| Id | 2852 | |
| Author | Yang L.; Iwami M.; Chen Y.; Wu M.; van Dam K.H. | |
| Title | Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review | |
| Reference | Yang L.; Iwami M.; Chen Y.; Wu M.; van Dam K.H. Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review,Progress in Planning 168 |
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| Link to article | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126517488&doi=10.1016%2fj.progress.2022.100657&partnerID=40&md5=f2035b13e381458870a754737d639a06 |
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| 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, impelling behaviour change and facilitating the construction of lower risk buildings and public spaces. Computational tools, including computer simulation, statistical models, and artificial intelligence, have been used to support responses to the current pandemic as well as to the spread of 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. We selected 109 out of 8,737 studies retrieved from databases and analysed them 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 support, including specific models and parameters. The proposed toolkit will help designers, planners, and computer modellers to select relevant approaches for evaluating design decisions depending on the target disease, geographic context, design stages, and spatial and temporal scales. The findings herein can be regarded as stand-alone tools, particularly for fighting against COVID-19, or be incorporated into broader frameworks to help cities become more resilient to future disasters. © 2022 Elsevier Ltd |
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| Keywords | artificial intelligence; COVID-19; decision support system; infectious disease; resilience; urban design; urban planning |
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