Abstract
HIV/AIDS epidemic is an important public health problem. The burden of the epidemic is estimated from surveillance systems data. The collected information is incomplete, making the estimation a challenging task and the reported trends often biased. The most common incomplete-data problems, in this kind of data, are due to under-diagnosis and reporting delays, mainly in the most recent years. This is a classical problem for imputation methodologies. In this paper we study the distribution of AIDS reporting delays through a mix approach, combining longitudinal K-means with the generalized least squares method. While the former identifies homogeneous delay patterns, the latter estimated longitudinal regression curves. We found that a 2-cluster structure is appropriated to accommodate the heterogeneity in reporting delay on HIV/AIDS data and that the corresponding estimated delay curves are almost stationary over time.
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Oliveira, A., Rita Gaio, A., da Costa, J.P., Reis, L.P. (2016). An Approach for Assessing the Distribution of Reporting Delay in Portuguese AIDS Data. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Mendonça Teixeira, M. (eds) New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 445. Springer, Cham. https://doi.org/10.1007/978-3-319-31307-8_66
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DOI: https://doi.org/10.1007/978-3-319-31307-8_66
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