Project Title: Risk Assessment of Mosquito-borne Diseases in Dar es salaam, Tanzania Based on Deep Learning and Remote sensing
Project Description: This project will apply deep learning in research domains of epidemiology, architecture, and remote sensing on multi-scale data, spanning from the level of individual household to the metropolitan level to identify risk areas of mosquito-borne diseases in East African cities. The urban malaria vector Anopheles stephensi recently started spreading across East Africa, posing a major health risk to urban populations. while the links between housing conditions and mosquitos-borne diseases are increasingly recognized, the relation between... This project will apply deep learning in research domains of epidemiology, architecture, and remote sensing on multi-scale data, spanning from the level of individual household to the metropolitan level to identify risk areas of mosquito-borne diseases in East African cities. The urban malaria vector Anopheles stephensi recently started spreading across East Africa, posing a major health risk to urban populations. while the links between housing conditions and mosquitos-borne diseases are increasingly recognized, the relation between attributes of urban environments and vector-borne diseases remains understudies, decreasing the efficacy of the measures to address health issues in African cities. This project will address the aforementioned knowledge-gap by investigating the relationship between mosquito densities in households and architectural, ecological, urban form variables inn Dar es salaam, Tanzania. The study will include household surveys of mosquito densities and housing conditions as well as high-resolution geospatial surveys of urban areas. The study will conduct statistical data analyses of the links between mosquito densities in the household and indicators delivered from the household and neighborhood surveys. The findings will be utilized in automated analysis of multispectral satellite remote sensing imagery based on deep learning models with manually delineated attributes of the urban environments to identify risk areas of mosquito-borne diseases at an unprecedented details. The productive performance of the risk assessment model will be evaluated and the results will be used to guide policy priorities and interventions in addressing mosquito-borne diseases. The research will be integrated in teaching programs at the Royal Danish Academy and the University of Copenhagen. The project thus has potential to address important knowledge gaps, enhance urban resilience in Africa, and strengthen Danish biomedical data science capacities
Principal Investigator : Yeromin Mlacha
Department Name :
Time frame: (2022-01-01) - (2026-12-31)
None added yet.
External Collaborating Partners
London School of Hygiene and Tropical Medicine (LSHTM)
University of Copenhagen