Predicting Future Mosquito Larval Habitats Using Time Series Climate Forecasting and Deep Learning

The research described in this article was divided into three phases. The first phase involved gathering meteorological data Mosquito habitats and breeding ranges are expanding globally and larvae counts from various locations in the United States [1]. Habitat preferences are based on the interaction and using this data set to create a predictive model for mosquito of several factors, including temperature, humidity, rainfall, larvae abundance. The second phase involved extracting time elevation, and availability of hosts. Climate change has been series sequences of the said meteorological variables for identified as a key driving factor for the shifts in mosquito specific regions of interest, to allow for the forecasting of distribution over the past 70 years and is likely to continue to environmental conditions. The third phase involved feeding be the chief determinant of mosquito population spread [1].