Discovering optimal strategies for mitigating COVID-19 spread using machine learning: Experience from Asia

Yue Pan; Limao Zhang; Zhenzhen Yan; May O Lwin; Miroslaw J Skibniewski

doi:10.1016/j.scs.2021.103254

Discovering optimal strategies for mitigating COVID-19 spread using machine learning: Experience from Asia

Sustain Cities Soc. 2021 Dec:75:103254. doi: 10.1016/j.scs.2021.103254. Epub 2021 Aug 13.

Authors

Yue Pan^{1

2}, Limao Zhang¹, Zhenzhen Yan³, May O Lwin⁴, Miroslaw J Skibniewski⁵

Affiliations

¹ School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore.
² Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Department of Civil Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, China.
³ School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore.
⁴ Wee Kim Wee School of Communication and Information, Nanyang Technological University, 31 Nanyang Link, WKWSCI Bldg, 637718, Singapore.
⁵ Department of Civil and Environmental Engineering, University of Maryland, College Park, 9 MD 20742-3021, USA.

Abstract

To inform data-driven decisions in fighting the global pandemic caused by COVID-19, this research develops a spatiotemporal analysis framework under the combination of an ensemble model (random forest regression) and a multi-objective optimization algorithm (NSGA-II). It has been verified for four Asian countries, including Japan, South Korea, Pakistan, and Nepal. Accordingly, we can gain some valuable experience to better understand the disease evolution, forecast the prevalence of the disease, which can provide sustainable evidence to guide further intervention and management. Random forest with a proper rolling time-window can learn the combined effects of environmental and social factors to accurately predict the daily growth of confirmed cases and daily death rate on a national scale, which is followed by NSGA-II to find a range of Pareto optimal solutions for ensuring the minimization of the infection rate and mortality at the same time. Experimental results demonstrate that the predictive model can alert the local government in advance, allowing the accused time to put forward relevant measures. The temperature in the category of environment and the stringency index belonging to the social factor are identified as the top 2 important features to exert a greater impact on the virus transmission. Moreover, optimal solutions provide references to design the best control strategies towards pandemic containment and prevention that can accommodate the country-specific circumstance, which are possible to decrease the two objectives by more than 95%. In particular, appropriate adjustment of social-related features needs to take priority over others, since it can bring about at least 1.47% average improvement of two objectives compared to environmental factors.

Keywords: COVID-19; Feature importance analysis; Multi-objective optimization; Random forest regression; Sustainability.