Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review.

ABSTRACT

The COVID-19 pandemic highlighted the need for decision-support tools to help cities become more resilient to infectious diseases. Through urban design and planning, non-pharmaceutical interventions can be enabled, nudging behaviour change and facilitating lower risk buildings and public spaces. Computational tools, including computer simulation, statistical models, and artificial intelligence, were used to support responses in the current pandemic as well as to the previous infectious diseases. Our multidisciplinary research group systematically reviewed state-of-the-art literature to propose a toolkit that employs computational modelling for various interventions and urban design processes. From 8,737 records returned from databases, 109 records were selected and analysed based on the pathogen type, transmission mode and phase, design intervention and process, as well as modelling methodology (method, goal, motivation, focus, and indication to urban design). We also explored the relationship between infectious disease and urban design as well as computational modelling supports, including specific models and parameters. The proposed toolkit will help designers, planners, and computer modellers to select relevant approaches to evaluate and consider design decisions depending on the disease, geographic context, design stages, and spatial and temporal scales. The findings herein can be regarded as stand-alone tools, particularly for COVID-19 or be incorporated into broader frameworks to help cities become more resilient to future disasters.