Comparison of logistic regression and growth function models for the analysis of the incidence of virus infection

Abstract

A logistic regression model was compared to logistic, Gompertz and log-logistic growth functions for analyzing a set of data describing the incidence of Alfalfa mosaic virus infection in lucerne fields aged from one to five years, and located in three different ecological areas of the Ebro Valley, Northeast Spain. Models were fitted in the form of generalized linear models, and none of them explained well the high variability of the field data, although they were useful to analyze the interdependence among epidemiological factors associated with estimated parameters in the models. The logistic regression model proved more sensitive than classical growth function models to detect significant differences in parameters such as the rate of incidence increase with age of lucerne field or the initial amount of disease, and to detect differences associated to explanatory variables such as the ecological area. Results indicate that logistic regression may be a method well suited to statistical analyses in plant epidemiology.

Status of parthenium weed (Parthenium hysterophorus L.) and its control options in Ethiopia

“…The logistic regression model is proved to be more sensitive than classical growth function models to detect significant differences in parameters such as the rate of incidence increase in fields or the initial amount of disease and to detect differences associated to explanatory variables such as the ecological area (Bergua et al, 2008). Hau and Mersha (2008) used logistic regression, logistic and Gompertz models to study the effect of bean rust on host dynamics of common bean in controlled greenhouse experiments.…”