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 \(k=3\) is 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.
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Notes
- 1.
Andalusian sanitary districts: https://www.sspa.juntadeandalucia.es/servicioandaluzdesalud/el-sas/servicios-y-centros/mapa-centros.
- 2.
The number of infections in Andalusia is daily reported at https://www.juntadeandalucia.es/institutodeestadisticaycartografia/badea/operaciones/consulta/anual/42249.
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Acknowledgements
This work was supported by the “Agencia Española de Investigación (España)” (grant reference: PID2020-115454GB-C22/AEI/10.13039/501100011033); the “Consejería de Salud y Familia (Junta de Andalucía)” (grant reference: PS-2020-780); and the “Consejería de Transformación Económica, Industria, Conocimiento y Universidades (Junta de Andalucía) y Programa Operativo FEDER 2014-2020” (grant references: UCO-1261651 and PY20_00074). David Guijo-Rubio’s research has been subsidised by the University of Córdoba through grants to Public Universities for the requalification of the Spanish university system of the Ministry of Universities, financed by the European Union - NextGenerationEU (grant reference: UCOR01MS).
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Díaz-Lozano, M., Guijo-Rubio, D., Gutiérrez, P.A., Hervás-Martínez, C. (2022). Clustering of COVID-19 Time Series Incidence Intensity in Andalusia, Spain. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. IWINAC 2022. Lecture Notes in Computer Science, vol 13259. Springer, Cham. https://doi.org/10.1007/978-3-031-06527-9_46
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