Wastewater Surveillance Can Function as an Early Warning System for COVID-19 in Low-Incidence Settings.

INTRODUCTION: During the first two years of the COVID-19 pandemic, Australia implemented a series of international and interstate border restrictions. The state of Queensland experienced limited COVID-19 transmission and relied on lockdowns to stem any emerging COVID-19 outbreaks. However, early detection of new outbreaks was difficult. In this paper, we describe the wastewater surveillance program for SARS-CoV-2 in Queensland, Australia, and report two case studies in which we aimed to assess the potential for this program to provide early warning of new community transmission of COVID-19. Both case studies involved clusters of localised transmission, one originating in a Brisbane suburb (Brisbane Inner West) in July-August 2021, and the other originating in Cairns, North Queensland in February-March 2021. MATERIALS AND METHODS: Publicly available COVID-19 case data derived from the notifiable conditions (NoCs) registry from the Queensland Health data portal were cleaned and merged spatially with the wastewater surveillance data using statistical area 2 (SA2) codes. The positive predictive value and negative predictive value of wastewater detection for predicting the presence of COVID-19 reported cases were calculated for the two case study sites. RESULTS: Early warnings for local transmission of SARS-CoV-2 through wastewater surveillance were noted in both the Brisbane Inner West cluster and the Cairns cluster. The positive predictive value of wastewater detection for the presence of notified cases of COVID-19 in Brisbane Inner West and Cairns were 71.4% and 50%, respectively. The negative predictive value for Brisbane Inner West and Cairns were 94.7% and 100%, respectively. CONCLUSIONS: Our findings highlight the utility of wastewater surveillance as an early warning tool in low COVID-19 transmission settings.

Monitoring Temporal Changes in SARS-CoV-2 Spike Antibody Levels and Variant-Specific Risk for Infection, Dominican Republic, March 2021-August 2022.

To assess changes in SARS-CoV-2 spike binding antibody prevalence in the Dominican Republic and implications for immunologic protection against variants of concern, we prospectively enrolled 2,300 patients with undifferentiated febrile illnesses in a study during March 2021-August 2022. We tested serum samples for spike antibodies and tested nasopharyngeal samples for acute SARS-CoV-2 infection using a reverse transcription PCR nucleic acid amplification test. Geometric mean spike antibody titers increased from 6.6 (95% CI 5.1-8.7) binding antibody units (BAU)/mL during March-June 2021 to 1,332 (95% CI 1,055-1,682) BAU/mL during May-August 2022. Multivariable binomial odds ratios for acute infection were 0.55 (95% CI 0.40-0.74), 0.38 (95% CI 0.27-0.55), and 0.27 (95% CI 0.18-0.40) for the second, third, and fourth versus the first anti-spike quartile; findings were similar by viral strain. Combining serologic and virologic screening might enable monitoring of discrete population immunologic markers and their implications for emergent variant transmission.

Antibiotic Consumption in Vanuatu before and during the COVID-19 Pandemic, 2018 to 2021: An Interrupted Time Series Analysis.

The study objectives were to examine antibiotic consumption at Vila Central Hospital (VCH), Vanuatu between January 2018 and December 2021 and the influence of the COVID-19 pandemic on antibiotic consumption during this period. Data on antibiotic usage were obtained from the Pharmacy database. We used the WHO's Anatomical Therapeutic Classification/Defined Daily Dose (ATC/DDD) index, VCH's inpatient bed numbers and the hospital's catchment population to calculate monthly antibiotic consumption. The results were expressed as DDDs per 100 bed days for inpatients (DBDs) and DDDs per 1000 inhabitants per day for outpatients (DIDs). Interrupted time series (ITS) was used to assess the influence of COVID-19 by comparing data before (January 2018 to January 2020) and during (February 2020 to December 2021) the pandemic. Ten antibiotics were examined. In total, 226 DBDs and 266 DBDs were consumed before and during COVID-19 by inpatients, respectively with mean monthly consumption being significantly greater during COVID-19 than before the pandemic (2.66 (p = 0.009, 95% CI 0.71; 4.61)). Whilst outpatients consumed 102 DIDs and 92 DIDs before and during the pandemic, respectively, the difference was not statistically significant. Findings also indicated that outpatients consumed a significantly lower quantity of Watch antibiotics during COVID-19 than before the pandemic (0.066 (p = 0.002, 95% CI 0.03; 0.11)). The immediate impact of COVID-19 caused a reduction in both inpatient and outpatient mean monthly consumption by approximately 5% and 16%, respectively, and this was followed by an approximate 1% monthly increase until the end of the study. By mid-2021, consumption had returned to pre-pandemic levels.

SARS-CoV-2 seroprevalence, cumulative infections, and immunity to symptomatic infection - A multistage national household survey and modelling study, Dominican Republic, June-October 2021.

Background: Population-level SARS-CoV-2 immunological protection is poorly understood but can guide vaccination and non-pharmaceutical intervention priorities. Our objective was to characterise cumulative infections and immunological protection in the Dominican Republic. Methods: Household members ≥5 years were enrolled in a three-stage national household cluster serosurvey in the Dominican Republic. We measured pan-immunoglobulin antibodies against the SARS-CoV-2 spike (anti-S) and nucleocapsid glycoproteins, and pseudovirus neutralising activity against the ancestral and B.1.617.2 (Delta) strains. Seroprevalence and cumulative prior infections were weighted and adjusted for assay performance and seroreversion. Binary classification machine learning methods and pseudovirus neutralising correlates of protection were used to estimate 50% and 80% protection against symptomatic infection. Findings: Between 30 Jun and 12 Oct 2021 we enrolled 6683 individuals from 3832 households. We estimate that 85.0% (CI 82.1-88.0) of the ≥5 years population had been immunologically exposed and 77.5% (CI 71.3-83) had been previously infected. Protective immunity sufficient to provide at least 50% protection against symptomatic SARS-CoV-2 infection was estimated in 78.1% (CI 74.3-82) and 66.3% (CI 62.8-70) of the population for the ancestral and Delta strains respectively. Younger (5-14 years, OR 0.47 [CI 0.36-0.61]) and older (≥75-years, 0.40 [CI 0.28-0.56]) age, working outdoors (0.53 [0.39-0.73]), smoking (0.66 [0.52-0.84]), urban setting (1.30 [1.14-1.49]), and three vs no vaccine doses (18.41 [10.69-35.04]) were associated with 50% protection against the ancestral strain. Interpretation: Cumulative infections substantially exceeded prior estimates and overall immunological exposure was high. After controlling for confounders, markedly lower immunological protection was observed to the ancestral and Delta strains across certain subgroups, findings that can guide public health interventions and may be generalisable to other settings and viral strains. Funding: This study was funded by the US CDC.

A Bayesian network analysis quantifying risks versus benefits of the Pfizer COVID-19 vaccine in Australia.

The Pfizer COVID-19 vaccine is associated with increased myocarditis incidence. Constantly evolving evidence regarding incidence and case fatality of COVID-19 and myocarditis related to infection or vaccination, creates challenges for risk-benefit analysis of vaccination. Challenges are complicated further by emerging evidence of waning vaccine effectiveness, and variable effectiveness against variants. Here, we build on previous work on the COVID-19 Risk Calculator (CoRiCal) by integrating Australian and international data to inform a Bayesian network that calculates probabilities of outcomes for the delta variant under different scenarios of Pfizer COVID-19 vaccine coverage, age groups (≥12 years), sex, community transmission intensity and vaccine effectiveness. The model estimates that in a population where 5% were unvaccinated, 5% had one dose, 60% had two doses and 30% had three doses, there was a substantially greater probability of developing (239-5847 times) and dying (1430-384,684 times) from COVID-19-related than vaccine-associated myocarditis (depending on age and sex). For one million people with this vaccine coverage, where transmission intensity was equivalent to 10% chance of infection over 2 months, 68,813 symptomatic COVID-19 cases and 981 deaths would be prevented, with 42 and 16 expected cases of vaccine-associated myocarditis in males and females, respectively. These results justify vaccination in all age groups as vaccine-associated myocarditis is generally mild in the young, and there is unequivocal evidence for reduced mortality from COVID-19 in older individuals. The model may be updated to include emerging best evidence, data pertinent to different countries or vaccines and other outcomes such as long COVID.

Designing an evidence-based Bayesian network for estimating the risk versus benefits of AstraZeneca COVID-19 vaccine.

Uncertainty surrounding the risk of developing and dying from Thrombosis and Thrombocytopenia Syndrome (TTS) associated with the AstraZeneca (AZ) COVID-19 vaccine may contribute to vaccine hesitancy. A model is urgently needed to combine and effectively communicate evidence on the risks versus benefits of the AZ vaccine. We developed a Bayesian network to consolidate evidence on risks and benefits of the AZ vaccine, and parameterised the model using data from a range of empirical studies, government reports, and expert advisory groups. Expert judgement was used to interpret the available evidence and determine the model structure, relevant variables, data for inclusion, and how these data were used to inform the model. The model can be used as a decision-support tool to generate scenarios based on age, sex, virus variant and community transmission rates, making it useful for individuals, clinicians, and researchers to assess the chances of different health outcomes. Model outputs include the risk of dying from TTS following the AZ COVID-19 vaccine, the risk of dying from COVID-19 or COVID-19-associated atypical severe blood clots under different scenarios. Although the model is focused on Australia, it can be adapted to international settings by re-parameterising it with local data. This paper provides detailed description of the model-building methodology, which can be used to expand the scope of the model to include other COVID-19 vaccines, booster doses, comorbidities and other health outcomes (e.g., long COVID) to ensure the model remains relevant in the face of constantly changing discussion on risks versus benefits of COVID-19 vaccination.

Differential privacy for public health data: An innovative tool to optimize information sharing while protecting data confidentiality.

Coronavirus disease 2019 (COVID-19) has highlighted the need for the timely collection and sharing of public health data. It is important that data sharing is balanced with protecting confidentiality. Here we discuss an innovative mechanism to protect health data, called differential privacy. Differential privacy is a mathematically rigorous definition of privacy that aims to protect against all possible adversaries. In layperson's terms, statistical noise is applied to the data so that overall patterns can be described, but data on individuals are unlikely to be extracted. One of the first use cases for health data in Australia is the development of the COVID-19 Real-Time Information System for Preparedness and Epidemic Response (CRISPER), which provides proof of concept for the use of this technology in the health sector. If successful, this will benefit future sharing of public health data.

Development of the COVID-19 Real-Time Information System for Preparedness and Epidemic Response (CRISPER), Australia.

Accurate and current information has been highlighted across the globe as a critical requirement for the COVID-19 pandemic response. To address this need, many interactive dashboards providing a range of different information about COVID-19 have been developed. A similar tool in Australia containing current information about COVID-19 could assist general practitioners and public health responders in their pandemic response efforts. The COVID-19 Real-time Information System for Preparedness and Epidemic Response (CRISPER) has been developed to provide accurate and spatially explicit real-time information for COVID-19 cases, deaths, testing and contact tracing locations in Australia. Developed based on feedback from key users and stakeholders, the system comprises three main components: (1) a data engine; (2) data visualization and interactive mapping tools; and (3) an automated alert system. This system provides integrated data from multiple sources in one platform which optimizes information sharing with public health responders, primary health care practitioners and the general public.

Managing the Digital Disruption Associated with COVID-19-Driven Rapid Digital Transformation in Brisbane, Australia.

BACKGROUND: The COVID-19 pandemic has forced rapid digital transformation of many health systems. These innovations are now entering the literature, but there is little focus on the resulting disruption. OBJECTIVE: We describe the implementation of digital innovations during the COVID-19 response of Australia's largest health service, Metro North (in Brisbane, Queensland), the challenges of the subsequent digital disruption, how these were managed, and lessons learned. METHODS: Prior to the COVID-19 pandemic, the Australian state of Queensland created the Queensland Digital Clinical Charter, which provides guidance for the development of digital health programs. The guidelines utilize three horizons: digitizing workflows, leveraging digital data to transform clinical care, and reimagining new and innovative models of care. The technical response to COVID-19 in Metro North is described across these horizons. The rapid digital response caused significant disruption to health care delivery; management of the disruption and the outcomes are detailed. This is a participatory action research project, with members of the research team assisting with leading the implementation project informing the case report content. RESULTS: Several digital innovations were introduced across Metro North during the COVID-19 response. This resulted in significant disruption creating digital hypervigilance, digital deceleration, data discordance, and postdigital "depression." Successful management of the digital disruption minimized the negative effects of rapid digital transformation, and contributed to the effective management of the pandemic in Queensland. CONCLUSION: The rapid digital transformation in Metro North during COVID-19 was successful in several aspects; however, ongoing challenges remain. These include the need to improve data sharing and increase interoperability. Importantly, the innovations need to be evaluated to ensure that Metro North can capitalize on these changes and incorporate them into long-term routine practice. Moving forward, it will be essential to manage not only the pandemic, but increasingly, the resultant digital disruption.

Risk-benefit analysis of the AstraZeneca COVID-19 vaccine in Australia using a Bayesian network modelling framework.

Thrombosis and Thrombocytopenia Syndrome (TTS) has been associated with the AstraZencea (AZ) COVID-19 vaccine (Vaxzevria). Australia has reported low TTS incidence of < 3/100,000 after the first dose, with case fatality rate (CFR) of 5-6%. Risk-benefit analysis of vaccination has been challenging because of rapidly evolving data, changing levels of transmission, and variation in rates of TTS, COVID-19, and CFR between age groups. We aim to optimise risk-benefit analysis by developing a model that enables inputs to be updated rapidly as evidence evolves. A Bayesian network was used to integrate local and international data, government reports, published literature and expert opinion. The model estimates probabilities of outcomes under different scenarios of age, sex, low/medium/high transmission (0.05%/0.45%/5.76% of population infected over 6 months), SARS-CoV-2 variant, vaccine doses, and vaccine effectiveness. We used the model to compare estimated deaths from AZ vaccine-associated TTS with i) COVID-19 deaths prevented under different scenarios, and ii) deaths from COVID-19 related atypical severe blood clots (cerebral venous sinus thrombosis & portal vein thrombosis). For a million people aged ≥ 70 years where 70% received first dose and 35% received two doses, our model estimated < 1 death from TTS, 25 deaths prevented under low transmission, and > 3000 deaths prevented under high transmission. Risks versus benefits varied significantly between age groups and transmission levels. Under high transmission, deaths prevented by AZ vaccine far exceed deaths from TTS (by 8 to > 4500 times depending on age). Probability of dying from COVID-related atypical severe blood clots was 58-126 times higher (depending on age and sex) than dying from TTS. To our knowledge, this is the first example of the use of Bayesian networks for risk-benefit analysis for a COVID-19 vaccine. The model can be rapidly updated to incorporate new data, adapted for other countries, extended to other outcomes (e.g., severe disease), or used for other vaccines.

Implications of a travel connectivity-based approach for infectious disease transmission risks in Oceania.

INTRODUCTION: The increase in international travel brought about by globalisation has enabled the rapid spread of emerging pathogens with epidemic and pandemic potential. While travel connectivity-based assessments may help understand patterns of travel network-mediated epidemics, such approaches are rarely carried out in sufficient detail for Oceania where air travel is the dominant method of transportation between countries. DESIGN: Travel data from the Australian Bureau of Statistics, Stats NZ and the United Nations World Tourism Organization websites were used to calculate travel volumes in 2018 within Oceania and between Oceania and the rest of the world. The Infectious Disease Vulnerability Index (IDVI) was incorporated into the analysis as an indicator of each country's capacity to contain an outbreak. Travel networks were developed to assess the spread of infectious diseases (1) into and from Oceania, (2) within Oceania and (3) between each of the Pacific Island Countries and Territories (PICTs) and their most connected countries. RESULTS: Oceania was highly connected to countries in Asia, Europe and North America. Australia, New Zealand and several PICTs were highly connected to the USA and the UK (least vulnerable countries for outbreaks based on the IDVI), and to China (intermediate low vulnerable country). High variability was also observed between the PICTs in the geographical distribution of their international connections. The PICTs with the highest number of international connections were Fiji, French Polynesia, Guam and Papua New Guinea. CONCLUSION: Travel connectivity assessments may help to accurately stratify the risk of infectious disease importation and outbreaks in countries depending on disease transmission in other parts of the world. This information is essential to track future requirements for scaling up and targeting outbreak surveillance and control strategies in Oceania.

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.