AUTHOR=Hu Zixin , Ge Qiyang , Li Shudi , Boerwinkle Eric , Jin Li , Xiong Momiao 

TITLE=Forecasting and Evaluating Multiple Interventions for COVID-19 Worldwide

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 3 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2020.00041

DOI=10.3389/frai.2020.00041

ISSN=2624-8212

ABSTRACT=As the Covid-19 pandemic surges around the world, questions arise about the number of cases worldwide at its peak, the length of the pandemic before receding, and the timing of intervention strategies to significantly stop the spread of Covid-19.  We developed the artificial intelligence (AI) inspired methods for modeling the transmission dynamics of the epidemics  and evaluating interventions  to curb the spread and impact of COVID-19.  The developed methods were applied to the surveillance data of cumulative and new COVID-19 cases and deaths reported by WHO, as of March 16, 2020. Both the timing and the degree of intervention were evaluated. Average error of  5-step ahead forecasting was 2.5%. The total peak number of cumulative cases and new cases, and the maximum number of cumulative cases in the world with complete intervention implemented four weeks later than the beginning date (March 16, 2020) reached 75,249,909, 10,086,085, and 255,392,154, respectively. However, the total peak number of cumulative cases and new cases, and the maximum number of cumulative cases in the world with complete intervention after one week were reduced to 951,799, 108,853 and 1,530,276, respectively.  Duration time of the COVID-19 spread was reduced from 356 days to 232 days between later and earlier interventions.  We observed that delaying intervention for one month caused the maximum number of cumulative cases -166.89 times that of earlier complete intervention, and the number of deaths increase from 53,560 to 8,938,725. Earlier and complete intervention is necessary to stem the tide of COVID-19 infection.