# COVID-19: Worldwide Profiles during the First 250 Days
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **Main Purpose** & **Countiries/** & **Period** & **Data Sources** & **Techniques** \\ \hline [PERSON] et al. & & & & & & \\ (2020) [8] & & country with a & 191 & 100 days & & Statistics tests, \\  & & similar spread of & countries & & & \\  & the coronavirus & & 191 & 100 days & & Hierarchical Clustering, \\  & & similar spread of & countries & & & \\  & the coronavirus & & & & & Minimal, and \\  & & study the impact of & & & & \\  & & the pandemic on & 12 & & & \\  & & tourism, & European & March 2020 & & CAAPS, \\  & & particularly in & countries & & D-EDGE, ECDC, & \\  & & tourism, & European & March 2020 & & OWID, \\  & & particularly in & countries & & STR, & \\  & & hospitality & & & WTTC & Hierarchical Clustering \\  & & understand the & & & John Hopkins & \\  & & clustering of & & & University, \\  & & countries in groups & & & & \\  & and & using country-level & 155 & 23 March & & Global Burden of \\  & Castillo-Cara, & pre-COVID-19 & countries & 2020 & & Disease, Global \\  & & variables and & & Observatory, & \\  & COVID-19 cases & & & World Bank & \\  & and deaths & & & & \\  & Identify the spatial & & Countries & & \\  & variations in & & with 1000 & & \\  & & relation to & countries & & \\  & countries’ public & & & cases at 6 & \\  & health expenditure & & May 2020 & & \\  & & understand the & & & \\  & spread of the & 7 & 22 February & & \\  & coronavirus among & countries & 2020 to 18 & & WHO & Statistics tests, Fuzzy \\  & [13] & different countries & & April 2020 & & Clustering \\  & & Determine the & & & & \\ [PERSON] et al. & similarity of & & & Until 21 & John Hopkins & Dynamic Time Warping \\  & (2020) [11] & COVID-19 spread & & of America & June 2020 & University & Clustering \\  & & over time & 208 & & \\  & Understand the & countries with & & & \\  & clustering of & territories, but & 22 January & & \\  & countries with & with several & 2020 to 4 & & \\  & respect to active & being & April 2020 & & \\  & cases & excluded from & & \\  & & the results & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Publications Related to the Temporal Analysis and Clustering of the Impact of COVID-19 by Country. Considering the motivations presented above, together with the lack of previous studies that have examined the temporal evolution of the pandemic on a global scale as a way to characterize and understand the similarities among countries, this work aims to test the following hypotheses with regard to the country-wise time profile evolutions of COVID-19:  
* H1: Are there different types of behavior among countries/territories? * H1a: If so, how many behavior clusters can be identified? * H1b: If so, are there any significant features associated with such clusters? * H1c: If so, what are the characteristics of countries/territories in each cluster? In terms of research methodologies, we used data science techniques, such as data visualizations and statistical tests, to do what in data mining is often called data characterization and data description, i.e., summarizing data by class and comparing classes [14]. We also employed time series and unsupervised learning machine learning-based techniques, namely to group countries by their similarity in terms of COVID-19 cases and deaths time profiles. Additionally, we carried out some preliminary analysis of the relationships between cases and deaths caused by COVID-19 and some countries' development indicators. The structure of this paper reflects the methodology employed during the corresponding research process, often known as CRISP-DM (CRoss-Industry Standard Process for Data Mining) [15]. Therefore, Section 2 describes the data used, including data sources, data transformation, and data analyses techniques, which under the CRISP-DM framework would correspond to the data understanding, data preparation, and modeling phases. Results are presented and discussed in Section 3 (equivalent to the CRISP-DM evaluation phase). Section 4 presents the study main conclusions.