Mapping Incidence and Prevalence Peak Data for SIR Forecasting Applications

TL;DR

This paper introduces a new method to incorporate peak incidence data into SIR models, improving their stability and accuracy in epidemic forecasting by solving a system of equations relating model parameters to peak data.

Contribution

The paper presents a novel approach for integrating peak incidence and hospitalization data into SIR models through a solvable system of equations, enhancing model fitting and forecasting.

Findings

The method improves model stability during early outbreak data fitting.

Simulation results show increased accuracy and computational efficiency.

Updated Dirichlet-Beta framework effectively incorporates hospital incidence data.

Abstract

Infectious disease modeling and forecasting have played a key role in helping assess and respond to epidemics and pandemics. Recent work has leveraged data on disease peak infection and peak hospital incidence to fit compartmental models for the purpose of forecasting and describing the dynamics of a disease outbreak. Incorporating these data can greatly stabilize a compartmental model fit on early observations, where slight perturbations in the data may lead to model fits that project wildly unrealistic peak infection. We introduce a new method for incorporating historic data on the value and time of peak incidence of hospitalization into the fit for a Susceptible-Infectious-Recovered (SIR) model by formulating the relationship between an SIR model's starting parameters and peak incidence as a system of two equations that can be solved computationally. This approach is assessed for practicality in terms of accuracy and speed of computation via simulation. To exhibit the modeling potential, we update the Dirichlet-Beta State Space modeling framework to use hospital incidence data, as this framework was previously formulated to incorporate only data on total infections.