults in South Australia

Background: Coping with COVID-19 is a challenge for culturally and linguistically diverse (CALD) older adults. In Australia, little attention has been given to understanding associations between cultural contexts, health promotion, and socio-emotional and mental health challenges of older CALD adults during the COVID-19 pandemic. Therefore, we have collected data from older CALD adults to examine their COVID-19 risk perceptions and its association with their health precautions, behavioural dimensions and emergency preparation.

Methods: A cross-sectional survey was conducted in South Australia. The CALD population aged 60 years and above were approached through 11 South Australian multicultural NGOs.

Results: We provide the details of 155 older CALD South Australians’ demographics, risk perceptions, health precautions (problem-and-emotion-focused), behavioural dimensions and emergency preparation.  The explanatory variables included demographic characteristics (age, gender, education and ethnicity); and risk perception (cognitive [likelihood of being affected] and affective dimension [fear and general concerns], and psychometric paradigm [severity, controllability, and personal impact]. The outcome measure variables were health precautions (problem-focused and emotion-focused), behavioral adaptions and emergency preparation.

Conclusions: This dataset may help the researchers who investigate multicultural health or aged care in the pandemic and or who may have interest to link with other datasets and secondary use of this primary dataset in order to develop culturally tailored pandemic-related response plan. The data set is available from Harvard Dataverse.

Sound Decision Making in Uncertain Times: Can Systems Modelling Be Useful for Informing Policy and Planning for Suicide Prevention?

The COVID-19 pandemic demonstrated the significant value of systems modelling in supporting proactive and effective public health decision making despite the complexities and uncertainties that characterise an evolving crisis. The same approach is possible in the field of mental health. However, a commonly levelled (but misguided) criticism prevents systems modelling from being more routinely adopted, namely, that the presence of uncertainty around key model input parameters renders a model useless. This study explored whether radically different simulated trajectories of suicide would result in different advice to decision makers regarding the optimal strategy to mitigate the impacts of the pandemic on mental health. Using an existing system dynamics model developed in August 2020 for a regional catchment of Western Australia, four scenarios were simulated to model the possible effect of the COVID-19 pandemic on levels of psychological distress. The scenarios produced a range of projected impacts on suicide deaths, ranging from a relatively small to a dramatic increase. Discordance in the sets of best-performing intervention scenarios across the divergent COVID-mental health trajectories was assessed by comparing differences in projected numbers of suicides between the baseline scenario and each of 286 possible intervention scenarios calculated for two time horizons; 2026 and 2041. The best performing intervention combinations over the period 2021–2041 (i.e., post-suicide attempt assertive aftercare, community support programs to increase community connectedness, and technology enabled care coordination) were highly consistent across all four COVID-19 mental health trajectories, reducing suicide deaths by between 23.9–24.6% against the baseline. However, the ranking of best performing intervention combinations does alter depending on the time horizon under consideration due to non-linear intervention impacts. These findings suggest that systems models can retain value in informing robust decision making despite uncertainty in the trajectories of population mental health outcomes. It is recommended that the time horizon under consideration be sufficiently long to capture the full effects of interventions, and efforts should be made to achieve more timely tracking and access to key population mental health indicators to inform model refinements over time and reduce uncertainty in mental health policy and planning decisions.

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

At the end of December 2019, an outbreak of COVID-19 occurred in Wuhan city, China. Modelling plays a crucial role in developing a strategy to prevent a disease outbreak from spreading around the globe. Models have contributed to the perspicacity of epidemiological variations between and within nations and the planning of desired control strategies. In this paper, a literature review was conducted to summarise knowledge about COVID-19 disease modelling in three countries-China, the UK and Australia-to develop a robust research framework for the regional areas that are urban and rural health districts of New South Wales, Australia. In different aspects of modelling, summarising disease and intervention strategies can help policymakers control the outbreak of COVID-19 and may motivate modelling disease-related research at a finer level of regional geospatial scales in the future.

Managing Pandemics with Health Informatics: Successes and Challenges

INTRODUCTION

The novel COVID-19 pandemic struck the world unprepared. This keynote outlines challenges and successes using data to inform providers, government officials, hospitals, and patients in a pandemic.

METHODS

The authors outline the data required to manage a novel pandemic including their potential uses by governments, public health organizations, and individuals.

RESULTS

An extensive discussion on data quality and on obstacles to collecting data is followed by examples of successes in clinical care, contact tracing, and forecasting. Generic local forecast model development is reviewed followed by ethical consideration around pandemic data. We leave the reader with thoughts on the next inevitable outbreak and lessons learned from the COVID-19 pandemic.

CONCLUSION

COVID-19 must be a lesson for the future to direct us to better planning and preparing to manage the next pandemic with health informatics.

Estimating age-specific COVID-19 fatality risk and time to death by comparing population diagnosis and death patterns: Australian data

Background

Mortality is a key component of the natural history of COVID-19 infection. Surveillance data on COVID-19 deaths and case diagnoses are widely available in the public domain, but they are not used to model time to death because they typically do not link diagnosis and death at an individual level. This paper demonstrates that by comparing the unlinked patterns of new diagnoses and deaths over age and time, age-specific mortality and time to death may be estimated using a statistical method called deconvolution.

Methods

Age-specific data were analysed on 816 deaths among 6235 cases over age 50 years in Victoria, Australia, from the period January through December 2020. Deconvolution was applied assuming logistic dependence of case fatality risk (CFR) on age and a gamma time to death distribution. Non-parametric deconvolution analyses stratified into separate age groups were used to assess the model assumptions.

Results

It was found that age-specific CFR rose from 2.9% at age 65 years (95% CI:2.2 – 3.5) to 40.0% at age 95 years (CI: 36.6 – 43.6). The estimated mean time between diagnosis and death was 18.1 days (CI: 16.9 – 19.3) and showed no evidence of varying by age (heterogeneity P = 0.97). The estimated 90% percentile of time to death was 33.3 days (CI: 30.4 – 36.3; heterogeneity P = 0.85). The final age-specific model provided a good fit to the observed age-stratified mortality patterns.

Conclusions

Deconvolution was demonstrated to be a powerful analysis method that could be applied to extensive data sources worldwide. Such analyses can inform transmission dynamics models and CFR assessment in emerging outbreaks. Based on these Australian data it is concluded that death from COVID-19 occurs within three weeks of diagnosis on average but takes five weeks in 10% of fatal cases. Fatality risk is negligible in the young but rises above 40% in the elderly, while time to death does not seem to vary by age.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01314-w.

Safe Blues: The case for virtual safe virus spread in the long-term fight against epidemics

Viral spread is a complicated function of biological properties, the environment, preventative measures such as sanitation and masks, and the rate at which individuals come within physical proximity. It is these last two elements that governments can control through social-distancing directives. However, infection measurements are almost always delayed, making real-time estimation nearly impossible. Safe Blues is one way of addressing the problem caused by this time lag via online measurements combined with machine learning methods that exploit the relationship between counts of multiple forms of the Safe Blues strands and the progress of the actual epidemic. The Safe Blues protocols and techniques have been developed together with an experimental minimal viable product, presented as an app on Android devices with a server backend. Following initial exploration via simulation experiments, we are now preparing for a university-wide experiment of Safe Blues.

Lies, Gosh Darn Lies, and not enough good statistics: why epidemic model parameter estimation fails

We sought to investigate whether epidemiological parameters that define epidemic models could be determined from the epidemic trajectory of infections, recovery, and hospitalizations prior to peak, and also to evaluate the comparability of data between jurisdictions reporting their statistics. We found that, analytically, the pre-peak growth of an epidemic underdetermines the model variates, and that the rate limiting variables are dominated by the exponentially expanding eigenmode of their equations. The variates quickly converge to the ratio of eigenvector components of the positive growth mode, which determines the doubling time. Without a sound epidemiological study framework, measurements of infection rates and other parameters are highly corrupted by uneven testing rates, uneven counting, and under reporting of relevant values. We argue that structured experiments must be performed to estimate these parameters in order to perform genetic association studies, or to construct viable models accurately predicting critical quantities such as hospitalization loads.

Design of a nonlinear model for the propagation of COVID-19 and its efficient nonstandard computational implementation

In this manuscript, we develop a mathematical model to describe the spreading of an epidemic disease in a human population. The emphasis in this work will be on the study of the propagation of the coronavirus disease (COVID-19). Various epidemiologically relevant assumptions will be imposed upon the problem, and a coupled system of first-order ordinary differential equations will be obtained. The model adopts the form of a nonlinear susceptible-exposed-infected-quarantined-recovered system, and we investigate it both analytically and numerically. Analytically, we obtain the equilibrium points in the presence and absence of the coronavirus. We also calculate the reproduction number and provide conditions that guarantee the local and global asymptotic stability of the equilibria. To that end, various tools from analysis will be employed, including Volterra-type Lyapunov functions, LaSalle's invariance principle and the Routh-Hurwitz criterion. To simulate computationally the dynamics of propagation of the disease, we propose a nonstandard finite-difference scheme to approximate the solutions of the mathematical model. A thorough analysis of the discrete model is provided in this work, including the consistency and the stability analyses, along with the capability of the discrete model to preserve the equilibria of the continuous system. Among other interesting results, our numerical simulations confirm the stability properties of the equilibrium points.

COVID-19 Down Under: Australia's Initial Pandemic Experience

The following case study aims to provide a broad overview of the initial Australian epidemiological situation of the novel coronavirus disease (COVID-19) pandemic. We provide a case presentation of Australia's current demographic characteristics and an overview of their health care system. The data we present on Australia's COVID-19 situation pertain to the initial wave of the pandemic from January through to 20 April 2020. The results of our study indicate the number of reported COVID-19 cases in Australia reduced, and Australia initially managed to successfully flatten the curve-from an initial doubling time of 3.4 days at the end of March 2020 to a doubling time of 112 days as of 20 April 2020. Using SEIR mathematical modelling, we investigate a scenario assuming infections increase once mitigation measures are lifted. In this case, Australia could experience over 15,000 confirmed cases by the end of April 2020. How Australia's government, health authorities and citizens adjust to preventative measures to reduce the risk of transmission as well as the risk of overburdening Australia's health care system is crucial. Our study presents the initial non-pharmaceutical intervention measures undertaken by the Australian health authorities in efforts to mitigate the rate of infection, and their observed and predicted outcomes. Finally, we conclude our study by presenting the observed and expected economic, social, and political disruptions Australians may endure as a result of the initial phase of the pandemic.

An evaluation of enhanced surveillance of hospitalised COVID-19 patients to inform the public health response in Victoria

Background

Public health surveillance is crucial for supporting a rapid and effective response to public health emergencies. In response to the coronavirus disease (COVID-19) pandemic, an enhanced surveillance system of hospitalised COVID-19 patients was established by the Victorian Department of Health and Human Services (DHHS) and the Victorian Healthcare Associated Infection Surveillance System Coordinating Centre. The system aimed to reduce workforce capacity constraints and increase situational awareness on the status of hospitalised patients.

Methods

The system was evaluated, using guidelines from the United States Centers for Disease Control and Prevention, against eight attributes: acceptability; data quality; flexibility; representativeness; simplicity; stability; timeliness; and usefulness. Evidence was generated from stakeholder consultation, participant observation, document review, systems review, issues log review and audits. Data were collected and analysed over a period of up to three months, covering pre- and post-implementation from March to June 2020.

Results

This system was rapidly established by leveraging established relationships and infrastructure. Stakeholders agreed that the system was important but was limited by a reliance on daily manual labour (including weekends), which impeded scalability. The ability of the system to perform well in each attribute was expected to shift with the severity of the pandemic; however, at the time of this evaluation, when there were an average 23 new cases per day (0.3 cases per 100,000 population per day), the system performed well.

Conclusion

This enhanced surveillance system was useful and achieved its key DHHS objectives during the COVID-19 public health emergency in Victoria. Recommendations for improvement were made to the current and future systems, including the need to plan alternatives to improve the system's scalability and to maintain stakeholder acceptability.

Hospital outcomes after a COVID-19 diagnosis from January to May 2020 in New South Wales Australia

Objective

To describe hospitalisation rates following COVID-19 infection in NSW.

Design, setting and participants

Analysis of all confirmed COVID-19 cases diagnosed in NSW from 1 January to 31 May 2020 extracted from the NSW Notifiable Conditions Information Management System and linked to routinely collected hospitalisation data.

Outcome measures

In-patient hospitalisations and hospital service utilisation details.

Results

There were 3,101 COVID-19 cases diagnosed between 1 January and 31 May 2020 in NSW: mean age 46.7 years, 50.5% were females. Overall, 12.5% (n = 389) had a record of inpatient hospitalisation, 4.2% (n = 130) were admitted to ICU and 1.9% (n = 58) received ventilation. Among adult cases, hospital and ICU admission rates increased with increasing age: 2.9% of those aged 20-29 years were hospitalised, increasing to 46.6% of those aged 80-89 years; 0.6% of those aged 20-29 years were admitted to ICU, increasing to 11.2% of those aged 70-79 years. The median time from symptoms to hospitalisation was seven days (IQR 4-11). The median time in hospital was nine days (IQR 4-20), and in ICU six days (IQR 2-15); the median time in hospital increased with older age. Almost half (49.4%) of those hospitalised with a diagnostic code had pneumonia/lower respiratory tract infection and another 36.6% had an upper respiratory tract infection or other known COVID-19 symptoms.

Conclusion

COVID-19 is a serious infection particularly in older adults. During January to May of 2020, 1 in 8 of those diagnosed in NSW were hospitalised. While this partly reflects the cautious approach to case management in the initial phase of the pandemic, it also demonstrates the large potential impact of COVID-19 on Australian health services and need for continuing mitigation strategies.