סמינר המחלקה להנדסת תעשייה

A Multilayer Model for Early Detection of COVID-19
Ronen Mansuri is a M.Sc. candidate at the Department of Industrial Engineering,
Tel Aviv University
The lecture will beheldon
Tuesday, April 20 , 2021, at 14:00 Via Zoom
https://us02web.zoom.us/j/81256590339?pwd=c0ZFTmVOSEM3elNlNEVwMUpUL3hvdz09

Abstract
Current efforts for COVID-19 screening mainly rely on reported symptoms and potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that utilizes four layers of information:

1) sociodemographic characteristics of the tested individual, 2) spatiotemporal patterns of the disease observed near the testing episode, 3) medical condition and general health consumption of the tested individual over the past five years, and 4) information reported by the tested in ividual during the testing
episode.
We evaluated our model on 140.7K members of Maccabi Health Services, tested for COVID-19 at least once between February and October 2020. These individuals had 264.5K COVID-19 PCR tests, out of which 16.5K
were found positive. Our multilayer model obtained an area under the curve (AUC) of 81.6% when tested over all individuals, and of 72.8% when tested over individuals who did not report any symptom. Furthermore,
considering only information collected before the testing episode – that is, before the individual may had the chance to report on any symptom – our model could reach a considerably high AUC of 79.5%. Namely, most of
the value contributed by the testing episode can be gained by earlier information. Our ability to predict early the outcomes of COVID-19 tests is pivotal for breaking transmission chains and can be utilized for a more efficient
testing policy.

Bio
Ronen is a MSc candidate at the department of Industrial Engineering in Tel Aviv University, specializing in Personalized Medicine. Ronen holds a B.A. degree in Statistics from the Hebrew University in Jerusalem and MBA in Operational Research and Marketing from Tel Aviv University. Before joining the department, Ronen worked 18 years in various positions in the industry. He worked as a statistical consultant in SAS Institute, Marketing Analyst in Cellcom and in his last role, he was the Head of Global Clinical Programming in Teva Pharmaceuticals for 12 years. His research focuses on Personalized Medicine and Big Data Analytics collected
from Electronical Medical Records, smart phones, smart watches and self-reported daily questionnaires. The research is being supervised by Dr. Dan Yamin.