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Luo Z, Zhang L, Liu N, Wu Y. Time series clustering of COVID-19 pandemic-related data. DATA SCIENCE AND MANAGEMENT 2023. [PMCID: PMC10050198 DOI: 10.1016/j.dsm.2023.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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Dhandapani S, Jerald Rodriguez AR. Poor and rich dolphin optimization algorithm with modified deep fuzzy clustering for COVID-19 patient analysis. CONCURRENCY AND COMPUTATION : PRACTICE & EXPERIENCE 2023; 35:e7456. [PMID: 36718388 PMCID: PMC9877607 DOI: 10.1002/cpe.7456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 07/28/2022] [Accepted: 08/18/2022] [Indexed: 06/18/2023]
Abstract
The Coronavirus disease 2019 (COVID-19) is considered as a pandemic by the World Health Organization (WHO), which has spread worldwide. Over millions of peoples are infected across the globe and several people are died. However, the most worrying group of patients suffered from lung severity with respiratory failure. Hence, cluster analysis is utilized for examining the heterogeneity of diseases for determining various clinical phenotypes having the same traits. This article devises an optimization-driven technique for COVID-19 patient analysis using the spark framework. Here, the input data is partitioned and fed to different slave nodes. In slave node, the selection of imperative features is done using the proposed poor and rich dolphin optimization algorithm (PRDOA). The proposed PRDOA is obtained by combining poor and rich (PRO) and dolphin echolation (DE) algorithm. The fitness is newly devised considering Minkowski distance measure. The clustering is performed on the master node using the proposed Tanimoto-based deep fuzzy clustering (TDFC) for effective COVID-19 patient analysis. Thus, the proposed TDFC is obtained by incorporating Tanimoto concept and deep fuzzy clustering. The proposed PRDOA with TDFC offered enhanced performance with the highest clustering accuracy of 89.8%, dice coefficient of 90%, Jaccard coefficient of 85.7%, and rand coefficient of 85.7%.
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Cerqueti R, Ficcadenti V. Combining rank-size and k-means for clustering countries over the COVID-19 new deaths per million. CHAOS, SOLITONS, AND FRACTALS 2022; 158:111975. [PMID: 35291220 PMCID: PMC8913321 DOI: 10.1016/j.chaos.2022.111975] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/31/2022] [Accepted: 03/03/2022] [Indexed: 05/09/2023]
Abstract
This paper deals with the cluster analysis of selected countries based on COVID-19 new deaths per million data. We implement a statistical procedure that combines a rank-size exploration and a k-means approach for clustering. Specifically, we first carry out a best-fit exercise on a suitable polynomial rank-size law at an individual country level; then, we cluster the considered countries by adopting a k-means clustering procedure based on the calibrated best-fit parameters. The investigated countries are selected considering those with a high value for the Healthcare Access and Quality Index to make a consistent analysis and reduce biases from the data collection phase. Interesting results emerge from the meaningful interpretation of the parameters of the best-fit curves; in particular, we show some relevant properties of the considered countries when dealing with the days with the highest number of new daily deaths per million and waves. Moreover, the exploration of the obtained clusters allows explaining some common countries' features.
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Affiliation(s)
- Roy Cerqueti
- Sapienza University of Rome, Department of Social and Economic Sciences, Piazzale Aldo Moro, 5, 00185 Rome, Italy
- London South Bank University, Business School, Borough Road, 103, SE1 0AA London, United Kingdom
- GRANEM, University of Angers, France
| | - Valerio Ficcadenti
- London South Bank University, Business School, Borough Road, 103, SE1 0AA London, United Kingdom
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Raj A, Bhattacharyya P, Gupta GR. Clusters of COVID-19 Indicators in India: Characterization, Correspondence and Change Analysis. SN COMPUTER SCIENCE 2022; 3:210. [PMID: 35400015 PMCID: PMC8981186 DOI: 10.1007/s42979-022-01083-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 03/04/2022] [Indexed: 11/26/2022]
Abstract
We conduct a long-term epidemiology study of COVID-19 in India from Mar 2020 to May 2021 using a number of indicators such as active cases, daily new cases, and deaths, on a micro (district level, per capita) and macro level (state level). Our automated shape-based cluster discovery of the per capita daily new cases (case rate) during the first wave in India (between Mar 2020 and Jan 2021) revealed four distinct shape patterns: sharp-rise and decline, steady-rise and decline, plateau and multiple relatively high peaks. These clusters exhibit a strong geographical correlation. To determine the correspondence between clusters obtained by different indicators, we design a novel metric for determining edge-weights in their intersection graph. This is used for comparative analysis and to develop informative hierarchical cartographic visualizations. We then perform dynamic cluster analysis for different time windows to answer some pertinent questions. Is the second wave similar to or different from the first wave? How has the relative ranking (on micro- and macro-level indicators) of the states varied over the last one year? How much medical resources have been stressed during the peak? We demonstrate that using multiple indicators, we can assess the impact of the epidemic holistically in a particular geography. Our analysis techniques and insights obtained can help the local and state governments in monitoring and managing COVID-19 situation and fine-tuning the ongoing vaccination drive in India.
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Fidan H, Erkan Yuksel M. A comparative study for determining Covid-19 risk levels by unsupervised machine learning methods. EXPERT SYSTEMS WITH APPLICATIONS 2022; 190:116243. [PMID: 34815623 PMCID: PMC8603411 DOI: 10.1016/j.eswa.2021.116243] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/05/2021] [Accepted: 11/14/2021] [Indexed: 05/24/2023]
Abstract
The restrictions have been preferred by governments to reduce the spread of Covid-19 and to protect people's health according to regional risk levels. The risk levels of locations are determined due to threshold values based on the number of cases per 100,000 people without environmental variables. The purpose of our study is to apply unsupervised machine learning techniques to determine the cities with similar risk levels by using the number of cases and environmental parameters. Hierarchical, partitional, soft, and gray relational clustering algorithms were applied to different datasets created with weekly the number of cases, population densities, average ages, and air pollution levels. Comparisons of the clustering algorithms were performed by using internal validation indexes, and the most successful method was identified. In the study, it was revealed that the most successful method in clustering based on the number of cases is Gray Relational Clustering. The results show that using the environmental variables for restrictions requires more clusters than 4 for healthier decisions and Gray Relational Clustering gives stable results, unlike other algorithms.
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Affiliation(s)
- Huseyin Fidan
- Department of Industrial Engineering, Faculty of Engineering-Architecture, Burdur Mehmet Akif Ersoy University, Burdur, Turkey
| | - Mehmet Erkan Yuksel
- Department of Computer Engineering, Faculty of Engineering-Architecture, Burdur Mehmet Akif Ersoy University, Burdur, Turkey
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Shi N, Huang J, Ai J, Wang Q, Cui T, Yang L, Ji H, Bao C, Jin H. Transmissibility and pathogenicity of the severe acute respiratory syndrome coronavirus 2: A systematic review and meta-analysis of secondary attack rate and asymptomatic infection. J Infect Public Health 2022; 15:297-306. [PMID: 35123279 PMCID: PMC8801962 DOI: 10.1016/j.jiph.2022.01.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 11/16/2021] [Accepted: 01/23/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Understanding the transmissibility and pathogenicity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial for control policies, but evidence remains limited. METHODS We presented a systematic and meta-analytic summary concerning the transmissibility and pathogenicity of COVID-19. RESULTS A total of 105 studies were identified, with 35042 infected cases and 897912 close contacts. 48.6% (51/105) of studies on secondary transmissions were from China. We estimated a total SIR of 7.8% (95% confidence interval [CI], 6.8%-8.8%), SAR of 6.6% (95% CI, 5.7%-7.5%), and symptomatic infection ratio of 86.9% (95%CI, 83.9%-89.9%) with a disease series interval of 5.84 (95%CI, 4.92-6.94) days. Household contacts had a higher risk of both symptomatic and asymptomatic infection, and transmission was driven between index cases and second-generation cases, with little transmission occurring in second-to-later-generation cases (SIR, 12.4% vs. 3.6%). The symptomatic infection ratio was not significantly different in terms of infection time, generation, type of contact, and index cases. CONCLUSIONS Our results suggest a higher risk of infection among household contacts. Transmissibility decreased with generations during the intervention. Pathogenicity of SARS-CoV-2 varied among territories, but didn't change over time. Strict isolation and medical observation measures should be implemented.
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Affiliation(s)
- Naiyang Shi
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Jinxin Huang
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Jing Ai
- Jiangsu Center of Disease Control and Prevention, Nanjing 210009, China
| | - Qiang Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Tingting Cui
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Liuqing Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Hong Ji
- Jiangsu Center of Disease Control and Prevention, Nanjing 210009, China
| | - Changjun Bao
- Jiangsu Center of Disease Control and Prevention, Nanjing 210009, China
| | - Hui Jin
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China.
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Wiliński A, Kupracz Ł, Senejko A, Chrząstek G. COVID-19: average time from infection to death in Poland, USA, India and Germany. QUALITY & QUANTITY 2022; 56:4729-4746. [PMID: 35194255 PMCID: PMC8853365 DOI: 10.1007/s11135-022-01340-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/31/2022] [Indexed: 10/26/2022]
Abstract
There are many discussions in the media about an interval (delay) from the time of the infections to deaths. Apart from the curiosity of the researchers, defining this time interval may, under certain circumstances, be of great organizational and economic importance. The study considers an attempt to determine this difference through the correlations of shifted time series and a specific bootstrapping that allows finding the distance between local maxima on the series under consideration. We consider data from Poland, the USA, India and Germany. The median of the difference's distribution is quite consistent for such diverse countries. The main conclusion of our research is that the searched interval has rather a multimodal form than unambiguously determined.
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Xiang L, Ma S, Yu L, Wang W, Yin Z. Modeling the Global Dynamic Contagion of COVID-19. Front Public Health 2022; 9:809987. [PMID: 35096753 PMCID: PMC8795671 DOI: 10.3389/fpubh.2021.809987] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 11/15/2021] [Indexed: 11/13/2022] Open
Abstract
The COVID-19 infections have profoundly and negatively impacted the whole world. Hence, we have modeled the dynamic spread of global COVID-19 infections with the connectedness approach based on the TVP-VAR model, using the data of confirmed COVID-19 cases during the period of March 23rd, 2020 to September 10th, 2021 in 18 countries. The results imply that, (i) the United States, the United Kingdom and Indonesia are global epidemic centers, among which the United States has the highest degree of the contagion of the COVID-19 infections, which is stable. South Korea, France and Italy are the main receiver of the contagion of the COVID-19 infections, and South Korea has been the most severely affected by the overseas epidemic; (ii) there is a negative correlation between the timeliness, effectiveness and mandatory nature of government policies and the risk of the associated countries COVID-19 epidemic affecting, as well as the magnitude of the net contagion of domestic COVID-19; (iii) the severity of domestic COVID-19 epidemics in the United States and Canada, Canada and Mexico, Indonesia and Canada is almost equivalent, especially for the United States, Canada and Mexico, whose domestic epidemics are with the same tendency; (iv) the COVID-19 epidemic has spread though not only the central divergence manner and chain mode of transmission, but also the way of feedback loop. Thus, more efforts should be made by the governments to enhance the pertinence and compulsion of their epidemic prevention policies and establish a systematic and efficient risk assessment mechanism for public health emergencies.
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Affiliation(s)
- Lijin Xiang
- School of Finance, Shandong University of Finance and Economics, Jinan, China
| | - Shiqun Ma
- School of Finance, Shandong University of Finance and Economics, Jinan, China
| | - Lu Yu
- School of Finance, Shandong University of Finance and Economics, Jinan, China
| | - Wenhao Wang
- School of Finance, Shandong University of Finance and Economics, Jinan, China
| | - Zhichao Yin
- School of Finance, Shandong University of Finance and Economics, Jinan, China
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Nobi A, Tuhin KH, Lee JW. Application of principal component analysis on temporal evolution of COVID-19. PLoS One 2021; 16:e0260899. [PMID: 34855909 PMCID: PMC8638895 DOI: 10.1371/journal.pone.0260899] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 11/18/2021] [Indexed: 11/20/2022] Open
Abstract
The COVID-19 is one of the worst pandemics in modern history. We applied principal component analysis (PCA) to the daily time series of the COVID-19 death cases and confirmed cases for the top 25 countries from April of 2020 to February of 2021. We calculated the eigenvalues and eigenvectors of the cross-correlation matrix of the changes in daily accumulated data over monthly time windows. The largest eigenvalue describes the overall evolution dynamics of the COVID-19 and indicates that evolution was faster in April of 2020 than in any other period. By using the first two PC coefficients, we can identify the group dynamics of the COVID-19 evolution. We observed groups under critical states in the loading plot and found that American and European countries are represented by strong clusters in the loading plot. The first PC plays an important role and the correlations (C1) between the normalized logarithmic changes in deaths or confirmed cases and the first PCs may be used as indicators of different phases of the COVID-19. By varying C1 over time, we identified different phases of the COVID-19 in the analyzed countries over the target time period.
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Affiliation(s)
- Ashadun Nobi
- Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Sonapur Noakhali, Bangladesh
| | - Kamrul Hasan Tuhin
- Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Sonapur Noakhali, Bangladesh
| | - Jae Woo Lee
- Department of Physics, Inha University, Incheon, Republic of Korea
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Sadeghi B, Cheung RCY, Hanbury M. Using hierarchical clustering analysis to evaluate COVID-19 pandemic preparedness and performance in 180 countries in 2020. BMJ Open 2021; 11:e049844. [PMID: 34753756 PMCID: PMC8578186 DOI: 10.1136/bmjopen-2021-049844] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
OBJECTIVE To rank and score 180 countries according to COVID-19 cases and fatality in 2020 and compare the results to existing pandemic vulnerability prediction models and results generated by standard epidemiological scoring techniques. SETTING One hundred and eighty countries' patients with COVID-19 and fatality data representing the healthcare system preparedness and performance in combating the pandemic in 2020. DESIGN Using the retrospective daily COVID-19 data in 2020 broken into 24 half-month periods, we applied unsupervised machine learning techniques, in particular, hierarchical clustering analysis to cluster countries into five groups within each period according to their cumulative COVID-19 fatality per day over the year and cumulative COVID-19 cases per million population per day over the half-month period. We used the average of the period scores to assign countries' final scores for each measure. PRIMARY OUTCOME The primary outcomes are the COVID-19 cases and fatality grades in 2020. RESULTS The United Arab Emirates and the USA with F in COVID-19 cases, achieved A or B in the fatality scores. Belgium and Sweden ranked F in both scores. Although no African country ranked F for COVID-19 cases, several African countries such as Gambia and Liberia had F for fatality scores. More developing countries ranked D and F in fatality than in COVID-19 case rankings. The classic epidemiological measures such as averages and rates have a relatively good correlation with our methodology, but past predictions failed to forecast the COVID-19 countries' preparedness. CONCLUSION COVID-19 fatality can be a good proxy for countries' resources and system's resilience in managing the pandemic. These findings suggest that countries' economic and sociopolitical factors may behave in a more complex way as were believed. To explore these complex epidemiological associations, models can benefit enormously by taking advantage of methods developed in computer science and machine learning.
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Affiliation(s)
| | - Rex C Y Cheung
- Department of Decision Sciences, San Francisco State University, San Francisco, California, USA
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11
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James N, Menzies M. Trends in COVID-19 prevalence and mortality: A year in review. PHYSICA D. NONLINEAR PHENOMENA 2021; 425:132968. [PMID: 34121785 PMCID: PMC8183049 DOI: 10.1016/j.physd.2021.132968] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 05/10/2021] [Accepted: 06/01/2021] [Indexed: 05/21/2023]
Abstract
This paper introduces new methods to study the changing dynamics of COVID-19 cases and deaths among the 50 worst-affected countries throughout 2020. First, we analyse the trajectories and turning points of rolling mortality rates to understand at which times the disease was most lethal. We demonstrate five characteristic classes of mortality rate trajectories and determine structural similarity in mortality trends over time. Next, we introduce a class of virulence matrices to study the evolution of COVID-19 cases and deaths on a global scale. Finally, we introduce three-way inconsistency analysis to determine anomalous countries with respect to three attributes: countries' COVID-19 cases, deaths and human development indices. We demonstrate the most anomalous countries across these three measures are Pakistan, the United States and the United Arab Emirates.
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Affiliation(s)
- Nick James
- School of Mathematics and Statistics, University of Melbourne, Victoria, Australia
| | - Max Menzies
- Yau Mathematical Sciences Centre, Tsinghua University, Beijing, China
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12
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James N, Menzies M. Efficiency of communities and financial markets during the 2020 pandemic. CHAOS (WOODBURY, N.Y.) 2021; 31:083116. [PMID: 34470250 DOI: 10.1063/5.0054493] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
This paper investigates the relationship between the spread of the COVID-19 pandemic, the state of community activity, and the financial index performance across 20 countries. First, we analyze which countries behaved similarly in 2020 with respect to one of three multivariate time series: daily COVID-19 cases, Apple mobility data, and national equity index price. Next, we study the trajectories of all three of these attributes in conjunction to determine which exhibited greater similarity. Finally, we investigate whether country financial indices or mobility data responded more quickly to surges in COVID-19 cases. Our results indicate that mobility data and national financial indices exhibited the most similarity in their trajectories, with financial indices responding quicker. This suggests that financial market participants may have interpreted and responded to COVID-19 data more efficiently than governments. Furthermore, results imply that efforts to study community mobility data as a leading indicator for financial market performance during the pandemic were misguided.
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Affiliation(s)
- Nick James
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Max Menzies
- Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China
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13
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Rios RA, Nogueira T, Coimbra DB, Lopes TJS, Abraham A, Mello RFD. Country transition index based on hierarchical clustering to predict next COVID-19 waves. Sci Rep 2021; 11:15271. [PMID: 34315932 PMCID: PMC8316493 DOI: 10.1038/s41598-021-94661-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 07/01/2021] [Indexed: 02/07/2023] Open
Abstract
COVID-19 has widely spread around the world, impacting the health systems of several countries in addition to the collateral damage that societies will face in the next years. Although the comparison between countries is essential for controlling this disease, the main challenge is the fact of countries are not simultaneously affected by the virus. Therefore, from the COVID-19 dataset by the Johns Hopkins University Center for Systems Science and Engineering, we present a temporal analysis on the number of new cases and deaths among countries using artificial intelligence. Our approach incrementally models the cases using a hierarchical clustering that emphasizes country transitions between infection groups over time. Then, one can compare the current situation of a country against others that have already faced previous waves. By using our approach, we designed a transition index to estimate the most probable countries' movements between infectious groups to predict next wave trends. We draw two important conclusions: (1) we show the historical infection path taken by specific countries and emphasize changing points that occur when countries move between clusters with small, medium, or large number of cases; (2) we estimate new waves for specific countries using the transition index.
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Affiliation(s)
- Ricardo A Rios
- Institute of Computing, Federal University of Bahia, Salvador, Brazil.
| | - Tatiane Nogueira
- Institute of Computing, Federal University of Bahia, Salvador, Brazil
| | - Danilo B Coimbra
- Institute of Computing, Federal University of Bahia, Salvador, Brazil
| | - Tiago J S Lopes
- Department of Reproductive Biology, National Center for Child Health and Development Research Institute, Tokyo, Japan
| | | | - Rodrigo F de Mello
- Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil
- Itaú Unibanco, Av. Eng. Armando de Arruda Pereira, São Paulo, Brazil
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14
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Buszko M, Orzeszko W, Stawarz M. COVID-19 pandemic and stability of stock market-A sectoral approach. PLoS One 2021; 16:e0250938. [PMID: 34014941 PMCID: PMC8136637 DOI: 10.1371/journal.pone.0250938] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 04/16/2021] [Indexed: 12/23/2022] Open
Abstract
The COVID-19 pandemic seems to be the most important phenomenon observed from March 2020 in virtually all countries of the world. The necessity to prevent the spread of COVID-19 and keep health care systems efficient resulted in the forced, drastic limitation of economic activity. Many service sectors were hit particularly hard with this but industry and agriculture were also affected. In particular, the pandemic substantially influenced financial markets and we can observe that some markets or instruments vary in stability since they have been affected in the different degree. In the paper, we present the problem of stability of stock markets during the COVID-19 pandemic. Due to the low number of works related to CEE countries during the pandemic, we analyze the Warsaw Stock Exchange, which is one of the most important markets in the CEE. Our main goal was to find how various industries represented by stock market indices have reacted to the COVID-19 shock and consequently which sectors turned out to keep stability and remained resistant to the pandemic. In our investigation, we use two clustering methods: the K-means and the Ward techniques with the criterion of maximizing the silhouette coefficient and six indicators describing stability in terms of profitability, volume, overbought/oversold conditions and volatility. The results of the research present that during the pandemic it was possible to identify 5 clusters of sector indices in the short term and 4 in the medium term. We found that the composition of the clusters is quite stable over time and that none of the obtained clusters can be univocally considered the most or the least stable taking into account all the analyzed indicators. However, we showed that the obtained clusters have different stability origins, i.e. they vary from each other in terms of the investigated indicators of stability.
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Affiliation(s)
- Michał Buszko
- Department of Financial Management, Faculty of Economic Sciences and Management, Nicolaus Copernicus University in Torun, Torun, Poland
| | - Witold Orzeszko
- Department of Applied Informatics and Mathematics in Economics, Faculty of Economic Sciences and Management, Nicolaus Copernicus University in Torun, Torun, Poland
| | - Marcin Stawarz
- Department of Applied Informatics and Mathematics in Economics, Faculty of Economic Sciences and Management, Nicolaus Copernicus University in Torun, Torun, Poland
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15
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James N. Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19. PHYSICA A 2021; 570:125831. [PMID: 36570814 PMCID: PMC9758953 DOI: 10.1016/j.physa.2021.125831] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 01/28/2021] [Indexed: 05/14/2023]
Abstract
This paper uses new and recently introduced methodologies to study the similarity in the dynamics and behaviours of cryptocurrencies and equities surrounding the COVID-19 pandemic. We study two collections; 45 cryptocurrencies and 72 equities, both independently and in conjunction. First, we examine the evolution of cryptocurrency and equity market dynamics, with a particular focus on their change during the COVID-19 pandemic. We demonstrate markedly more similar dynamics during times of crisis. Next, we apply recently introduced methods to contrast trajectories, erratic behaviours, and extreme values among the two multivariate time series. Finally, we introduce a new framework for determining the persistence of market anomalies over time. Surprisingly, we find that although cryptocurrencies exhibit stronger collective dynamics and correlation in all market conditions, equities behave more similarly in their trajectories and extremes, and show greater persistence in anomalies over time.
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Affiliation(s)
- Nick James
- School of Mathematics and Statistics, University of Sydney, NSW, Australia
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16
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James N, Menzies M, Radchenko P. COVID-19 second wave mortality in Europe and the United States. CHAOS (WOODBURY, N.Y.) 2021; 31:031105. [PMID: 33810707 DOI: 10.1063/5.0041569] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 02/09/2021] [Indexed: 05/19/2023]
Abstract
This paper introduces new methods to analyze the changing progression of COVID-19 cases to deaths in different waves of the pandemic. First, an algorithmic approach partitions each country or state's COVID-19 time series into a first wave and subsequent period. Next, offsets between case and death time series are learned for each country via a normalized inner product. Combining these with additional calculations, we can determine which countries have most substantially reduced the mortality rate of COVID-19. Finally, our paper identifies similarities in the trajectories of cases and deaths for European countries and U.S. states. Our analysis refines the popular conception that the mortality rate has greatly decreased throughout Europe during its second wave of COVID-19; instead, we demonstrate substantial heterogeneity throughout Europe and the U.S. The Netherlands exhibited the largest reduction of mortality, a factor of 16, followed by Denmark, France, Belgium, and other Western European countries, greater than both Eastern European countries and U.S. states. Some structural similarity is observed between Europe and the United States, in which Northeastern states have been the most successful in the country. Such analysis may help European countries learn from each other's experiences and differing successes to develop the best policies to combat COVID-19 as a collective unit.
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Affiliation(s)
- Nick James
- School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia
| | - Max Menzies
- Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China
| | - Peter Radchenko
- School of Business, University of Sydney, NSW 2006, Australia
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James N, Menzies M, Chan J. Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19. PHYSICA A 2021; 565:125581. [PMID: 33250564 PMCID: PMC7687370 DOI: 10.1016/j.physa.2020.125581] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 11/12/2020] [Indexed: 05/07/2023]
Abstract
This paper introduces new methods for analysing the extreme and erratic behaviour of time series to evaluate the impact of COVID-19 on cryptocurrency market dynamics. Across 51 cryptocurrencies, we examine extreme behaviour through a study of distribution extremities, and erratic behaviour through structural breaks. First, we analyse the structure of the market as a whole and observe a reduction in self-similarity as a result of COVID-19, particularly with respect to structural breaks in variance. Second, we compare and contrast these two behaviours, and identify individual anomalous cryptocurrencies. Tether (USDT) and TrueUSD (TUSD) are consistent outliers with respect to their returns, while Holo (HOT), NEXO (NEXO), Maker (MKR) and NEM (XEM) are frequently observed as anomalous with respect to both behaviours and time. Even among a market known as consistently volatile, this identifies individual cryptocurrencies that behave most irregularly in their extreme and erratic behaviour and shows these were more affected during the COVID-19 market crisis.
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Affiliation(s)
- Nick James
- School of Mathematics and Statistics, University of Sydney, NSW, Australia
| | - Max Menzies
- Yau Mathematical Sciences Center, Tsinghua University, Beijing, China
| | - Jennifer Chan
- School of Mathematics and Statistics, University of Sydney, NSW, Australia
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James N, Menzies M. Association between COVID-19 cases and international equity indices. PHYSICA D. NONLINEAR PHENOMENA 2021; 417:132809. [PMID: 33362322 PMCID: PMC7756167 DOI: 10.1016/j.physd.2020.132809] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 11/17/2020] [Accepted: 11/17/2020] [Indexed: 05/03/2023]
Abstract
This paper analyzes the impact of COVID-19 on the populations and equity markets of 92 countries. We compare country-by-country equity market dynamics to cumulative COVID-19 case and death counts and new case trajectories. First, we examine the multivariate time series of cumulative cases and deaths, particularly regarding their changing structure over time. We reveal similarities between the case and death time series, and key dates that the structure of the time series changed. Next, we classify new case time series, demonstrate five characteristic classes of trajectories, and quantify discrepancy between them with respect to the behavior of waves of the disease. Finally, we show there is no relationship between countries' equity market performance and their success in managing COVID-19. Each country's equity index has been unresponsive to the domestic or global state of the pandemic. Instead, these indices have been highly uniform, with most movement in March.
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Affiliation(s)
- Nick James
- School of Mathematics and Statistics, University of Sydney, NSW, Australia
| | - Max Menzies
- Yau Mathematical Sciences Center, Tsinghua University, Beijing, China
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James N, Menzies M. COVID-19 in the United States: Trajectories and second surge behavior. CHAOS (WOODBURY, N.Y.) 2020; 30:091102. [PMID: 33003920 PMCID: PMC7519449 DOI: 10.1063/5.0024204] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
This paper introduces a mathematical framework for determining second surge behavior of COVID-19 cases in the United States. Within this framework, a flexible algorithmic approach selects a set of turning points for each state, computes distances between them, and determines whether each state is in (or over) a first or second surge. Then, appropriate distances between normalized time series are used to further analyze the relationships between case trajectories on a month-by-month basis. Our algorithm shows that 31 states are experiencing second surges, while four of the 10 largest states are still in their first surge, with case counts that have never decreased. This analysis can aid in highlighting the most and least successful state responses to COVID-19.
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Affiliation(s)
- Nick James
- School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia
| | - Max Menzies
- Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China
- Author to whom correspondence should be addressed:
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