Hamza Tazi Bouardi

Hamza Tazi Bouardi

Currently Data Scientist @ BCG GAMMA, former graduate student at MIT (Master of Business Analytics 2020) under the supervision of Prof. Dimitris Bertsimas, and at École Centrale Paris (BS General Engineering 2017, MS Applied Mathematics 2019).

Publications

Optimizing Vaccine Allocation to Combat the COVID-19 Pandemic

Published in ArXiV, 2020

Paper describing a coordinate descent algorithm that iterates between optimizing vaccine allocations (e.g. based on the population’s risk classes) and simulating the dynamics of the pandemic (using an extension of the DELPHI epidemiological model) in order to reduce the number of deaths given a certain allocated budget.

Recommended citation: D. Bertsimas et al. (2020), Optimizing Vaccine Allocation to Combat the COVID-19 Pandemic, under review. http://academicpages.github.io/files/paper3.pdf

Trauma Outcome Predictor

Published in ArXiV (under review), 2020

Paper leveraging Optimal Classification Trees (D. Bertsimas and J. Dunn, 2017) and K-NN Optimal Imputation (D. Bertsimas, C. Pawlowski and Y.D. Zhuo, 2018) methods on a dataset with EHR information and 1.2M+ rows in Julia and Python to predict mortality and morbidity for trauma patients with 0.93 and 0.77 AUROC (C-Statistic).

Recommended citation: D. Bertsimas et al. (2020), Trauma Outcome Predictor, 34th Eastern Association for the Surgery of Trauma Annual Scientific Assembly. http://academicpages.github.io/files/paper3.pdf

From Predictions to Prescriptions: A Data-Driven Response to COVID-19

Published in Health Care Management Science (currently under review), 2020

Paper gathering the whole CovidAnalytics.io research effort, with applications of Operations Research and Management Science spanning personalized medicine, optimal resource allocation, and epidemiological modeling.

Recommended citation: D. Bertsimas et al. (2020), From Predictions to Prescriptions: A Data-Driven Response to COVID-19, Health Care Management Science (under review). https://www.medrxiv.org/content/10.1101/2020.06.26.20141127v1.full.pdf

Forecasting COVID-19 and Analyzing the Effect of Government Interventions

Published in Operations Research (major revision), 2020

Paper describing and analyzing the performance and predictions of the DELPHI (Differential Equations Lead to Predictions of Hospitalizations and Infections) epidemiological model for COVID-19. This paper also analyzes the effect of government interventions, as it is a central part of the model and its extensions.

Recommended citation: M. Li, H. Tazi Bouardi et al. (2020), Forecasting COVID-19 and Analyzing the Effect of Government Interventions, Operations Research (major revision). https://www.medrxiv.org/content/10.1101/2020.06.23.20138693v1.full.pdf