Aedes aegypti is still one of the main concerns when it comes to disease vectors. Among the many ways to deal with it, there are important protocols that make use of egg numbers in ovitraps to calculate indices, such as the LIRAa and the Breteau Index, which can provide information on predictable outbursts and epidemics. Also, there are many research lines that require egg numbers, specially when mass production of mosquitoes is needed. Egg counting is a laborious and error-prone task that can be automated via computer vision-based techniques, specially deep learning-based counting with object detection. In this work, we propose a new dataset comprising field and laboratory eggs, along with test results of three neural networks applied to the task: Faster R-CNN, Side-Aware Boundary Localization and FoveaBox.

Millions of people around the world are infected with mosquito-borne diseases each year. One of the most dangerous species is Aedes aegypti, the main vector of viruses such as dengue, yellow fever, chikungunya, and Zika, among others. Mosquito prevention and eradication campaigns are essential to avoid major public health consequences. In this respect, entomological surveillance is an important tool. At present, this traditional monitoring tool is executed manually and requires digital transformation to help authorities make better decisions, improve their planning efforts, speed up execution, and better manage available resources. Therefore, new technological tools based on proven techniques need to be designed and developed. However, such tools should also be cost-effective, autonomous, reliable, and easy to implement, and should be enabled by connectivity and multi-platform software applications. This paper presents the design, development, and testing of an innovative system named MosquIoT. It is based on traditional ovitraps with embedded Internet of Things (IoT) and Tiny Machine Learning (TinyML) technologies, which enable the detection and quantification of Ae. aegypti eggs. This innovative and promising solution may help dynamically understand the behavior of Ae. aegypti populations in cities, shifting from the current reactive entomological monitoring model to a proactive and predictive digital one.

SAN FRANCISCO – Silicon Valley researchers are attacking flying bloodsuckers in California's Fresno County. A white high-top Mercedes van winds its way through the suburban sprawl and strip malls as a swarm of male Aedes aegypti mosquitoes shoot out of a black plastic tube on the passenger-side window. These pests are tiny and, with a wingspan of just a few millimeters, all but invisible. "You hear that little beating sound?" says Kathleen Parkes, a spokesperson for Verily Life Sciences, a unit of Alphabet. Jacob Crawford, a Verily senior scientist riding with Parkes, begins describing a mosquito-control technique with dazzling potential.

Verily partnered with MosquitoMate, a Kentucky-based sterile mosquito breeder, and Fresno's local authority, the Consolidated Mosquito Abatement District, to release a million insects a week through the end of 2017. His team used a sieve-like device to sort the sexes by size in their pupal stage, and then gave each mosquito another look under a microscope before shipping them from Kentucky to California, where district officials released the bugs by hand, shaking them out of cardboard tubes. If this summer's trial proves effective, Mulligan and Dobson hope they could expand to other places in California and wipe out the existing pockets of Aedes aegypti before they become a permanent feature of the landscape. Its ultimate goal is to be able to make more mosquito factories, ready to ship all over the world whenever a new mosquito-borne disease strikes.