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.

中文翻译：

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使用未观察到的成分对埃及伊蚊陷阱数据建模

已经提出了几种模型来描述埃及伊蚊的种群动态。对模型参数的直观解释和简单性是机械模型的一些主要特征。另一种可能性是使用统计模型，该模型具有优势，但不容易解释。状态空间模型（SSM），也称为机械时间序列模型，结合了机械模型和统计模型的有益方面。本研究介绍了Ae的SSM 。埃及产卵器数据估计潜在状态和静态参数，对数据进行适当的分析。在此框架中，静态和状态参数的估计很容易实现。进行仿真研究以研究参数的估计器的某些属性。该模型也适用于Ae。aegypti陷阱数据并突出显示其对于实际陷阱数据集的重要性和潜力。结果表明，所提出的SSM具有良好的性能，并且可以合理估计参数。