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The prevalence of taeniasis in Thailand has decreased over the past six decades. However, it remains a public health concern, particularly in focal areas, especially along the border regions where migration between Thailand and neighboring endemic countries is frequent. Spatial distribution analysis provides a useful method for identifying high-risk areas and implementing targeted integrated control measures. This study aimed to examine the spatial patterns of taeniasis in 2008 and 2014, along with their associated One Health risk factors at the sub-district level.
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.
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.
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.
New research highlights the long-term physical health problems faced by people who survive drug-resistant tuberculosis (TB) .