Mosquitoes populations modelling for early warning system and rapid response public by health authorities correlating climate, weather and spatial-tem

As a result of the recent climate changes, mosquito-borne diseases (like Zika, dengue) are becoming endemic not only in sub-tropical regions of Africa and Latin America but in other parts of the world. This project will combine public health, mobile technology and climate modelling to evaluate the impacts of environmental changes on water providing breeding habitats for mosquitoes in Northeast Brazil. We aim to develop a series of spatial-temporal models to predict the burden of mosquito populations by deploying cutting-edge mobile and internet of things (IoT) technology leveraging multiple data sources from newly acquired climate, weather, mosquito surveillance, water and sanitation and socioeconomic data. This technology will include the use of mobile surveillance apps using gamification and citizen science technology co-developed with local stakeholders for reporting locations of water breeding points in Brazil. We will develop a data-driven early warning system to predict changes in occurrence and abundance of mosquito breeding points. This real-time system will alert public health and environmental authorities to mobilise community engagement for the prevention and rapid response to vector outbreaks. We will also develop educational content for public and community stakeholders to increase awareness of mosquito breeding habitats and water management. With public health stakeholders (WHO and Recife City Hall), we will co-develop community engagement strategies and evidence-based policies to improve standing water management and treatment. Most importantly, building on existing partnerships in the provinces in Northeast Brazil, where mosquito-borne diseases are endemic, we will work with academics and local stakeholder partners from Recife, Olinda and Campina Grande, and have a unique access to mosquito surveillance data to calibrate our predictive models in real-time via mobile app and IoT devices. Access to real-time datasets will not only provide a unique method for calibrating the predictive modelling results ? but also will put us in a position to evaluate the entire early-warning decision-support dashboard system with the authorities during their standard daily operations to ensure outstanding real-world impact on vector surveillance and public health policy. It is absolutely unique for a research project to have the opportunity to validate the research in the timeframe of the project while directly translating the results to public health authorities, policy makers, WHO, and stakeholders in Brazil, Turkey and other countries where vector-borne disease are soon to become endemic.