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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.
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) .
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.
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.