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Malaria is a deadly disease caused by Plasmodium spp. Several blood phenotypes have been associated with malarial resistance, which suggests a genetic component to immune protection.
Novel malaria vector control strategies targeting the odour-orientation of mosquitoes during host-seeking, such as 'attract-and-kill' or 'push-and-pull', have been suggested as complementary tools to indoor residual spraying and long-lasting insecticidal nets. These would be particularly beneficial if they can target vectors in the peri-domestic space where people are unprotected by traditional interventions.
The human landing catch (HLC) method, in which human volunteers collect mosquitoes that land on them before they can bite, is used to quantify human exposure to mosquito vectors of disease. Comparing HLCs in the presence and absence of interventions such as repellents is often used to measure protective efficacy (PE).
As both mechanistic and geospatial malaria modeling methods become more integrated into malaria policy decisions, there is increasing demand for strategies that combine these two methods. This paper introduces a novel archetypes-based methodology for generating high-resolution intervention impact maps based on mechanistic model simulations. An example configuration of the framework is described and explored.
We aimed to assess safety, tolerability, and Plasmodium vivax relapse rates of ultra-short course (3.5 days) high-dose (1 mg/kg twice daily) primaquine (PQ) for uncomplicated malaria because of any Plasmodium species in children randomized to early- or delayed treatment.
Half of all pregnancies at risk of malaria worldwide occur in the Asia-Pacific region, where Plasmodium falciparum and Plasmodium vivax co-exist. Despite substantial reductions in transmission, malaria remains an important cause of adverse health outcomes for mothers and offspring, including pre-eclampsia. Malaria transmission is heterogeneous, and infections are commonly subpatent and asymptomatic.
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.
Malaria risk maps are crucial for controlling and eliminating malaria by identifying areas of varying transmission risk. In the Greater Mekong Subregion, these maps guide interventions and resource allocation. This article focuses on analysing changes in malaria transmission and developing fine-scale risk maps using five years of routine surveillance data in Laos (2017-2021). The study employed data from 1160 geolocated health facilities in Laos, along with high-resolution environmental data.
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.
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.