Modeling Aedes aegypti trap data with unobserved components

Abstract

Several models have been proposed to describe the population dynamics of Aedes aegypti. Intuitive interpretation of model parameters and simplicity are some of the main characteristics of mechanistic models. Another possibility is the use of statistical models, which have their advantages but are not easy to interpret. The state-space model (SSM), also known as a mechanistic time series model, incorporates the beneficial aspects of both mechanistic and statistical models. This study introduces a SSM for Ae. aegypti ovitrap data to estimate latent state and static parameters, making suitable analysis of the data. The estimation of static and state parameters is easy to achieve in this framework. A simulation study is performed to study some properties of the estimators for the parameters. The model is also applied to Ae. aegypti trap data and highlights its importance and potential for the real trap data sets. The results show that the proposed SSM has good performance and the parameters can be reasonably estimated.