Forecasting and evaluating intervention of Covid-19 in the World
Zixin Hu, Qiyang Ge, Shudi Li, Eric Boerwincle, Li Jin, and Momiao, Xiong

TL;DR
This study develops an AI-inspired auto-encoder model to forecast Covid-19 spread and evaluate intervention timing, demonstrating that delays significantly increase cases and deaths, emphasizing the importance of immediate public health actions.
Contribution
A modified auto-encoder model for real-time Covid-19 transmission forecasting and intervention evaluation, providing insights into the impact of intervention timing on epidemic outcomes.
Findings
Forecasting error of 2.5% for 5-step predictions
Delayed intervention increases peak cases by over 166 times
Earlier intervention shortens epidemic duration from 356 to 232 days
Abstract
When the Covid-19 pandemic enters dangerous new phase, whether and when to take aggressive public health interventions to slow down the spread of COVID-19. To develop the artificial intelligence (AI) inspired methods for real-time forecasting and evaluating intervention strategies to curb the spread of Covid-19 in the World. A modified auto-encoder for modeling the transmission dynamics of the epidemics is developed and applied to the surveillance data of cumulative and new Covid-19 cases and deaths from WHO, as of March 16, 2020. The average errors of 5-step forecasting were 2.5%. The total peak number of cumulative cases and new cases, and the maximum number of cumulative cases in the world with later intervention (comprehensive public health intervention is implemented 4 weeks later) could reach 75,249,909, 10,086,085, and 255,392,154, respectively. The case ending time was January…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCOVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Viral Infections and Outbreaks Research
