AI-Enhanced Strategies for COVID-19 Vaccination and Booster Prioritization: A Comprehensive Framework

Sara AlShaya; Aji Gopakumar; Rahul Raghupathy; Badshah Mukherjee; Bachir  Abou Elias; Sheik Jamal Mohideen; Vibhor Mathur; Sudheer Kurakula

doi:10.55489/njcm.151220244270

Authors

Sara AlShaya Emirates Health Services (EHS), Dubai, UAE
Aji Gopakumar Emirates Health Services (EHS), Dubai, UAE
Rahul Raghupathy Emirates Health Services (EHS), Dubai, UAE
Badshah Mukherjee SAS Middle East FZ, Dubai, UAE
Bachir Abou Elias Emirates Health Services (EHS), Dubai, UAE
Sheik Jamal Mohideen Emirates Health Services (EHS), Dubai, UAE
Vibhor Mathur Emirates Health Services (EHS), Dubai, UAE
Sudheer Kurakula Emirates Health Services (EHS), Dubai, UAE

DOI:

https://doi.org/10.55489/njcm.151220244270

Keywords:

United Arab Emirates, AI-augmented EHS Intelligence platform, Machine learning in public health, COVID-19 vaccination, Booster dose, Data analytics, UAE healthcare innovation

Abstract

Background: Distributing vaccines efficiently during the COVID-19 pandemic presented significant logistical challenges. To address the need for identifying populations at risk of breakthrough infections and those requiring booster shots, Emirates Health Services (EHS) developed a framework utilizing AI-driven digital solutions. Objective: To develop a machine learning (ML) model to identify individuals at risk of breakthrough infections and in need of booster doses, aiming to prioritize booster administration and reduce repeated infections among fully vaccinated individuals in the Northern Emirates of the UAE.

Methods: A monitoring dashboard was developed using the EHS Intelligence (PaCE) platform. The study, conducted in three phases, created models to predict infection risk, COVID-19 severity in ICU patients and breakthrough infection risk, using data from the Wareed EMR system.

Results: The AI models accurately identified high-risk individuals and predicted ICU mortality, achieving AUCs of 75% and 74% for infection risk, 94% and 91% for ICU mortality in training and validation datasets, observed 79% AUC with 85% accuracy for identifying high-risk groups for booster vaccination.

Conclusion: The integration of AI in vaccination prioritization demonstrated its potential to enhance public health initiatives and improve pandemic management in the UAE.

References

Haas EJ, McLaughlin JM, Khan F, et al. Infections, hospitalizations, and deaths averted via a nationwide vaccination campaign using the Pfizer-BioNTech BNT162b2 mRNA COVID-19 vaccine in Israel: a retrospective surveillance study. Lancet Infect Dis. 2021;22:357-366. DOI: https://doi.org/10.1016/S1473-3099(21)00566-1 PMid:34562375

Goldberg Y, Mandel M, Bar-On YM, et al. Waning immunity after the BNT162b2 vaccine in Israel. N Engl J Med. 2021;385:e85. DOI: https://doi.org/10.1056/NEJMoa2114228 PMCid:PMC8609604

Tang P, Hasan MR, Chemaitelly H, et al. BNT162b2 and mRNA-1273 COVID-19 vaccine effectiveness against the SARS-CoV-2 delta variant in Qatar. Nat Med. 2021;27:2136-2143. DOI: https://doi.org/10.1038/s41591-021-01583-4 PMid:34728831

Mahase E. COVID-19: booster vaccine gives "significant increased protection" in over 50s. BMJ. 2021;375. DOI: https://doi.org/10.1136/bmj.n2814 PMid:34789456

Korosec, C.S., Dick, D.W., Moyles, I.R. et al. SARS-CoV-2 booster vaccine dose significantly extends humoral immune response half-life beyond the primary series. Sci Rep 2024;14:8426. DOI: https://doi.org/10.1038/s41598-024-58811-3 PMid:38637521 PMCid:PMC11026522

Bilgin GM, Lokuge K, Munira SL, Glass K. Assessing the potential impact of COVID-19 booster doses and oral antivirals: A mathematical modelling study of selected middle-income countries in the Indo-Pacific. Vaccine X. 2023 Sep 9;15:100386. DOI: https://doi.org/10.1016/j.jvacx.2023.100386 PMid:37727365 PMCid:PMC10506093

Irving SA, Sundaram ME. Prioritisation of COVID-19 boosters in the omicron era. Lancet. 2022 Oct 15;400(10360):1282-1283. DOI: https://doi.org/10.1016/S0140-6736(22)01971-7 PMid:36244366

Chapman, L. A. C. et al. Risk factor targeting for vaccine prioritization during the COVID-19 pandemic. Sci. Rep. 12(1), 3055 (2022). DOI: https://doi.org/10.1038/s41598-022-06971-5 PMid:35197495 PMCid:PMC8866501

Matrajt, L., Eaton, J., Leung, T. & Brown, E. R. Vaccine optimization for COVID-19: Who to vaccinate first? Sci. Adv. 7(6), eabf1374 (2021). DOI: https://doi.org/10.1126/sciadv.abf1374 PMid:33536223 PMCid:PMC8128110

Bubar, K. M. et al. Model-informed COVID-19 vaccine prioritization strategies by age and serostatus. Science 371(6532), 916-921 (2021). DOI: https://doi.org/10.1126/science.abe6959 PMid:33479118 PMCid:PMC7963218

Li Y, Lee K-C, Bressington D, Liao Q, He M, Law K-K, Leung AYM, Molassiotis A, Li M. A Theory and Evidence-Based Artificial Intelligence-Driven Motivational Digital Assistant to Decrease Vaccine Hesitancy: Intervention Development and Validation. Vaccines. 2024; 12(7):708. DOI: https://doi.org/10.3390/vaccines12070708 PMid:39066346 PMCid:PMC11281439

Larson HJ, Lin L. Generative artificial intelligence can have a role in combating vaccine hesitancy. BMJ. 2024 Jan 16;384:q69. DOI: https://doi.org/10.1136/bmj.q69 PMid:38228351 PMCid:PMC10789191

Ma X, Wang Y, Gao T, He Q, He Y, Yue R, et al. Challenges and strategies to research ethics in conducting COVID-19 research. J Evid Based Med. (2020) 13:173-177. DOI: https://doi.org/10.1111/jebm.12388 PMid:32445288 PMCid:PMC7280675

Naudé W. Artificial intelligence vs COVID-19: limitations, constraints and pitfalls. AI Soc. (2020) 35:761-65. DOI: https://doi.org/10.1007/s00146-020-00978-0 PMid:32346223 PMCid:PMC7186767

Yassine HM, Shah Z. How could artificial intelligence aid in the fight against coronavirus? Expert Rev Anti Infect Ther. (2020) 18:493-7. DOI: https://doi.org/10.1080/14787210.2020.1744275 PMid:32223349

Sharma A, Virmani T, Pathak V, Sharma A, Pathak K, Kumar G, Pathak D. Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine. Biomed Res Int. 2022 Jul 6;2022:7205241. DOI: https://doi.org/10.1155/2022/7205241 PMid:35845955 PMCid:PMC9279074

Yagin FH, Cicek İB, Alkhateeb A, Yagin B, Colak C, Azzeh M, Akbulut S. Explainable artificial intelligence model for identifying COVID-19 gene biomarkers. Comput Biol Med. 2023;154:106619. DOI: https://doi.org/10.1016/j.compbiomed.2023.106619 PMid:36738712 PMCid:PMC9889119

Al-Qaness MAA, Saba AI, Elsheikh AH, Elaziz MA, Ibrahim RA, Lu S, et al. Efficient artificial intelligence forecasting models for COVID-19 outbreak in Russia and Brazil. Process Saf Environ Prot. (2021) 149:399-409. DOI: https://doi.org/10.1016/j.psep.2020.11.007 PMid:33204052 PMCid:PMC7662076

Fang C, Bai S, Chen Q, Zhou Y, Xia L, Qin L, et al. Deep learning for predicting COVID-19 malignant progression. Med Image Anal. (2021) 72:102096. DOI: https://doi.org/10.1016/j.media.2021.102096 PMid:34051438 PMCid:PMC8112895

Srinivasa Rao ASR, Vazquez JA. Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey when cities and towns are under quarantine. Infect Control Hosp Epidemiol. 2020 Jul;41(7):826-830. DOI: https://doi.org/10.1017/ice.2020.61 PMid:32122430 PMCid:PMC7200852

Du Y, Tu L, Zhu P, Mu M, Wang R, Yang P, et al. Clinical Features of 85 Fatal Cases of COVID-19 from Wuhan. A Retrospective Observational Study. Am J Respir Crit Care Med. 2020 Jun 1;201(11):1372-1379. DOI: https://doi.org/10.1164/rccm.202003-0543OC PMid:32242738 PMCid:PMC7258652

Daoust JF. Elderly people and responses to COVID-19 in 27 countries. PLoS One. 2020;15(7):e0235590. DOI: https://doi.org/10.1371/journal.pone.0235590 PMid:32614889 PMCid:PMC7332014

Li J, Chen Z, Nie Y, Ma Y, Guo Q, Dai X. Identification of symptoms prognostic of COVID-19 severity: multivariate data analysis of a case series in Henan province. J Med Internet Res. 2020 Jun 30;22(6):e19636. DOI: https://doi.org/10.2196/19636 PMid:32544071 PMCid:PMC7332230

Aktar S, Talukder A, Ahamad MM, Kamal AHM, Khan JR, Protikuzzaman M, Hossain N, Azad AKM, Quinn JMW, Summers MA, Liaw T, Eapen V, Moni MA. Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19. Diagnostics (Basel). 2021 Jul 31;11(8):1383. DOI: https://doi.org/10.3390/diagnostics11081383 PMid:34441317 PMCid:PMC8393412

Lasya KL, Lahari DR. Akarsha AL, Prakash KB, Tran D. Analysis and Prediction of COVID-19 datasets using Machine Learning Algorithms. ICEEICT 2022; 01-03. DOI: https://doi.org/10.1109/ICEEICT53079.2022.9768598

Raja, M. A., Ullah, I., Babar, M., & Aziz, T. (2023). Prediction of COVID-19 using machine learning techniques. The Asian Bulletin of Big Data Management, 3(1), 221-234. DOI: https://doi.org/10.62019/abbdm.v3i1.45

Sanzida S, Azmiara A, Chand SM, Muktadir M, Riasat K. Automatic COVID-19 prediction using explainable machine learning techniques. International Journal of Cognitive Computing in Engineering, 2023:4;36-46. DOI: https://doi.org/10.1016/j.ijcce.2023.01.003 PMCid:PMC9876019

Sara AlShaya, Maryam Sayed Jaffar, Sheik Abdullah Jamal Mohideen, Aji Gopakumar, Vibhor Mathur, Maimoona Saeed. A Machine Learning Model to Predict COVID-19 Infection Risk in the United Arab Emirates. J Commun Dis. 2023;55(4):71-79. DOI: https://doi.org/10.24321/0019.5138.202358

Sara Alshaya, Maryam Sayed Jaffar, Aji Gopakumar, Sheik Abdullah Jamal Mohideen, Vibhor Mathur, Sudheer Kurakula, Badshah Mukherjee. Artificial Intelligence (AI) Model to predict the risk of COVID-19 ICU Severity: A Pandemic Success Story. J Commun Dis. 2024;56(2):6-14. DOI: https://doi.org/10.24321/0019.5138.202426

Abhirup B, Surajit R, Bart V, Joanne K, Michail M, Simonne W, Mark B, Louise SM. Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population, International Immunopharmacology 2020;86:106705. DOI: https://doi.org/10.1016/j.intimp.2020.106705 PMid:32652499 PMCid:PMC7296324

Tay J, Yen YH, Rivera K, Chou EH, Wang CH, Chou FY, et al. Development and External Validation of Clinical Features-based Machine Learning Models for Predicting COVID-19 in the Emergency Department. West J Emerg Med. 2024 Jan;25(1):67-78. DOI: https://doi.org/10.5811/WESTJEM.60243

Bottrighi, A., Pennisi, M., Roveta, A. et al. A machine learning approach for predicting high risk hospitalized patients with COVID-19 SARS-Cov-2. BMC Med Inform Decis Mak 22, 340 (2022). DOI: https://doi.org/10.1186/s12911-022-02076-1 PMid:36578017 PMCid:PMC9795955

Sakagianni A, Koufopoulou C, Verykios V, Loupelis E, Kalles D, Feretzakis G. Prediction of COVID-19 Mortality in the Intensive Care Unit Using Machine Learning. Stud Health Technol Inform. 2023 May 18;302:536-540. DOI: https://doi.org/10.3233/SHTI230200

Mouliou DS, Pantazopoulos I, Gourgoulianis, KI. COVID-19 Smart Diagnosis in the Emergency Department: all-in in Practice. Expert Review of Respiratory Medicine 2022;16(3):263-272. DOI: https://doi.org/10.1080/17476348.2022.2049760 PMid:35245149 PMCid:PMC8935450

Monadhel H, Abbas AR, Mohammed AJ. COVID-19 Vaccine: Predicting Vaccine Types and Assessing Mortality Risk Through Ensemble Learning Agorithms. F1000Res. 2023 Sep 25;12:1200. DOI: https://doi.org/10.12688/f1000research.140395.1 PMid:38799245 PMCid:PMC11128056

Gonzalez-Parra G, Mahmud MS, Kadelka C. Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models. medRxiv [Preprint]. 2024 Mar 7:2024.03.04.24303726. doi: 10.1101/2024.03.04.24303726. Update in: Infect Dis Model. 2024 May 15;9(4):1057-1080. DOI: https://doi.org/10.1016/j.idm.2024.05.005 PMid:38988830 PMCid:PMC11233876