Time series clustering of COVID-19 pandemic-related data

Luo, Zhixue; Zhang, Lin; Liu, Na; Wu, Ye

doi:10.1016/j.dsm.2023.03.003

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…Dynamic time warping (DTW) is a widely used statistical algorithm ( 30 , 45 ), but its application in identifying various disease transmission patterns has been limited. Recently, multiple studies have used DTW to analyze the trajectories of COVID-19 in different countries, aiming to identify, cluster and predict future trends in disease transmission ( 46–49 ). Here, we utilized DTW to examine and visualize similarities of RSV time-series, yielding clusters of RSV activity before and after the 2009 influenza pandemic in the ten different regions of the US.…”

Section: Discussionmentioning

confidence: 99%

The role of viral interference in shaping RSV epidemics following the 2009 H1N1 influenza pandemic

Li,

Thindwa,

Weinberger

et al. 2024

Preprint

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Respiratory syncytial virus (RSV) primarily affects infants, young children, and older adults, with seasonal outbreaks in the United States (US) peaking around December or January. Despite the limited implementation of non-pharmaceutical interventions, disrupted RSV activity was observed in different countries following the 2009 influenza pandemic, suggesting possible viral interference from influenza. Although interactions between the influenza A/H1N1 pandemic virus and RSV have been demonstrated at an individual level, it remains unclear whether the disruption of RSV activity at the population level can be attributed to viral interference. In this work, we first evaluated changes in the timing and intensity of RSV activity across 10 regions of the US in the years following the 2009 influenza pandemic using dynamic time warping. We observed a reduction in RSV activity following the pandemic, which was associated with intensity of influenza activity in the region. We then developed an age-stratified, two-pathogen model to examine various hypotheses regarding viral interference mechanisms. Based on our model estimates, we identified three mechanisms through which influenza infections could interfere with RSV: 1) reducing susceptibility to RSV coinfection; 2) shortening the RSV infectious period in coinfected individuals; and 3) reducing RSV infectivity in coinfection. Our study offers statistical support for the occurrence of atypical RSV seasons following the 2009 influenza pandemic. Our work also offers new insights into the mechanisms of viral interference that contribute to disruptions in RSV epidemics and provides a model-fitting framework that enables the analysis of new surveillance data for studying viral interference at the population level.

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Section: Discussionmentioning

confidence: 99%

The role of viral interference in shaping RSV epidemics following the 2009 H1N1 influenza pandemic

Li,

Thindwa,

Weinberger

et al. 2024

Preprint

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“…The dynamic time warping distance and hierarchical cluster were also used by [38] to cluster the time series of daily new cases and deaths from different countries into four patterns. They found that geographic factors were important but not decisive for the pandemic development and that the population age may have also influenced the formation of cluster patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing the Impact of COVID-19 on Economic Sustainability: A Clustering Approach

Nicolis,

Maidana,

Contreras

et al. 2024

Sustainability

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This work presents a comprehensive analysis of the economic impact of the COVID-19 pandemic, with a focus on OECD countries and the Chilean case. Utilizing a clustering approach, the research aims to investigate how countries can be categorized based on their pandemic mitigation strategies, economic responses, and infection rates. The methodology incorporates k-means and hierarchical clustering techniques, along with dynamic time warping, to account for the temporal variations in the pandemic’s progression across different nations. The study integrates the GDP into the analysis, thereby offering a perspective on the relationship between this economic indicator and health measures. Special attention is given to the case of Chile, thus providing a detailed examination of its economic and financial indicators during the pandemic. In particular, the work addresses the following main research questions: How can the OECD countries be clustered according to some health and economical indicators? What are the impacts of mitigation measures and the pension fund withdrawals on the Chilean economy? The study identifies significant differences (p-value < 0.05%) in the GDPs and infection rates between the two identified clusters that are influenced by government measures, particularly in the banking sector (55% and 60% in clusters 1 and 2, respectively). In Chile, a rebound in the IMACEC index is noted after increased liquidity, especially following partial pension fund withdrawals, thereby aligning with discrepancies between model forecasts and actual data. This study provides important insights for evidence-based public policies, thus aiding decision makers in mitigating the socioeconomic impact of global health crises and offering strategic advice for a sustainable economy.

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“…[14]. Innovative integrations of methodologies in data analysis are developed-for example, for data clustering [15][16][17], applying systems biology approaches [18] similar to coarse-grained gene expression data along the cell cycle [19], ideas of phase synchronisation [20] and others. In addition, the epidemic modelling and simulations have two primary goals.…”

Section: Introductionmentioning

confidence: 99%

Robust Global Trends during Pandemics: Analysing the Interplay of Biological and Social Processes

Mitrović Dankulov,

Tadić,

Melnik

2023

Dynamics

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The essence of the stochastic processes behind the empirical data on infection and fatality during pandemics is the complex interdependence between biological and social factors. Their balance can be checked on the data of new virus outbreaks, where the population is unprepared to fight the viral biology and social measures and healthcare systems adjust with a delay. Using a complex systems perspective, we combine network mapping with K-means clustering and multifractal detrended fluctuations analysis to identify typical trends in fatality rate data. We analyse global data of (normalised) fatality time series recorded during the first two years of the recent pandemic caused by the severe acute respiratory syndrome coronavirus 2 as an appropriate example. Our results reveal six clusters with robust patterns of mortality progression that represent specific adaptations to prevailing biological factors. They make up two significant groups that coincide with the topological communities of the correlation network, with stabilising (group g1) and continuously increasing rates (group g2). Strong cyclic trends and multifractal small-scale fluctuations around them characterise these patterns. The rigorous analysis and the proposed methodology shed more light on the complex nonlinear shapes of the pandemic’s main characteristic curves, which have been discussed extensively in the literature regarding the global infectious diseases that have affected humanity throughout its history. In addition to better pandemic preparedness in the future, the presented methodology can also help to differentiate and predict other trends in pandemics, such as fatality rates, caused simultaneously by different viruses in particular geographic locations.

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Time series clustering of COVID-19 pandemic-related data

Cited by 7 publications

References 33 publications

The role of viral interference in shaping RSV epidemics following the 2009 H1N1 influenza pandemic

The role of viral interference in shaping RSV epidemics following the 2009 H1N1 influenza pandemic

Analyzing the Impact of COVID-19 on Economic Sustainability: A Clustering Approach

Robust Global Trends during Pandemics: Analysing the Interplay of Biological and Social Processes

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