The podcast explores the importance of advocacy for malaria research and control. It follows over 120 advocates gathering in Washington, DC, as part of the ‘United to Beat Malaria’ campaign, urging Congress to continue supporting global malaria efforts.

Key topics include:

• The US President’s Malaria Initiative (PMI), founded in 2005, which provides bed nets, test kits, and treatments to combat malaria

• The role of global partnerships, including the Global Fund, in distributing resources efficiently.

• How Uganda’s malaria response is supported by international funding for the dissemination of key public health interventions.

• The importance of sustained funding for malaria research, with US agencies like NIH, CDC, and PMI contributing to vaccine development and disease surveillance.
  
  

Featuring: Margaret Reilly McDonnell (United to Beat Malaria), Dr David Walton (formerly PMI), Dr Jimmy Opigo (Uganda National Malaria Control Program), Jamie Bay Nishi (ASTMH) and Ed Royce (former House Foreign Affairs Committee (HFAC) Chairman).

With a shortage of entomologists in malaria-endemic regions, could AI fill the gap? We explore VectorCam, an offline tool powered by a Convolutional Neural Network that aims to support local vector surveillance.

with Dr. Soumya Acharya and Sunny Patel of Johns Hopkins University.

About The Podcast

The Johns Hopkins Malaria Minute is produced by the Johns Hopkins Malaria Research Institute to highlight impactful malaria research and to share it with the global community.

Can AI identify mosquito species? VectorCAM, a pocket-sized device, uses machine learning to differentiate species with 95% accuracy, enhancing malaria surveillance efforts

Transcript

Not all mosquitoes are created equal. Of the more than three thousand species, only a limited number of the Anopheles genus can transmit malaria. Even within that subset, subtle physiological differences affect how malaria spreads. Some mosquitoes prefer to bite indoors, while others outdoors. Some need large bodies of water to breed, while others only need a small puddle. Distinguishing these species is critical for effective malaria control—whether using bed nets, indoor spraying, or outdoor larval management. But identifying them by eye takes expert, entomological knowledge. Could AI help? The VectorCAM team at Johns Hopkins is working on just that. Their pocket-sized device uses a small light and magnifying lens, allowing a phone camera to capture close-up images of mosquitoes placed on slides. With up to 95% accuracy, it can identify mosquito species based on morphology in seconds. The hope is that VectorCAM will help health teams better understand mosquito populations, paving the way for more targeted and relevant malaria control efforts.

Source

Towards transforming malaria vector surveillance using VectorBrain: a novel convolutional neural network for mosquito species, sex, and abdomen status identifications (Scientific Reports)

About The Podcast

The Johns Hopkins Malaria Minute podcast is produced by the Johns Hopkins Malaria Research Institute to highlight impactful malaria research and to share it with the global community.