2020/05/27 by Risto Miikkulainen, Miikkulainen, Risto, Olivier Francon +9
Computer Science · Economics, Econometrics and Finance · Mathematics · #Artificial Intelligence (cs.AI) #COVID-19 Pandemic Impacts #COVID-19 epidemiological studies #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #cs.AI #cs.LG #cs.NE
paper · pdf · doi:10.48550/arxiv.2005.13766
openalex publication_date 2020/05/27 · arxiv created 2020/08/01 · arxiv updated 2020/08/04 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with non-pharmaceutical interventions (NPIs) such as social distancing restrictions and school and business closures. This paper demonstrates how evolutionary AI could be used to facilitate the next step, i.e. determining most effective intervention strategies automatically. Through evolutionary surrogate-assisted prescription (ESP), it is possible to generate a large number of candidate strategies and evaluate them with predictive models. In principle, strategies can be customized for different countries and locales, and balance the need to contain the pandemic and the need to minimize their economic impact. While still limited by available data, early experiments suggest that workplace and school restrictions are the most important and need to be designed carefully. It also demonstrates that results of lifting restrictions can be unreliable, and suggests creative ways in which restrictions can be implemented softly, e.g. by alternating them over time. As more data becomes available, the approach can be increasingly useful in dealing with COVID-19 as well as possible future pandemics.