Time series clustering of COVID-19 pandemic-related data

Aghabozorgi, 2015, Time-series clustering–a decade review, Inf. Syst., 53, 16, 10.1016/j.is.2015.04.007 Alzahrani, 2020, Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions, J. Infect. Publ. Health., 13, 914, 10.1016/j.jiph.2020.06.001 Badr, 2020, Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study, Lancet Infect. Dis., 20, 1247, 10.1016/S1473-3099(20)30553-3 Cassão, 2022, Unsupervised analysis of COVID-19 pandemic evolution in brazilian states, Procedia Comput. Sci., 196, 655, 10.1016/j.procs.2021.12.061 Ceylan, 2020, Estimation of COVID-19 prevalence in Italy, Spain, and France, Sci. Total Environ., 729 Chen, 2020, A time-dependent SIR model for COVID-19 with undetectable infected persons, IEEE Trans. Network Sci. Eng., 7, 3279, 10.1109/TNSE.2020.3024723 Chinazzi, 2020, The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak, Science, 368, 395, 10.1126/science.aba9757 Dunn, 1973, A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, J. Cybern., 3, 32, 10.1080/01969727308546046 Giorgino, 2009, Computing and visualizing dynamic time warping alignments in R: the dtw package, J. Stat. Software, 31, 1 Guidotti, 2022, A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution, Sci. Data, 9, 1, 10.1038/s41597-022-01245-1 Guidotti, 2020, COVID-19 data Hub, J. Open Source Softw., 5, 2376, 10.21105/joss.02376 Ho, 2020, Is older age associated with COVID-19 mortality in the absence of other risk factors? General population cohort study of 470,034 participants, PLoS One, 15, 10.1371/journal.pone.0241824 James, 2020, Cluster-based dual evolution for multivariate time series: analyzing COVID-19, Chaos: Interdiscipl. J. Nonlinear Sci., 30, 10.1063/5.0013156 Karim, 2021, Omicron SARS-CoV-2 variant: a new chapter in the COVID-19 pandemic, Lancet, 398, 2126, 10.1016/S0140-6736(21)02758-6 Keogh, 2005, Exact indexing of dynamic time warping, Knowl. Inf. Syst., 7, 358, 10.1007/s10115-004-0154-9 Landmesser, 2021, The use of the dynamic time warping (DTW) method to describe the COVID-19 dynamics in Poland, Oeconomia Copernicana, 12, 539, 10.24136/oc.2021.018 Liao, 2005, Clustering of time series data—a survey, Pattern Recogn., 38, 1857, 10.1016/j.patcog.2005.01.025 Lloyd, 1982, Least squares quantization in PCM, IEEE Trans. Inf. Theor., 28, 129, 10.1109/TIT.1982.1056489 Łuczak, 2021, Fuzzy clustering methods to identify the epidemiological situation and its changes in European countries during COVID-19, Entropy, 24, 14, 10.3390/e24010014 Mahmoudi, 2020, Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries, Chaos, Solit. Fractals, 140, 110230, 10.1016/j.chaos.2020.110230 Meintrup, 2021, Nine months of COVID-19 pandemic in Europe: a comparative time series analysis of cases and fatalities in 35 countries, Int. J. Environ. Res. Publ. Health, 18, 6680, 10.3390/ijerph18126680 2022 Rousseeuw, 1987, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math., 20, 53, 10.1016/0377-0427(87)90125-7 Sakoe, 1978, Dynamic programming algorithm optimization for spoken word recognition, IEEE Trans. Acoust. Speech Signal Process., 26, 43, 10.1109/TASSP.1978.1163055 Sardá-Espinosa, 2019, Time-series clustering in R using the dtwclust package, R. J., 11, 22 Su, 2022, Mind the “worry fatigue” amid Omicron scares, Brain Behav. Immun., 101, 60, 10.1016/j.bbi.2021.12.023 Wang, 2020, Examining the change of human mobility adherent to social restriction policies and its effect on COVID-19 cases in Australia, Int. J. Environ. Res. Publ. Health, 17, 7930, 10.3390/ijerph17217930 Wu, 2022, Incubation Period of COVID-19 caused by unique SARS-CoV-2 strains: a systematic review and meta-analysis, JAMA Netw. Open, 5, 10.1001/jamanetworkopen.2022.28008 Xie, 2021, Analysis of time series features of COVID-19 in various countries based on pedigree clustering, J. Geo-Informat. Sci., 236 Yanez, 2020, COVID-19 mortality risk for older men and women, BMC Publ. Health, 20, 1742, 10.1186/s12889-020-09826-8 Zarikas, 2020, Clustering analysis of countries using the COVID-19 cases dataset, Data Brief, 31, 10.1016/j.dib.2020.105787 Zhang, 2021, The significant immune escape of pseudotyped SARS-CoV-2 variant Omicron, Emerg. Microb. Infect., 11, 1, 10.1080/22221751.2021.2017757 Zhu, 2020, A novel coronavirus from patients with pneumonia in China, 2019, N. Engl. J. Med., 382, 727, 10.1056/NEJMoa2001017