Skip to content

Search

Modeling COVID-19 disease processes by remote elicitation of causal Bayesian networks from medical experts

COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology.

The seasonality of respiratory syncytial virus in Western Australia prior to implementation of SARS-CoV-2 non-pharmaceutical interventions

Respiratory syncytial virus (RSV) seasonality is dependent on the local climate. We assessed the stability of RSV seasonality prior to the SARS-CoV-2 pandemic in Western Australia (WA), a state spanning temperate and tropical regions.

Development of a sustained release implant of benzathine penicillin G for secondary prophylaxis of rheumatic heart disease

Regular intramuscular (i.m.) benzathine penicillin G (BPG) injections have been the cornerstone of rheumatic heart disease (RHD) secondary prophylaxis since the 1950s. Patient adherence to IM BPG is poor, largely due to pain, the need for regular injections every 3-4 weeks and health sector delivery challenges in resource-limited settings. There is an urgent need for new approaches for secondary prophylaxis, such as an implant which could provide sustained penicillin concentrations for more than 6 months.

Epidemiology of soil-transmitted helminths using quantitative PCR and risk factors for hookworm and Necator americanus infection in school children in Dak Lak province, Vietnam

Soil-transmitted helminth (STH) infection is driven by a complex interaction of demographic, socioeconomic and behavioural factors, including those related to water, sanitation and hygiene (WASH). Epidemiological studies that measure both infection and potential risk factors associated with infection help to understand the drivers of transmission in a population and therefore can provide information to optimise STH control programmes.

Spatial and Temporal Data Visualisation for Mass Dissemination: Advances in the Era of COVID-19

The COVID-19 pandemic is the first major pandemic of the digital age and has been characterised by unprecedented public consumption of spatial and temporal disease data, which can enable greater transparency and accountability of governments to the public for their public health decisions.

Determining the true incidence of seasonal respiratory syncytial virus-confirmed hospitalizations in preterm and term infants in Western Australia

Respiratory syncytial virus contributes to significant global infant morbidity and mortality. We applied a previously developed statistical prediction model incorporating pre-pandemic RSV testing data and hospital admission data to estimate infant RSV-hospitalizations by birth month and prematurity, focused on infants aged <1 year.

Patient-reported outcome measures for paediatric acute lower respiratory infection studies

Patient-reported outcome measures (PROMs) are recommended for capturing meaningful outcomes in clinical trials. The use of PROMs for children with acute lower respiratory infections (ALRIs) has not been systematically reported. We aimed to identify and characterise patient-reported outcomes and PROMs used in paediatric ALRI studies and summarise their measurement properties.

Prevalence of long-term physical sequelae among patients treated with multi-drug and extensively drug-resistant tuberculosis: a systematic review and meta-analysis

Physical sequelae related to multi-drug resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) are emerging and under-recognised global challenges. This systematic review and meta-analysis aimed to quantify the prevalence and the types of long-term physical sequelae associated with patients treated for MDR- and XDR-TB.

The Challenge of Diagnosing Invasive Pulmonary Aspergillosis in Children: A Review of Existing and Emerging Tools

Invasive pulmonary aspergillosis remains a major cause of morbidity and mortality for immunocompromised children, particularly for patients with acute leukaemia and those undergoing haematopoietic stem cell transplantation. Timely diagnosis, using a combination of computed tomography (CT) imaging and microbiological testing, is key to improve prognosis, yet there are inherent challenges in this process. For CT imaging, changes in children are generally less specific than those reported in adults and recent data are limited.

among children with pneumonia using a causal Bayesian network

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.