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Spatiotemporal patterns of influenza in Western Australia

Understanding the geospatial distribution of influenza infection and the risk factors associated with infection clustering can inform targeted preventive interventions. We conducted a geospatial analysis to investigate the spatial patterns and identify drivers of medically attended influenza infection across all age groups in Western Australia.

Opinion: Modelling for the health of our next generation

Nearly 170 years ago a British doctor applied geospatial mapping to identify the source of a cholera outbreak in central London. Using a street map to plot the location of the homes of the sick, Dr John Snow was able to pinpoint a ‘ground zero’ for the outbreak – a contaminated water pump.

Survivors of drug-resistant TB face long-term health problems: study

New research highlights the long-term physical health problems faced by people who survive drug-resistant tuberculosis (TB) .

Replicating hypergraph disease dynamics with lower-order interactions

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. 

Modelling Micro-Elimination: Third-Trimester Tenofovir Prophylaxis for Perinatal Transmission of Hepatitis B in the Remote Dolpa District of Nepal

Hepatitis B (HBV) prevalence is very high in pregnant women in the Dolpa district of Nepal, a region characterised by a remote geographic landscape and low vaccination coverage. Using mathematical modelling, we evaluated the impact of third-trimester tenofovir disoproxil fumarate (TDF) prophylaxis on HBV burden and estimated the time required to achieve HBV elimination in Dolpa. 

Understanding Malaria Transmission and Control within and Between Regions in Zambia Using a Socio-Spatial Determinants of Health Framework

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. 

Quantifying undetected tuberculosis in Ethiopia using a novel geospatial modelling approach

Tuberculosis (TB) is the leading infectious cause of death globally, with approximately three million cases remaining undetected, thereby contributing to community transmission. Understanding the spatial distribution of undetected TB in high-burden settings is critical for designing and implementing geographically targeted interventions for early detection and control.

Mapping traditional birth attendance in sub-Saharan Africa between 2012 and 2023: analysis of data from demographic and health surveys

Traditional birth attendance (TBA) remains common in Sub-Saharan Africa (SSA), impacting maternal and neonatal mortality rates. This study aimed at producing high-resolution geospatial estimates and identifying predictors of TBA-assisted childbirth in SSA.

Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum and Plasmodium vivax malaria, 2000-22: a spatial and temporal modelling study

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. 

Comparison of new computational methods for spatial modelling of malaria

Geostatistical analysis of health data is increasingly used to model spatial variation in malaria prevalence, burden, and other metrics. Traditional inference methods for geostatistical modelling are notoriously computationally intensive, motivating the development of newer, approximate methods for geostatistical analysis or, more broadly, computational modelling of spatial processes.