MAXIMUM-LIKELIHOOD FOR PARASITOLOGISTS

In quantitative biology, observed data are fitted to a model that capt ures the essence of the system under investigation in order to obtain estimates of the parameters of the model, as well as their standard er rors and interactions. The fitting is best done by the method of maxim um likelihood, though least-squares fits ore often used as an approxim ation because the calculations are perceived to be simpler Here Brian Williams and Chris Dye argue that the method of maximum likelihood is generally preferable to least squares giving the best estimates of the parameters for data with any given error distribution, and the calcul ations are no more difficult than for least-squares fitting. They offe r a relatively simple explanation of the methods and describe its impl ementation using examples from leishmaniasis epidemiology.