A two-stage approach for estimating the parameters of an age-group epidemic model from incidence data

TL;DR

This paper introduces a novel two-stage method for accurately estimating age-specific parameters in epidemic models using outbreak incidence data, applicable to recurring outbreaks without structural constraints.

Contribution

The paper presents a new two-stage estimation approach for the next-generation matrix in age-group epidemic models, handling nonlinearities and recurring outbreaks effectively.

Findings

Accurate parameter estimation demonstrated in simulation studies.

Application to real influenza data yields consistent epidemiological parameters.

Method outperforms previous approaches in flexibility and efficiency.

Abstract

Age-dependent dynamics is an important characteristic of many infectious diseases. Age-group epidemic models describe the infection dynamics in different age-groups by allowing to set distinct parameter values for each. However, such models are highly nonlinear and may have a large number of unknown parameters. Thus, parameter estimation of age-group models, while becoming a fundamental issue for both the scientific study and policy making in infectious diseases, is not a trivial task in practice. In this paper, we examine the estimation of the so called next-generation matrix using incidence data of a single entire outbreak, and extend the approach to deal with recurring outbreaks. Unlike previous studies, we do not assume any constraints regarding the structure of the matrix. A novel two-stage approach is developed, which allows for efficient parameter estimation from both statistical and computational perspectives. Simulation studies corroborate the ability to estimate accurately the parameters of the model for several realistic scenarios. The model and estimation method are applied to real data of influenza-like-illness in Israel. The parameter estimates of the key relevant epidemiological parameters and the recovered structure of the estimated next-generation matrix are in line with results obtained in previous studies.