@conference{COVID19-MDiaz2022,
author = "Miguel D{\'i}az-Lozano and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "In this paper, an approach based on a time series clustering technique is presented by extracting relevant features from the original temporal data. A curve characterization is applied to the daily contagion rates of the 34 sanitary districts of Andalusia, Spain. By determining the maximum incidence instant and two inflection points for each wave, an outbreak curve can be described by six intensity features, defining its initial and final phases. These features are used to derive different groups using state-of-the-art clustering techniques. The experimentation carried out indicates that {\$}{\$}k=3{\$}{\$}k=3is the optimum number of descriptive groups of intensities. According to the resulting clusters for each wave, the pandemic behavior in Andalusia can be visualised over time, showing the most affected districts in the pandemic period considered. Additionally, in order to perform a pandemic overview of the whole period, the approach is also applied to joint information of all the considered periods",
booktitle = "Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence (Proceedings of the 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022)",
doi = "10.1007/978-3-031-06527-9_46",
isbn = "978-3-031-06527-9",
keywords = "COVID-19 contagions, clustering, curve characterization",
month = "May",
number = "Part II",
organization = "Tenerife, Spain",
pages = "462--471",
publisher = "Springer",
series = "Lecture Notes in Computer Science (LNCS)",
title = "{C}lustering of {COVID}-19 {T}ime {S}eries {I}ncidence {I}ntensity in {A}ndalusia, {S}pain",
url = "doi.org/10.1007/978-3-031-06527-9_46",
volume = "13259",
year = "2022",
}