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Since its inception in 2005, the US President's Malaria Initiative (PMI) has played a major role in the reductions in malaria morbidity and mortality observed across Africa. With the status of PMI funding and operations currently uncertain, we aimed to quantify the impact that a fully functioning PMI would have on malaria cases and deaths in Africa during 2025.
With more than 1.2 million illnesses and 29,000 deaths in sub-Saharan Africa in 2017, typhoid fever continues to be a major public health problem. Effective control of the disease would benefit from an understanding of the subnational geospatial distribution of the disease incidence.
Tuberculosis (TB) remains a major public health challenge in Ethiopia, despite being a preventable disease. TB preventive treatment (TPT) is a critical intervention to prevent the progression from latent TB infection to active disease, particularly among household contacts of TB patients and people living with HIV due to weakened immunity. However, the initiation and completion rates of TPT at subnational and local levels have not been thoroughly investigated. This study aims to map facility-based TPT initiation and completion rates among household contacts of TB across Ethiopia.
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.
Malaria imposes a significant global health burden and remains a major cause of child mortality in sub-Saharan Africa. In many countries, malaria transmission varies seasonally. The use of seasonally-deployed interventions is expanding, and the effectiveness of these control measures hinges on quantitative and geographically-specific characterisations of malaria seasonality.
Quantifying the extent to which previous infections and vaccinations confer protection against future infection or disease outcomes is critical to managing the transmission and consequences of infectious diseases. We present a general statistical model for predicting the strength of protection conferred by different immunising exposures (numbers, types, and strains of both vaccines and infections), against multiple outcomes of interest, whilst accounting for immune waning.
Malaria incidence (MI) has significantly declined in Nepal, and this study aimed to investigate the spatiotemporal distribution and drivers of MI at the ward level. Data for malaria cases were obtained from the National Surveillance System from 2013 to 2021. Data for covariates, including annual mean temperature, annual mean precipitation, and distance to the nearest city, were obtained from publicly available sources. A Bayesian spatial model was used to identify factors associated with the spatial distribution of MI.
Arboviruses transmitted mainly by Aedes (Stegomyia) aegypti and Ae. albopictus, including dengue, chikungunya, and Zika viruses, and yellow fever virus in urban settings, pose an escalating global threat. Existing risk maps, often hampered by surveillance biases, may underestimate or misrepresent the true distribution of these diseases and do not incorporate epidemiological similarities despite shared vector species.
The clinical development of novel vaccines, injectable therapeutics, and oral chemoprevention drugs has the potential to deliver significant advancements in the prevention of Plasmodium falciparum malaria. These innovations could support regions in accelerating malaria control, transforming existing intervention packages by supplementing interventions with imperfect effectiveness or offering an entirely new tool.
o map subnational and local prevalence of drug-resistant tuberculosis (DR-TB) across Africa. We assembled a geolocated dataset from 173 sources across 31 African countries, comprising drug susceptibility test results and covariate data from publicly available databases. We used Bayesian model-based geostatistical framework with multivariate Bayesian logistic regression model to estimate DR-TB prevalence at lower administrative levels.