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Respiratory syncytial virus (RSV) is a significant cause of respiratory infections in young children. Since 2021, RSV has been a notifiable disease in Australia. However, current surveillance systems focus on hospitalised RSV, with limited surveillance at a community level through primary care clinics. This approach only captures RSV requiring hospitalisation. Less severe illnesses, while not captured, may have significant social and economic impacts including the associated cost of care and absenteeism. The aim of this study is to establish an understanding of the broader burden of RSV in young children in a community setting.
Disease spreading models such as the ubiquitous SIS compartmental model and its numerous variants are widely used to understand and predict the behavior of a given epidemic or information diffusion process. A common approach to imbue more realism to the spreading process is to constrain simulations to a network structure, where connected nodes update their disease state based on pairwise interactions along the edges of their local neighborhood.
To characterise small-area geographical variation in the prevalence of diabetes in Australian youth. A combined statistical reconstruction and small-area estimation algorithm was applied to privacy-modulated data from the 2021 Australian Census.
In the context of high malaria burden yet limited resources, Guinea's national malaria programme adopted an innovative subnational tailoring approach, including engagement of stakeholders, data review, and data analytics, to update their malaria operational plan for 2024-2026 and identify the most appropriate interventions for each district considering the resources available.
Malaria remains a leading cause of illness and death globally, with countries in sub-Saharan Africa bearing a disproportionate burden. Global high-resolution maps of malaria prevalence, incidence, and mortality are crucial for tracking spatially heterogeneous progress against the disease and to inform strategic malaria control efforts. We present the latest such maps, the first since 2019, which cover the years 2000–22. The maps are accompanied by administrative-level summaries and include estimated COVID-19 pandemic-related impacts on malaria burden.
Differential exposure and effect of malaria results from blends of biophysical, geospatial, and social determinants of health (SDoH). Likewise, effective policies and programmatic interventions against malaria must consider the complex interaction of social and spatial factors, while comprehensive health promotion approaches must simultaneously tackle SDoH and the ecological dimensions that drive malaria.
Vietnam, as one of the countries in the Greater Mekong Subregion, has committed to eliminating all malaria by 2030. Declining case numbers highlight the country's progress, but challenges including imported cases and pockets of residual transmission remain. To successfully eliminate malaria and to prevent reintroduction of malaria transmission, geostatistical modelling of vulnerability (importation rate) and receptivity (quantified by the reproduction number) of malaria is critical.
The overarching purpose of this research is to tackle a pressing public health challenge: the declining rates of physical activity among children and their increasing reliance on private vehicles for the daily school commute.
Child anthropometric deficits remain a major public health problem in Sub-Saharan Africa (SSA) and are a key target of the UN Sustainable Development Goals (SDGs). The SDGs recommend disaggregation of health indicators by ethnic group. However, few studies have assessed how ethnicity is associated with anthropometric deficits across SSA.
Post-acute sequelae of COVID-19 (PASC), or long COVID, are a public health concern. While most recover from SARS-CoV-2 infections within weeks, some experience persistent symptoms. Here, we quantified the association between repeated SARS-CoV-2 infections and the risk of hospital-diagnosed PASC.