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

Abstract

There is evidence that rapid global climate instability and land-use change is contributing towards the increased migration of mosquitoes from the sub-tropical regions of sub-Saharan Africa and the Middle-East into temperate zone areas. This phenomenon has contributed to the increased potential of vector-borne disease - transmitted by various mosquito species such as Aedes aegypti, Aedes culex and spreading viruses such as dengue, chikungunya, West Nile and Zika - becoming a major public health threat in new areas like Brazil (which recently experienced an epidemic of microcephaly in babies). As a result of the recent climate changes, mosquito-borne diseases are becoming endemic in other parts of the world. The project will investigate the intersection between mosquito surveillance; public health; mobile and big data technology; biodiversity and climate modelling to increase our knowledge of the impacts of environmental changes on water providing breeding points for mosquitoes. The development of spatial-temporal models will be utilized extensively to predict the occurrence of mosquito breeding sites by using hierarchical Bayesian modelling and leveraging multiple data sources - climate and weather change patterns, mosquitoes' breeding points locations; local weather; urban water and sanitation infrastructure; socioeconomic variables and data from Internet of Things (IoT) weather monitoring devices. This will include mobile phone surveillance apps - for health professionals and for the general public - co-authored and co-developed by UCL researchers in collaboration with Brazilian local stakeholders from Recife, Olinda and Campina Grande, as well as end users who are involved with reporting locations of water breeding points. The project's interdisciplinary and transdisciplinary nature will involve a wide spectrum of complementary research activities that are, in turn, complementary to each other. It will build on models that will be used for datadriven decision to support an early warning system geographically predicting the entomological burden of mosquitos. This will be in real-time - a system that will alert public health, environmental and mosquito (or vector) control authorities in order to enable the allocation of resources to high risk areas, as well as to mobilize community engagement for prevention and rapid response to vector outbreaks thereby reducing the burden of human disease. In addition, we will develop educational content for the public to increase awareness of mosquito breeding points. With public health stakeholders in Northeast Brazil, we will co-develop and co-deliver community engagement strategies to improve standing and storm water management and biological treatment. Building on our long-term partnerships in Northeast Brazil, we will evaluate the system with stakeholders in urban and rural areas of Recife, Olinda and Campina Grande where mosquito-borne diseases are endemic. It will be a unique opportunity to not only validate the research findings in real-settings within the designated time frame of the project but also to translate the results to public health authorities, policymakers, project stakeholders WHO HQ (Geneva), and Recife City Hall (Brazil) and other countries where vector-borne diseases are soon to become endemic. (AU)