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Mapping Incidence and Prevalence Peak Data for SIR Modeling Applications.

Alexander C Murph1, G Casey Gibson2, Lauren J Beesley2

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Summary

This study introduces a new method for fitting Susceptible-Infectious-Recovered (SIR) models using historical peak hospitalization data. This approach improves infectious disease forecasting accuracy by stabilizing model fits with early epidemic data.

Keywords:
Compartmental ModelsDisease ForecastingHospital IncidencePrevalence

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Computational Science

Background:

  • Infectious disease modeling is crucial for epidemic response.
  • Compartmental models like SIR are used for forecasting disease dynamics.
  • Early data can lead to unstable model fits and unrealistic forecasts.

Purpose of the Study:

  • To develop a novel method for incorporating peak hospitalization data into SIR model fitting.
  • To enhance the accuracy and stability of infectious disease forecasting.
  • To assess the impact of using incidence versus prevalence data in modeling.

Main Methods:

  • Formulated a system of two equations to computationally solve for SIR model parameters based on peak incidence.
  • Updated the Dirichlet-Beta State Space modeling framework to utilize hospital incidence data.
  • Assessed the method's accuracy and computational speed via simulation.

Main Results:

  • The new method allows for the calculation of SIR parameter estimates, including transmission and recovery rates.
  • Misspecifying prevalence data as incidence data leads to a noticeable loss in accuracy.
  • The updated Dirichlet-Beta State Space model demonstrates practical potential for forecasting using hospital incidence data.

Conclusions:

  • Incorporating peak hospitalization data significantly stabilizes compartmental model fits.
  • Accurate data type specification (incidence vs. prevalence) is critical for reliable infectious disease modeling.
  • The developed method offers a practical and accurate approach to infectious disease forecasting.