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Spatial codistribution of HIV, tuberculosis and malaria in Ethiopia

HIV, tuberculosis (TB) and malaria are the three most important infectious diseases in Ethiopia, and sub-Saharan Africa. Understanding the spatial codistribution of these diseases is critical for designing geographically targeted and integrated disease control programmes. This study investigated the spatial overlap and drivers of HIV, TB and malaria prevalence in Ethiopia.

Mapping malaria by sharing spatial information between incidence and prevalence data sets

As malaria incidence decreases and more countries move towards elimination, maps of malaria risk in low-prevalence areas are increasingly needed. For low-burden areas, disaggregation regression models have been developed to estimate risk at high spatial resolution from routine surveillance reports aggregated by administrative unit polygons.

Major funding boost accelerates fight against malaria

Research to eliminate one of the world’s deadliest diseases – malaria – will be accelerated thanks to a USD $4.7 million grant from the Gates Foundation for scientists at The Kids Research Institute Australia and The University of Western Australia (UWA).

International funding boost for global malaria research

The Malaria Atlas Project (MAP) – which houses the world’s largest malaria database and is at the forefront of efforts to track and tackle the disease – has been awarded more than $16 million by the Bill & Melinda Gates Foundation.

Pandemic healthcare disruptions contributed to 76,000 extra malaria deaths: report

Disruptions of malaria case management caused by the COVID-19 pandemic likely contributed to an extra 76,000 malaria deaths in sub-Saharan Africa, according to analysis by The Kids Research Institute Australia and Curtin University.

$12 million grant puts WA research team in the hot seat to help wipe out malaria forever

A world-leading research team built to tackle malaria has relocated from Oxford University to Western Australia to take advantage of the state’s growing big data talent pool.

Performance characteristics and potential public health impact of improved pre-erythrocytic malaria vaccines targeting childhood burden

New malaria vaccine development builds on groundbreaking recommendations and roll-out of two approved pre-erythrocytic vaccines (PEVs); RTS,S/AS01 and R21/Matrix-M. Whilst these vaccines are effective in reducing childhood malaria within yearly routine immunization programs or seasonal vaccination, there is little evidence on how different PEV efficacies, durations of protection, and spacing between doses influence the potential to avert uncomplicated and severe childhood malaria. 

Estimating the potential malaria morbidity and mortality avertable by the US President's Malaria Initiative in 2025: a geospatial modelling analysis

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. 

Trends in malaria prevalence among school-age children in Mainland Tanzania, 2015-2023: A multilevel survey analysis

In high-transmission areas, school-aged children have higher malaria prevalence and contribute significantly to the transmission reservoir. Malaria infections can be asymptomatic or present with symptoms which may contribute to anaemia, severe illness and fatal malaria. This analysis provides trends of malaria prevalence and associated risk factors among school-aged children in mainland Tanzania. 

Malaria in Nepal: A Spatiotemporal Study of the Disease Distribution and Challenges on the Path to Elimination

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.