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. 2020 May 22:3:41.
doi: 10.3389/frai.2020.00041. eCollection 2020.

Forecasting and Evaluating Multiple Interventions for COVID-19 Worldwide

Zixin Hu  1   2 , Qiyang Ge  3 , Shudi Li  4 , Eric Boerwinkle  4 , Li Jin  1   2 , Momiao Xiong  4
Affiliations

Forecasting and Evaluating Multiple Interventions for COVID-19 Worldwide

Zixin Hu et al. Front Artif Intell. .

Abstract

As the Covid-19 pandemic surges around the world, questions arise about the number of global cases at the pandemic's peak, the length of the pandemic before receding, and the timing of intervention strategies to significantly stop the spread of Covid-19. We have developed 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 16th, 2020. Both the timing and the degree of intervention were evaluated. The average error of five-step ahead forecasting was 2.5%. The total peak number of cumulative cases, new cases, and the maximum number of cumulative cases in the world with complete intervention implemented 4 weeks later than the beginning date (March 16th, 2020) reached 75,249,909, 10,086,085, and 255,392,154, respectively. However, the total peak number of cumulative cases, new cases, and the maximum number of cumulative cases in the world with complete intervention after 1 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 1 month caused the maximum number of cumulative cases reduce by -166.89 times that of earlier complete intervention, and the number of deaths increased from 53,560 to 8,938,725. Earlier and complete intervention is necessary to stem the tide of COVID-19 infection.

Keywords: COVID-19; artificial intelligence; auto-encoder; forecasting; time series; transmission dynamics.

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Figures

Figure 1
Figure 1
Architecture of modified autoencoder which consisted of two single AE. Each single AE was a three-layer feedforward neural network.
Figure 2
Figure 2
Trajectory of COVID-19 in the seven most infected countries—Iran, Spain, Italy, Germany, USA, France and China as a function of days from January 21st to June 19th, 2020. (A,C,E,G) Forecasted curves of the newly confirmed cases of COVID-19 under scenarios 1, 2, 3, and 4, respectively. (B,D,F,H) Forecasted curves of the cumulative confirmed cases of COVID-19 under scenarios 1, 2, 3, and 4, respectively.
Figure 3
Figure 3
The reported and forecasted curves of the cumulative and new confirmed cases of Covid-19 in the world as a function of days from January 20th, to July 28th, 2020.
Figure 4
Figure 4
Time-case plot of the top seven infected countries: Iran, Spain, Italy, Germany, USA, France and China. (A) Time-case plot under intervention scenario 1; (B) Time-case plot under intervention scenario 2; (C) Time-case plot under intervention scenario 3 and (D) Time-case plot under intervention scenario 4.
Figure 5
Figure 5
Case-fatality curve for the world.
See this image and copyright information in PMC

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