When will COVID-19 End? Answer from Data-Driven Innovation Lab
The University of Singapore has done some interesting research on the timing of the end of the coronavirus
Singapour university of technology and design -Data-Driven Innovation Lab-Artificial Intelligence for Innovation.
The Data-Driven Innovation Lab’s site provides continuous predictive monitoring of COVID-19 developments as a complement to monitoring confirmed cases. SIR (susceptible-infected-recovered) model is regressed with data from different countries to estimate the pandemic life cycle curves and predict when the pandemic might end in respective countries and the world, with codes from Milan Batista and data from Our World in Data. The only precautions with goodness-of-fit R^2 > 0.8 and statistical significance (p-value < 0.01) are reported. The predictions are expected to change with changing real-world scenarios over time and are updated daily with the latest data. Motivation, theory, method, and caution are in this paper.
Disclaimer: Content from this website is STRICTLY ONLY for educational and research purposes and may contain errors. The model and data are inaccurate to the complex, evolving, and heterogeneous realities of different countries. Predictions are uncertain by nature. Readers must take any predictions with caution. Over-optimism based on some predicted end dates is dangerous because it may loosen our disciplines and controls and cause the turnaround of the virus and infection, and must be avoided.
You can find this answer in the below link:
Jianxi Luo is a tenured Associate Professor with the Singapore University of Technology and Design, Director of Data-Driven Innovation Lab, and Director of SUTD Technology Entrepreneurship Program. Prof. Luo holds a Ph.D. in Engineering Systems (Technology Management and Policy track) and an S.M. degree in Technology Policy from Massachusetts Institute of Technology, and M.S. and B.E. degrees in Engineering from Tsinghua University. Prior to joining SUTD, he had been a faculty member at New York University, visiting scholar at Columbia University and the University of Cambridge. He was Chair of the INFORMS Technology Innovation Management & Entrepreneurship Section. He is currently on the editorial boards of Design Science (Associate Editor), Research in Engineering Design, IEEE Transactions on Engineering Management, among other journals.
His research fuses design science, network science, and artificial intelligence to push the frontiers of data-driven design and create artificial intelligence, for more informed, inspired, and creative decisions in engineering design, innovation management, and policy. He has published over 120 academic articles and given >70 invited talks at >50 universities, companies, organizations, and governments around the world. His research has received more than a dozen awards from Design Society, ASME Design Engineering Division, INFORMS, Complex System Society among others, including SUTD “Excellence in Research” Award 2018.
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Data-Driven Innovation Lab-Artificial Intelligence for Innovation