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Updated: Feb 10, 2026

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
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Development of a Patient-Level Multi-objective Optimisation Model for Screening Strategies for Childhood Type 1

Gonçalo Leiria1, R Brett McQueen2, Conner Jackson2

  • 1Department of Computer Science, University of Exeter, Exeter, UK.

Pharmacoeconomics
|February 9, 2026
PubMed

Insights

This study developed a patient-level simulation model for type 1 diabetes (T1D) screening. Optimal strategies were identified, balancing cost-effectiveness and minimizing diabetic ketoacidosis (DKA) events.

Area of Science:

  • Computational modeling and simulation
  • Public health and epidemiology
  • Endocrinology and metabolic diseases

Background:

  • Type 1 diabetes (T1D) is a chronic autoimmune disease requiring lifelong management.
  • Early detection of pre-symptomatic T1D can potentially improve outcomes and reduce complications.
  • Current screening strategies for T1D are not universally established or optimized for cost-effectiveness.

Purpose of the Study:

  • To develop a comprehensive patient-level simulation model for T1D, encompassing childhood and adulthood.
  • To identify and evaluate the cost-effectiveness of optimal screening strategies for pre-symptomatic T1D.
  • To balance screening costs, clinical benefits (life years gained), and patient burden (e.g., number of tests).

Main Methods:

  • A Python-based patient-level simulation model was created to track 100,000 participants.
  • A multi-objective optimization approach (NSGA-II algorithm) was employed to minimize cost-effectiveness ratio, diabetic ketoacidosis (DKA) events, and the number of screening tests.
  • The model incorporated data from large-scale screening studies, risk functions, and clinical trials for transition probabilities.

Main Results:

  • Four optimal T1D screening strategies were identified in the USA, meeting common cost-effectiveness thresholds.
  • These strategies involve a maximum of 1, 2, 3, or 4 islet autoantibody (IA) tests.
  • The model demonstrated the utility of multi-objective optimization in patient-level simulations for T1D screening.

Conclusions:

  • The developed patient-level simulation model and optimization pipeline offer a valuable reference for T1D screening research.
  • The identified strategies provide evidence-based options for implementing cost-effective T1D screening programs.
  • Further application of this methodology can aid in refining public health strategies for T1D prevention and management.
Abstract

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