Modeling Aedes aegypti trap data with unobserved components
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.
KeywordsClassical inference Dengue Ovitraps State-space models Time series
T. R. Santos was supported by the PrPq-Universidade Federal de Minas Gerais-Brazil, CNPq-Brazil, and the FAPEMIG Foundation. The author thanks the associate editor and two anonymous reviewers for constructive comments and suggestions which substantially helped improve the quality of the paper. The author also thanks Helio Eustaquio dos Santos (in memoriam), Alzira de Rezende dos Santos, and Graciele Fernanda for their endless discussions.
- Barsante LS, Paixao KS, Laass KH, Cardoso RTN, Eiras AE, Acebal JL (2014) A model to predict the population size of the dengue fever vector based on rainfall data. arXiv preprint arXiv:1409.7942
- Bonat WH, Ribeiro PJ Jr, Krainski ET (2014) Modelagem espaço-temporal de contagens de ovos de Aedes aegypti em Recife-PE. Rev Bras Estat 74:75–100Google Scholar
- De Almeida PS, Meotti C, Dos Santos Almeida G, Nascimento J, De Araujo AD, Faccenda O, Gino M (2013) Infestação de Aedes aegypti (Linnaeus, 1762)(diptera: culicidae) determinada por armadilhas de oviposição (ovitrampas) no município de Costa Rica, estado de Mato Grosso do Sul. Rev Patol Trop 42(3):331–339CrossRefGoogle Scholar
- Doornik JA (1999) Ox: an object-oriented matrix language, 3rd edn. Timberlake Consultants Press, LondonGoogle Scholar
- Ferreira CP, Yang HM (2003) Estudo Dinâmico da População de Mosquito Aedes aegypti. Tend Mat Apl Comput 4:187–196Google Scholar
- Harvey AC (1989) Forecasting, structural time series models and the Kalman filter. University Press, CambridgeGoogle Scholar
- IBGE (2010) Instituto Brasileiro de Geografia e Estatistica, Ministerio do Planejamento, Orcamento e Gestao. http://www.ibge.gov.br/estadosat/perfil.php?sigla=ms. Accessed 25 May 2018
- Kalman RE (1960) A new approach to linear filtering and prediction problems. J Fluids Eng 82(1):35–45Google Scholar
- Lana RM, Carneiro TG, Honório NA, Codeço CT (2011) Multiscale analysis and modelling of Aedes aegypti population spatial dynamics. J Inf Data Manag 2(2):211–220Google Scholar
- R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. Accessed 13 January 2017
- Santos TR, Franco GC (2019) Bootstrap for correcting the mean square error of prediction and smoothed estimates in structural models. Braz J Probab Stat (forthcoming)Google Scholar
- West M, Harrison J (1997) Bayesian forecasting and dynamic models. Springer, New YorkGoogle Scholar