Using Machine Learning to Improve Vector Control, Public Health and Reduce Fragmentation of Urban Water Management

Abstract

Urban waters (UW) are complex environments, and their definition is related to water systems in urban zones, whether in a natural system or an urban facility. The health of these environments is related to public health and the quality of life because public health is the focal point of environmental and anthropic impacts. Infrastructure is paramount for maintaining public health and social and economic development sanitation. Insufficient infrastructure favors disease vectors. The population and environment suffer from deficient urban water infrastructure in Brazil despite government efforts to manage the existing systems. In this work, machine learning (regression trees) demonstrates the deficiency of sanitation and UW management fragmentation on public health by using the Aedes aegypti infestation index (HI) and water supply, wastewater, stormwater and drainage indicators (SNIS data). The results show that each Brazilian region faces different problems. The more infested regions were Northeastern, Northern and Southeastern. Moreover, municipalities with better SNIS data have lower infestation rates. Minimizing problems related to sanitation through the integrated management of water and urban areas is extremely important in developing countries. UW governance is connected to public health. Water management fragmentation leads to more complex issues, and managers must confront them to improve the quality of life in urban zones.