2. Summary

A map demonstrating the disproportionate North-South divide of VBD deaths

Vector-borne diseases cause more than 700,000 deaths annually, and account for more than 17% of all infectious diseases. In today’s pandemic-ridden society, disease prevention and control has become all the more prevalent. However, while the threat of disease has increased in recent years, technologies’ preventive measures have also developed in equal measure. Contact tracing, telemedicine, face detection for the unmasked, all of these help reduce infection rates. Covid-19 is not the first time there has been a pandemic outbreak, nor will it be the last. According to WHO, there was an estimated 229 million cases of malaria worldwide. Malaria is carried by the female Anopheles mosquito, which transmits the virus (aka the vector) to humans by biting them. Malaria is not the only source of vector-borne diseases, unfortunately. Other diseases include the Aedes mosquito which carries the Zika virus, ticks which carry Lyme disease, and fleas, which caused the infamous Bubonic Plague. My project aims to use image recognition through Neural Networks to correctly identify harmful insects that may carry disease. While researching for the project, I noticed that the majority of the death rates of VBDs were in the Developing World. As many people would be unable to access or afford proper medical care, I hope that the population would be able to correctly identify whether the insect was a carrier or not based on its image, thereby causing reason for possible caution. Another potential application (such as in mosquito alert for aedes mosquitos) would be to use crowdsourcing and mobile applications to build a live map of vector carriers, thus raising community awareness of areas of possible concern, containing a reporting system of bites, vector carriers, and breeding sites.