Page 37 - IFIA_Magazine_No11

IFIA Magazine - Mar ch 2021 |

predictive monitoring of COVID19- 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^0.8 > 2 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.

Jianxi Luo is a tenured Associate Professor with the Singapore University of
Technology and Design, Director of the 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.

Source:
Data-Driven Innovation Lab-Artificial Intelligence for Innovation

37