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Augmenting Epidemiological Models with Point-Of-Care Diagnostics Data.

Özgür Özmen1, Laura L Pullum2, Arvind Ramanathan1

  • 1Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America.

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This study presents a method to process Point-of-Care (POC) diagnostic data for epidemiological models. Our approach effectively calibrates models, predicting disease dynamics and peak loads using POC data.

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Public Health

Background:

  • Increasing adoption of Point-of-Care (POC) diagnostics generates valuable data.
  • Challenges exist in integrating POC data into epidemiological models for accurate disease prediction.
  • Existing models often struggle to leverage granular, real-world diagnostic data effectively.

Purpose of the Study:

  • To develop a method for processing zip-code level POC diagnostic data.
  • To apply processed POC data for calibrating an epidemiological model.
  • To assess the efficacy of parsimonious models and a novel calibration algorithm.

Main Methods:

  • Developed a data processing technique for zip-code level POC datasets.
  • Implemented a calibration algorithm utilizing simulated annealing.
  • Calibrated a modified Susceptible-Infected-Recovered (SIR) dynamics model.
  • Explored correlations between calibrated peak load and population density data.

Main Results:

  • Parsimonious models accurately predict infected patient dynamics.
  • The calibration algorithm successfully predicts peak loads from POC data within empirical parameter ranges.
  • Linearity assumptions between factors like peak load and population density can be misleading.
  • Demonstrated the potential for multi-scale decision-making through calibrated epidemiological models.

Conclusions:

  • The proposed method enables effective use of POC diagnostic data for epidemiological modeling.
  • Calibrated models offer reliable predictions of disease spread and peak infection periods.
  • Further research is needed to uncover complex relationships between epidemiological parameters and external factors.
  • This approach supports evidence-based public health policy and resource allocation.