Using digital twins to understand environmental health impacts

Abstract

The growth of Internet of Things (IoT) and wearable sensors has advanced digital twin (DT) technology in urban planning, utility management, and healthcare by providing real-time data. Researchers, public health officials, and first responders can leverage DTs to understand the complex interactions between people and the environment and to model the impacts of the built environment and population density on local temperature and air quality, as well as the health impacts of these exposures. The combination of environmentally aware human DTs and smart cities (including agricultural land) could provide better strategies for forecasting vector-borne or environmentally driven diseases, which are predicted to increase over the next decades because of warmer temperatures and more contact between people, animals, and insects caused by higher population densities. This research brief discusses the current state of environmentally aware DTs and highlights areas where the technology needs to improve, such as the need to establish data and metadata standards for IoT and wearable sensors, the use of edge computing resources to achieve scaling and rapidly communicate results better, and access to labeled data. Improving environmentally aware DT technology could help establish more effective forecasting and mitigation strategies to reduce the loss of human life from natural disasters and disease outbreaks.