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Review Article

Real-Time Environmental Trigger Modeling and Personalized Allergic Rhinitis Management in the United States: Exploring a Digital Twin Ecosystem

Authors

  • Ubalaeze Elechi University of Nigeria, Nigeria https://orcid.org/0009-0002-3474-1002

    elechiuba@gmail.com

  • Adekunle Fatai Adeoye Georgia State University, USA
  • Kelechi Elechi The University of Texas Health Science Center at San Antonio, USA
  • Stephen Onyekachi Obiya National University of Science and Technology MISIS, Russian Federation
  • Shehu Abubakar Umar National University of Science and Technology MISIS, Russian Federation
  • Paul Iwu National University of Science and Technology MISIS, Russian Federation
  • Muhammad Bello Demola University of Ulster, UK
  • Okabeonye Sunday Agbo Enugu State University of Science and Technology, Nigeria https://orcid.org/0009-0000-3305-2649
  • Joy Chinyere Elokaakwaeze Kwame Nkrumah University of Science and Technology, Ghana
  • Victor Chiedozie Ezeamii Department of Biostatistics, Epidemiology, and Environmental Health Sciences, Georgia Southern University, USA https://orcid.org/0009-0005-4321-1136
  • Kindson Nkejah Abone University of Nigeria Teaching Hospital, Ituku-Ozalla, Enugu, Nigeria https://orcid.org/0009-0002-1197-6634
  • Chiazom Arubaleze University of Nigeria Teaching Hospital, Ituku-Ozalla, Enugu, Nigeria https://orcid.org/0009-0006-9770-7660

Abstract

Allergic rhinitis imposes a major health and socioeconomic burden. Real-time environmental trigger modeling and digital-twin technology promise a personalized approach to managing this burden. We review how a digital twin ecosystem can integrate live environmental data (such as pollen and pollution levels) with individual patient profiles (sensitization, symptoms, behaviors) to predict allergic rhinitis flares and inform tailored interventions. This narrative review synthesizes recent advances in digital architecture, data streams, and predictive analytics for allergic rhinitis. We discuss a layered digital-twin system that continuously fuses real-world exposures with personal health data to generate real-time risk assessments, treatment recommendations, and decision support. Early evidence suggests that such systems improve symptom tracking and enable preventive strategies to reduce flare-ups, but challenges remain in data integration, user engagement, and validation. We highlight clinical implications, cost benefits, technological gaps, and future directions for deploying digital twins in allergy care and broader public health initiatives worldwide.

Keywords:

Allergic Rhinitis Digital Twin Technology Personalized Medicine Predictive Analytics in Healthcare Real‑Time Environmental Monitoring

Article information

Journal

Journal of Medical Science, Biology, and Chemistry

Volume (Issue)

2(1), (2025)

Pages

84-91

Published

22-05-2025

How to Cite

Elechi, U., Adeoye, A. F., Elechi, K., Obiya, S. O., Umar, S. A., Paul, I., Demola, M. B., Agbo, O. S., Elokaakwaeze, J. C., Ezeamii, V. C. E., Abone, K. N. A., & Arubaleze, C. (2025). Real-Time Environmental Trigger Modeling and Personalized Allergic Rhinitis Management in the United States: Exploring a Digital Twin Ecosystem. Journal of Medical Science, Biology, and Chemistry, 2(1), 84-91. https://doi.org/10.69739/jmsbc.v2i1.559

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