Statistics and Its Interface

Volume 14 (2021)

Number 1

A novel intervention recurrent autoencoder for real time forecasting and non-pharmaceutical intervention selection to curb the spread of Covid-19 in the world

Pages: 37 – 47

DOI: https://dx.doi.org/10.4310/SII.2021.v14.n1.a10

Authors

Qiyang Ge (School of Mathematical Sciences, Fudan University, Shanghai, China)

Zixin Hu (State Key Laboratory of Genetic Engineering and Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China; and Human Phenome Institute, Fudan University, Shanghai, China)

Shudi Li (Department of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center, Houston, Tx., U.S.A.)

Wei Lin (School of Mathematical Sciences, SCMS, and SCAM, Fudan University, Shanghai, China)

Li Jin (State Key Laboratory of Genetic Engineering and Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China; and Human Phenome Institute, Fudan University, Shanghai, China)

Momiao Xiong (Department of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center, Houston, Tx., U.S.A.)

Abstract

As the Covid-19 pandemic soars around the world, there is urgent need to forecast the number of cases worldwide at its peak, the length of the pandemic before receding and implement public health interventions to significantly stop the spread of Covid-19. Widely used statistical and computer methods for modeling and forecasting the trajectory of Covid-19 are epidemiological models. Although these epidemiological models are useful for estimating the dynamics of transmission od epidemics, their prediction accuracies are quite low. To overcome this limitation, we formulated the real-time forecasting and evaluating multiple public health intervention problem into forecasting treatment response problem and developed recurrent neural network (RNN) for modeling the transmission dynamics of the epidemics and Counterfactual-RNN (CRNN) for evaluating and exploring public health intervention strategies to slow down the spread of Covid-19 worldwide. We applied the developed methods to the real data collected from January 22, 2020 to May 8, 2020 for real-time forecasting the confirmed cases of Covid-19 across the world.

Keywords

Covid-19, recurrent neural networks, artificial intelligence, time series, causal inference, forecasting

Received 14 May 2020

Accepted 29 June 2020

Published 18 December 2020