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A sensible decision—the defining criteria still apply

Vaccination plays an important role in pandemic planning and response. The possibility of developing an effective vaccine for a novel pandemic virus is not assured. However, as we have seen with SARS-CoV-2 vaccine development, with sufficient resources and global focus, successful outcomes can be achieved in a relatively short period. However even when vaccine is available it will initially be scarce. When one becomes available, how should it be distributed? In this paper we explicate how ethical thinking that is carefully attuned to context is essential to decisions about how we should conduct vaccination in a pandemic where demand exceeds supply. We focus on two key issues. First, setting the aims for a pandemic vaccination programme. Second, thinking about the means of delivering a chosen aim. We outline how pandemic vaccine distribution strategies can be implemented with distinct aims, e.g. protecting groups at greater risk of harm, saving the most lives, or ensuring societal benefit. Each aim will result in a focus on a different priority population and each strategy will have a different benefit-harm profile. Once we have decided our aim, we still have choices to make about delivery. We may achieve at least some ends via direct or indirect strategies. Such policy decisions are not merely technical, but necessarily involve ethics. One important general issue is that such planning decisions about distribution will always be made under conditions of uncertainty about vaccine safety and effectiveness. However, planning how to distribute vaccine for SARS-CoV-2 is even harder because we understand relatively little about the virus, transmission, and its immunological impact in the short and long term.

Freya Shearer and co-authors discuss the use of decision analysis in planning for infectious disease pandemics.Freya Shearer and co-authors discuss the use of decision analysis in planning for infectious disease pandemics.

As of 1 May 2020, there had been 6808 confirmed cases of COVID-19 in Australia. Of these, 98 had died from the disease. The epidemic had been in decline since mid-March, with 308 cases confirmed nationally since 14 April. 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 that contribute to individual country experiences of COVID-19, such as the intensity and timing of public health interventions, will assist in the next stage of response planning globally. We describe how the epidemic and public health response unfolded in Australia up to 13 April. We estimate that the effective reproduction number was likely below one in each Australian state since mid-March and forecast that clinical demand would remain below capacity thresholds over the forecast period (from mid-to-late April).

Understanding human movement patterns at local, national and international scales is critical in a range of fields, including transportation, logistics and epidemiology. Data on human movement is increasingly available, and when combined with statistical models, enables predictions of movement patterns across broad regions. Movement characteristics, however, strongly depend on the scale and type of movement captured for a given study. The models that have so far been proposed for human movement are best suited to specific spatial scales and types of movement. Selecting both the scale of data collection, and the appropriate model for the data remains a key challenge in predicting human movements. We used two different data sources on human movement in Australia, at different spatial scales, to train a range of statistical movement models and evaluate their ability to predict movement patterns for each data type and scale. Whilst the five commonly-used movement models we evaluated varied markedly between datasets in their predictive ability, we show that an ensemble modelling approach that combines the predictions of these models consistently outperformed all individual models against hold-out data.

Buruli ulcer (BU) is a rare, neglected tropical disease caused by Mycobacterium ulcerans that can lead to severe skin ulcers. To determine the epidemiology of BU in Victoria, Australia, during 2017-2022 we analyzed surveillance data. A total of 1,751 cases of BU were notified; 968 (55%) patients were male and 781 (45%) female (2 were missing sex data), and 984 (56%) resided in established BU-endemic areas, although an increasing number were in new BU-endemic areas. Most cases (83%, 1,301) were classified as category I. Multivariate modeling demonstrated that factors for severe BU included being male, being older, and living in a new BU-endemic or non-BU-endemic area. A relatively shorter interval between first visit to a clinician and receipt of diagnosis was protective against severe disease. The expansion of BU-endemic areas throughout Victoria remains a public health concern and calls for targeted action, particularly for patients and clinicians in new BU-endemic areas.

To complement labour-intensive conventional contact tracing, digital proximity tracing was implemented widely during the COVID-19 pandemic. However, the privacy-centred design of the dominant Google-Apple exposure notification framework has hindered assessment of its effectiveness. Between October 2021 and January 2022, we systematically collected app use and notification receipt data within a test and trace programme targeting around 50,000 university students in Leuven, Belgium. Due to low success rates in each studied step of the digital notification cascade, only 4.3% of exposed contacts (CI: 2.8-6.1%) received such notifications, resulting in 10 times more cases detected through conventional contact tracing. Moreover, the infection risk of digitally traced contacts (5.0%; CI: 3.0–7.7%) was lower than that of conventionally traced non-app users (9.8%; CI: 8.8-10.7%; p  = 0.002). Contrary to common perception as near instantaneous, there was a 1.2-day delay (CI: 0.6–2.2) between case PCR result and digital contact notification. These results highlight major limitations of a digital proximity tracing system based on the dominant framework. Digital proximity tracing apps were widely used during the COVID-19 pandemic but have not been thoroughly evaluated. Here, the authors use data from students in Leuven, Belgium and estimate that apps notified only ~4% exposed contacts, had a 1–2 day delay for notification, and identified fewer infected contacts than manual contact tracing.

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 effective reproduction number 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.

To protect healthcare workforce during the COVID-19 pandemic, rigorous efforts were made to reduce infection rates among healthcare workers (HCWs), especially prior to vaccine availability. This study aimed to investigate the prevalence of SARS-CoV-2 infections among HCWs and identify potential risk factors associated with transmission. We searched MEDLINE, Embase, and Google Scholar from 1 December 2019 to 5 February 2024. From 498 initial records, 190 articles were reviewed, and 63 studies were eligible. ROBINS-E tool revealed a lower risk of bias in several domains; however, some concerns related to confounding and exposure measurement were identified. Globally, 11% (95% confidence interval (CI) 9–13) of 283,932 HCWs were infected with SARS-CoV-2. Infection rates were associated with a constellation of risk factors and major circulating SARS-CoV-2 variants. Household exposure (odds ratio (OR) 7.07; 95% CI 3.93–12.73), working as a cleaner (OR 2.72; 95% CI 1.39–5.32), occupational exposure (OR 1.79; 95% CI 1.49–2.14), inadequate training on infection prevention and control (OR 1.46; 95% CI 1.14–1.87), insufficient use of personal protective equipment (OR 1.45; 95% CI 1.14–1.84), performing aerosol generating procedures (OR 1.36; 95% CI 1.21–1.52) and inadequate hand hygiene (OR 1.17; 95% CI 0.79–1.73) were associated with an increased SARS-CoV-2 infection. Conversely, history of quarantine (OR 0.23; 95% CI 0.08–0.60) and frequent decontamination of high touch areas (OR 0.52; 95% CI 0.42–0.64) were protective factors against SARS-CoV-2 infection. This study quantifies the substantial global burden of SARS-CoV-2 infection among HCWs. We underscore the urgent need for effective infection prevention and control measures, particularly addressing factors such as household exposure and occupational practices by HCWs, including cleaning staff.

Background Recent deliberations by Australian public health researchers and practitioners produced an ethical framework of how decisions should be made to distribute pandemic influenza vaccine. The outcome of the deliberations was that the population should be considered in two categories, Level 1 and Level 2, with Level 1 groups being offered access to the pandemic influenza vaccine before other groups. However, the public health researchers and practitioners recognised the importance of making space for public opinion and sought to understand citizens values and preferences, especially First Nations peoples. Methods We conducted First Nations Community Panels in two Australian locations in 2019 to assess First Nations people’s informed views through a deliberative process on pandemic influenza vaccination distribution strategies. Panels were asked to make decisions on priority levels, coverage and vaccine doses. Results Two panels were conducted with eighteen First Nations participants from a range of ages who were purposively recruited through local community networks. Panels heard presentations from public health experts, cross-examined expert presenters and deliberated on the issues. Both panels agreed that First Nations peoples be assigned Level 1 priority, be offered pandemic influenza vaccination before other groups, and be offered two doses of vaccine. Reasons for this decision included First Nations people’s lives, culture and families are important; are at-risk of severe health outcomes; and experience barriers and challenges to accessing safe, quality and culturally appropriate healthcare. We found that communication strategies, utilising and upskilling the First Nations health workforce, and targeted vaccination strategies are important elements in pandemic preparedness and response with First Nations peoples. Conclusions First Nations Community Panels supported prioritising First Nations peoples for pandemic influenza vaccination distribution and offering greater protection by using a two-dose full course to fewer people if there are initial supply limitations, instead of one dose to more people, during the initial phase of the vaccine roll out. The methodology and findings can help inform efforts in planning for future pandemic vaccination strategies for First Nations peoples in Australia.