A stochastic model of malaria transition rates from longitudinal data: considering the risk of "lost to follow-up".

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Abstract
A model, using stochastic processes, is developed to estimate some epidemiological parameters of malaria in a homogeneous population from longitudinal data. Assessments of transition probabilities from one state of health to the other are made taking "lost to follow-up" as a competing risk. The model is based on the assumptions that individuals are transferred at constant rate between states, and only one transition is possible between two consecutive surveys. It shows a good fit to the observed data; the model is simple to understand and can easily be used if computer facilities are not available.