ABSTRACT
Objective Develop the AI and casual inference-inspired methods for forecasting and evaluating the effects of public health interventions on curbing the spread of Covid-19.
Methods We 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 real-time forecasting the confirmed cases of Covid-19 across the world. The data were collected from January 22 to April 18, 2020 by John Hopkins Coronavirus Resource Center (https://coronavirus.jhu.edu/MAP.HTML).
Results The average errors of 1-step to 10-step forecasting were 2.9%. In the absence of a COVID-19 vaccine, we evaluated the potential effects of a number of public health measures. We found that the estimated peak number of new cases and cumulative cases, and the maximum number of cumulative cases worldwide with one week later additional intervention were reduced to 103,872, 2,104,800, and 2,271,648, respectively. The estimated total peak number of new cases and cumulative cases would be the same as the above and the maximum number of cumulative cases would be 3,864,872 in the world with 3 week later additional intervention. Duration time of the Covid-19 spread would be increased from 91 days to 123 days. Our estimation results showed that we were in the eve of stopping the spread of COVID-19 worldwide. However, we observed that transmission would quickly rebound if interventions were relaxed.
Conclusions The accuracy of the AI-based methods for forecasting the trajectory of Covid-19 was high. The AI and causal inference-inspired methods are a powerful tool for helping public health planning and policymaking. We concluded that the spread of COVID-19 would be stopped very soon.
Highlights
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.
Develop artificial intelligence (AI) and causal inference inspired methods for real-time forecasting and evaluation of interventions on the worldwide trajectory of the spread of Covid-19.
We estimated the maximum number of cumulative cases under immediate additional intervention to be 2,271,648; under later additional intervention the number increased to 3,864,872 and the case ending time would be May 25, 2020.
Without additional intervention, the spread of COVID-19 would be stopped on July 6, 2020.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
Dr. Wei Lin is supported by the National Key R&D Program of China (Grant no. 2018YFC0116600), by the National Natural Science Foundation of China (Grant nos. 11925103 & 61773125), and by the STCSM (Grant no. 18DZ1201000).
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All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.
Yes
All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.
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Paper in collection COVID-19 SARS-CoV-2 preprints from medRxiv and bioRxiv
The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, University of Washington, and Vrije Universiteit Amsterdam.
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