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We aim to determine the contribute of bacteria and virus to childhood CAP to inform further development of effective strategies.

A 15 year old girl, born in Hakha, Myanmar, presented with 2 months of intermittent hemoptysis 3 years after immigrating to Australia, via Malaysia.

To describe the process for assembling a linked study that will enable the conduct of population-based studies related to immunisation and immunisation policy.

We outline three scenarios from across the health spectrum where issues with health informatics are exemplified.

The rising incidence of invasive meningococcal disease (IMD) caused by Neisseria meningitidis serogroup W in Western Australia, Australia, presents challenges for prevention. We assessed the effects of a quadrivalent meningococcal vaccination program using 2012-2020 IMD notification data.

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.

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.

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.

Cystic fibrosis (CF) is a rare, inherited, life-limiting condition predominantly affecting the lungs, for which there is no cure. The disease is characterized by recurrent pulmonary exacerbations (PEx), which are thought to drive progressive lung damage. Management of these episodes is complex and generally involves multiple interventions targeting different aspects of disease. The emergence of innovative trials and use of Bayesian statistical methods has created renewed opportunities for studying heterogeneous populations in rare diseases.

The need for coronavirus 2019 (COVID-19) vaccination in different age groups and populations is a subject of great uncertainty and an ongoing global debate. Critical knowledge gaps regarding COVID-19 vaccination include the duration of protection offered by different priming and booster vaccination regimens in different populations, including homologous or heterologous schedules.