A technology company transforming vector surveillance

Mosquito borne disease: a modern public health threat that calls for modern solutions.

Mosquitoes are the deadliest animal in the world [Gates Notes], vectors for diseases like malaria, Dengue, and Zika. Though Malaria incidence has been significantly reduced by a concerted global effort, reduction has slowed in recent years [World Malaria Report 2019]. Dramatically, Dengue has grown 30 fold in the past 50 years, and with up to 390 million annual infections [Ebi et. al.]. This growth is a result of climate change, globalization, and mosquitoes' ability to rapidly evolve insecticide resistance.

              The writing is on the wall - we need better tools.

While vector surveillance is necessary to properly manage mosquito populations and disease risk, current methods are resource, labor, and time intensive. Collecting even limited amounts of data can take days to weeks, causing control efforts to occur well after an outbreak has begun. Additionally, the expertise required to perform mosquito surveillance, such as that required to identify a specimen's species, limits it implementation even in high income countries like the United States [NACCHO Report].

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Limitations of Current Practice

VecTech believes in accessible and reliable mosquito population data to empower mosquito borne disease prevention.

Over the past three years, our team at the Center for Bioengineering Innovation and Design at Johns Hopkins University has been developing computer vision algorithms to identify a mosquito specimen's species from an image.

 

These algorithms have potential to revolutionize mosquito control practice by reducing the cost to perform surveillance, reducing the expertise required to identify specimens, and providing more standardized mosquito species data.

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