Optimizing Influenza Vaccine Composition: From Predictions to Prescriptions

Hari Bandi, Dimitris Bertsimas
Proceedings of the 5th Machine Learning for Healthcare Conference, PMLR 126:121-142, 2020.

Abstract

We propose a holistic framework based on state-of-the-art methods in Machine Learning and Optimization to prescribe influenza vaccine composition that are specific to a region, or a country based on historical data concerning the rates of circulation of predominant viruses. First, we develop a tensor completion formulation to predict rates of circulation of viruses for the next season based on historical data. Then, taking into account the uncertainty in the predicted rates of circulation of predominant viruses, we propose a novel robust prescriptive framework for selecting suitable strains for each subtypes of the flu virus: Influenza A (H1N1 and H3N2) and B viruses for production. Finally, we train optimal regression trees to predict efficacy of the prescribed vaccine in terms of both morbidity and mortality rates using a set of weighted distances between the vaccine-strain and the actual circulating viruses during a flu season for each subtypes of the flu virus. Through numerical experiments, we show that our proposed vaccine compositions could potentially lower morbidity by 11-14% and mortality by 8-11% over vaccine compositions proposed by World Health Organization (WHO) for Northern hemisphere, and lower morbidity by 8-10% and mortality by 6-9% over vaccine compositions proposed by U.S Food and Drug Administration (FDA) for USA, and finally, lower morbidity by 10-12% and mortality by 9-11% over vaccine compositions proposed by European Medicines Agency (EMA) for Europe.

Cite this Paper

BibTeX
@InProceedings{pmlr-v126-bandi20a, title = {Optimizing Influenza Vaccine Composition: From Predictions to Prescriptions}, author = {Bandi, Hari and Bertsimas, Dimitris}, booktitle = {Proceedings of the 5th Machine Learning for Healthcare Conference}, pages = {121--142}, year = {2020}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {126}, series = {Proceedings of Machine Learning Research}, month = {07--08 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v126/bandi20a/bandi20a.pdf}, url = {https://proceedings.mlr.press/v126/bandi20a.html}, abstract = {We propose a holistic framework based on state-of-the-art methods in Machine Learning and Optimization to prescribe influenza vaccine composition that are specific to a region, or a country based on historical data concerning the rates of circulation of predominant viruses. First, we develop a tensor completion formulation to predict rates of circulation of viruses for the next season based on historical data. Then, taking into account the uncertainty in the predicted rates of circulation of predominant viruses, we propose a novel robust prescriptive framework for selecting suitable strains for each subtypes of the flu virus: Influenza A (H1N1 and H3N2) and B viruses for production. Finally, we train optimal regression trees to predict efficacy of the prescribed vaccine in terms of both morbidity and mortality rates using a set of weighted distances between the vaccine-strain and the actual circulating viruses during a flu season for each subtypes of the flu virus. Through numerical experiments, we show that our proposed vaccine compositions could potentially lower morbidity by 11-14% and mortality by 8-11% over vaccine compositions proposed by World Health Organization (WHO) for Northern hemisphere, and lower morbidity by 8-10% and mortality by 6-9% over vaccine compositions proposed by U.S Food and Drug Administration (FDA) for USA, and finally, lower morbidity by 10-12% and mortality by 9-11% over vaccine compositions proposed by European Medicines Agency (EMA) for Europe.} }
Endnote
%0 Conference Paper %T Optimizing Influenza Vaccine Composition: From Predictions to Prescriptions %A Hari Bandi %A Dimitris Bertsimas %B Proceedings of the 5th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2020 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v126-bandi20a %I PMLR %P 121--142 %U https://proceedings.mlr.press/v126/bandi20a.html %V 126 %X We propose a holistic framework based on state-of-the-art methods in Machine Learning and Optimization to prescribe influenza vaccine composition that are specific to a region, or a country based on historical data concerning the rates of circulation of predominant viruses. First, we develop a tensor completion formulation to predict rates of circulation of viruses for the next season based on historical data. Then, taking into account the uncertainty in the predicted rates of circulation of predominant viruses, we propose a novel robust prescriptive framework for selecting suitable strains for each subtypes of the flu virus: Influenza A (H1N1 and H3N2) and B viruses for production. Finally, we train optimal regression trees to predict efficacy of the prescribed vaccine in terms of both morbidity and mortality rates using a set of weighted distances between the vaccine-strain and the actual circulating viruses during a flu season for each subtypes of the flu virus. Through numerical experiments, we show that our proposed vaccine compositions could potentially lower morbidity by 11-14% and mortality by 8-11% over vaccine compositions proposed by World Health Organization (WHO) for Northern hemisphere, and lower morbidity by 8-10% and mortality by 6-9% over vaccine compositions proposed by U.S Food and Drug Administration (FDA) for USA, and finally, lower morbidity by 10-12% and mortality by 9-11% over vaccine compositions proposed by European Medicines Agency (EMA) for Europe.
APA
Bandi, H. & Bertsimas, D.. (2020). Optimizing Influenza Vaccine Composition: From Predictions to Prescriptions. Proceedings of the 5th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 126:121-142 Available from https://proceedings.mlr.press/v126/bandi20a.html.

Related Material