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Bringing optimised coronavirus disease 2019 (COVID-19) vaccine schedules to immunocompromised populations (BOOST-IC) is a multi-site, adaptive platform trial designed to assess the effect of different booster vaccination schedules in the Australian immunocompromised population on the immunogenicity, safety and cross-protection against COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants.
Untreated hepatitis C virus (HCV) infection can result in cirrhosis and hepatocellular cancer. Direct-acting antiviral (DAA) therapies are highly effective and have few side effects compared to older interferon-based therapy. Despite the Australian government providing subsidised and unrestricted access to DAA therapy for chronic HCV infection, uptake has not been sufficient to meet the global target of eliminating HCV as a public health threat by 2030.
Immunocompromised hosts experience more breakthrough infections and worse clinical outcomes following infection with COVID-19 than immunocompetent people. Prophylactic monoclonal antibody therapies can be challenging to access, and escape variants emerge rapidly. Immunity conferred through vaccination remains a central prevention strategy for COVID-19.
Vaccine policy and guideline recommendations require high quality evidence. A review of the evidence quality used to inform vaccine clinical practice guidelines could help guide researchers on how to improve the design of their clinical studies to produce evidence of greater value to decision-makers.
Group A streptococcus (GAS) infections, such as pharyngitis and impetigo, can lead to rheumatic fever and rheumatic heart disease (RHD). Australian Aboriginal and Torres Strait Islander populations experience high rates of RHD and GAS skin infection, yet rates of GAS pharyngitis are unclear.
Despite recent improvements in treatment modalities for cystic fibrosis (CF), there is currently limited evidence and a lack of consensus regarding optimal treatment strategies for the different aspects of CF, including pulmonary exacerbations (PEx). We aimed to establish a prospective cohort of people with CF (pwCF) to evaluate alternative approaches to managing CF in the era of modulator therapies.
Data on asymptomatic identification rates of respiratory viruses are limited, particularly in Indigenous populations, who suffer a high burden of OM.
The ability for vaccines to protect against infectious diseases varies among individuals, but computational models employed to inform policy typically do not account for this variation. Here we examine this issue: we implement a model of vaccine efficacy developed in the context of SARS-CoV-2 in order to evaluate the general implications of modelling correlates of protection on the individual level.
To assess potential benefits and direct healthcare cost savings with expansion of an existing childhood influenza immunisation program, we developed a dynamic transmission model for the state of Western Australia, evaluating increasing coverage in children < 5 years and routinely immunising school-aged children.
Pneumonia remains a leading cause of hospitalization and death among young children worldwide, and the diagnostic challenge of differentiating bacterial from non-bacterial pneumonia is the main driver of antibiotic use for treating pneumonia in children. Causal Bayesian networks (BNs) serve as powerful tools for this problem as they provide clear maps of probabilistic relationships between variables and produce results in an explainable way by incorporating both domain expert knowledge and numerical data.