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Mapping residual malaria transmission in Vietnam

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.

Mapping tuberculosis prevalence in Africa using a Bayesian geospatial analysis

Worldwide, tuberculosis (TB) remains the leading cause of death from infectious diseases. Africa is the second most-affected region, accounting for a quarter of the global TB burden, but there is limited evidence whether there is subnational variation of TB prevalence across the continent. Therefore, this study aimed to estimate sub-national and local TB prevalence across Africa.

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.

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. 

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.