A frequentist approach to estimating the force of infection and the recovery rate for a respiratory disease among infants in coastal Kenya

Abstract

This paper aims to develop a probability-based model involving the use of direct likelihood formulation and generalised linear modelling in order to estimate important disease parameters from real data. The force of infection and the recovery rate or per capita loss of infection are the parameters of interest. The problem of dealing with time-varying disease parameters is also addressed in the paper by fitting piecewise constant parameters over time. The findings of the current paper are comparable and similar to estimates from an independent approach suggested by White et al.21 that employed Bayesian MCMC modelling via WinBUGS.