Dennis Dingen, Marcel Van 't Veer, T. Bakkes, Erik Korsten, Arthur Bouwman, J. van Wijk
The gold standard in medical research to estimate the causal effect of a treatment is the Randomized Controlled Trial (RCT), but in many cases these are not feasible due to ethical, financial or practical issues. Observational studies are an alternative, but can easily lead to doubtful results, because of unbalanced selection bias and confounding. Moreover, RCTs often only apply to a specific subgroup and cannot readily be extrapolated. In response, we present Rod of Asclepius (RoA), a novel visual analytics method that integrates modern techniques designed for identification of causal effects and effect size estimation with subgroup analysis. The result is an interactive display designed to combine exploratory analysis with a robust set of techniques, including causal do-calculus, propensity score weighting, and effect estimation. It enables analysts to conduct observational studies in an exploratory, yet robust way. This is demonstrated by means of a use case involving patients undergoing surgery, for which we collaborated closely with clinical researchers.
在医学研究中，估算治疗效果的黄金标准是随机对照试验（RCT），但在很多情况下，由于伦理、经济或实际问题，随机对照试验并不可行。观察研究是一种替代方法，但由于不平衡的选择偏差和混杂因素，很容易导致可疑的结果。此外，RCT 通常只适用于特定的亚组，不能轻易推断。为此，我们提出了Rod of Asclepius (RoA)，这是一种新颖的可视化分析方法，它将用于识别因果效应和效应大小估计的现代技术与亚组分析相结合。它是一种交互式显示，旨在将探索性分析与一套强大的技术（包括因果计算、倾向得分加权和效应估计）结合起来。它使分析人员能够以探索性但稳健的方式开展观察研究。我们与临床研究人员密切合作，通过一个涉及手术患者的使用案例来证明这一点。
{"title":"RoA: visual analytics support for deconfounded causal inference in observational studies","authors":"Dennis Dingen, Marcel Van 't Veer, T. Bakkes, Erik Korsten, Arthur Bouwman, J. van Wijk","doi":"10.52933/jdssv.v4i3.72","DOIUrl":"https://doi.org/10.52933/jdssv.v4i3.72","url":null,"abstract":"The gold standard in medical research to estimate the causal effect of a treatment is the Randomized Controlled Trial (RCT), but in many cases these are not feasible due to ethical, financial or practical issues. Observational studies are an alternative, but can easily lead to doubtful results, because of unbalanced selection bias and confounding. Moreover, RCTs often only apply to a specific subgroup and cannot readily be extrapolated. In response, we present Rod of Asclepius (RoA), a novel visual analytics method that integrates modern techniques designed for identification of causal effects and effect size estimation with subgroup analysis. The result is an interactive display designed to combine exploratory analysis with a robust set of techniques, including causal do-calculus, propensity score weighting, and effect estimation. It enables analysts to conduct observational studies in an exploratory, yet robust way. This is demonstrated by means of a use case involving patients undergoing surgery, for which we collaborated closely with clinical researchers.","PeriodicalId":93459,"journal":{"name":"Journal of data science, statistics, and visualisation","volume":"4 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141265970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Crossover designs play an increasingly important role in precision medicine. We show the search of an optimal crossover design can be formulated as a convex optimization problem and convex optimization tools, such as CVX, can be directly used to search for an optimal crossover design. We first demonstrate how to transform crossover design problems into convex optimization problems and show CVX can effortlessly find optimal crossover designs that coincide with a few theoretical crossover optimal designs in the literature. The proposed approach is especially useful when it becomes problematic to construct optimal designs analytically for complicated models. We then apply CVX to find crossover designs for models with auto-correlated error structures or when the information matrices may be singular and analytical answers are unavailable. We also construct N-of-1 trials frequently used in precision medicine to estimate treatment effects on the individuals or to estimate average treatment effects, including finding dual-objective optimal crossover designs.
{"title":"An Efficient Way to Find Optimal Crossover Designs Using CVX for Precision Medicine","authors":"Yin Li, Weng Kee Wong, Hua Zhou, Keumhee Chough Carriere","doi":"10.52933/jdssv.v4i3.83","DOIUrl":"https://doi.org/10.52933/jdssv.v4i3.83","url":null,"abstract":"Crossover designs play an increasingly important role in precision medicine. We show the search of an optimal crossover design can be formulated as a convex optimization problem and convex optimization tools, such as CVX, can be directly used to search for an optimal crossover design. We first demonstrate how to transform crossover design problems into convex optimization problems and show CVX can effortlessly find optimal crossover designs that coincide with a few theoretical crossover optimal designs in the literature. The proposed approach is especially useful when it becomes problematic to construct optimal designs analytically for complicated models. We then apply CVX to find crossover designs for models with auto-correlated error structures or when the information matrices may be singular and analytical answers are unavailable. We also construct N-of-1 trials frequently used in precision medicine to estimate treatment effects on the individuals or to estimate average treatment effects, including finding dual-objective optimal crossover designs.","PeriodicalId":93459,"journal":{"name":"Journal of data science, statistics, and visualisation","volume":"9 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141265751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Cappello, Nikolaus Piccolotto, C. Muehlmann, M. Bögl, Peter Filzmoser, Silvia Miksch, K. Nordhausen
Many fields of science and industry collect and analyze multivariate time-varying measurements, e.g., healthcare, geophysics, or finance. Such data is often high-dimensional, correlated, and noisy. Experts are interested in latent components of the dataset, but due to the aforementioned properties these are difficult to obtain. Temporal Blind Source Separation (TBSS) is a suitable and well-established framework for these data. However, the large choice of methods and their tuning parameters impedes the effective use of TBSS in practice. The goal of Visual Analytics (VA) is to create powerful analytic tools by combining the strengths of humans and computers. We designed, developed, and evaluated VA contributions in previous work to support TBSS-related analysis tasks. In this paper, we highlight the benefits and opportunities of VA concepts for statistic-oriented problems using a real-world TBSS application example with a dataset of climate and meteorological measurements in Italy.
{"title":"Visual Interactive Parameter Selection for Temporal Blind Source Separation","authors":"C. Cappello, Nikolaus Piccolotto, C. Muehlmann, M. Bögl, Peter Filzmoser, Silvia Miksch, K. Nordhausen","doi":"10.52933/jdssv.v4i3.82","DOIUrl":"https://doi.org/10.52933/jdssv.v4i3.82","url":null,"abstract":"Many fields of science and industry collect and analyze multivariate time-varying measurements, e.g., healthcare, geophysics, or finance. Such data is often high-dimensional, correlated, and noisy. Experts are interested in latent components of the dataset, but due to the aforementioned properties these are difficult to obtain. Temporal Blind Source Separation (TBSS) is a suitable and well-established framework for these data. However, the large choice of methods and their tuning parameters impedes the effective use of TBSS in practice. The goal of Visual Analytics (VA) is to create powerful analytic tools by combining the strengths of humans and computers. We designed, developed, and evaluated VA contributions in previous work to support TBSS-related analysis tasks. In this paper, we highlight the benefits and opportunities of VA concepts for statistic-oriented problems using a real-world TBSS application example with a dataset of climate and meteorological measurements in Italy.","PeriodicalId":93459,"journal":{"name":"Journal of data science, statistics, and visualisation","volume":"5 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141265833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose an edge preserving median filter, called the level-set adaptive median filter, for noise removal in images. This filter uses connected sets of pixels with the same value, namely level-sets, as flexible regions which contour to edges in the image. The filter determines whether a set is noise or signal and smooths the noise. These set regions are flexible in terms of shape since they are created based on their values, and being data-driven therefore provide the mechanism for the filter to preserve edges in the image. We used metrics such as Pratt's Figure of Merit and Peak-Signal-to-Noise Ratio on the labelled faces in the wild data set. We concluded that the proposed level-set adaptive median filter does remove noise while preserving the edges in the image better than the traditional adaptive median filter.
{"title":"An edge preserving median filter for images based on level-sets","authors":"Jean-Pierre Stander","doi":"10.52933/jdssv.v4i3.74","DOIUrl":"https://doi.org/10.52933/jdssv.v4i3.74","url":null,"abstract":"We propose an edge preserving median filter, called the level-set adaptive median filter, for noise removal in images. This filter uses connected sets of pixels with the same value, namely level-sets, as flexible regions which contour to edges in the image. The filter determines whether a set is noise or signal and smooths the noise. These set regions are flexible in terms of shape since they are created based on their values, and being data-driven therefore provide the mechanism for the filter to preserve edges in the image. We used metrics such as Pratt's Figure of Merit and Peak-Signal-to-Noise Ratio on the labelled faces in the wild data set. We concluded that the proposed level-set adaptive median filter does remove noise while preserving the edges in the image better than the traditional adaptive median filter.","PeriodicalId":93459,"journal":{"name":"Journal of data science, statistics, and visualisation","volume":"5 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141266182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tree-based regression and classification has become a standard tool in modern data science. Bayesian Additive Regression Trees (BART) has in particular gained wide popularity due its flexibility in dealing with interactions and non-linear effects. BART is a Bayesian tree-based machine learning method that can be applied to both regression and classification problems and yields competitive or superior results when compared to other predictive models. As a Bayesian model, BART allows the practitioner to explore the uncertainty around predictions through the posterior distribution. In this paper, we present new Visualisation techniques for exploring BART models. We construct conventional plots to analyse a model’s performance and stability as well as create new tree-based plots to analyse variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our new Visualisations are designed to work with the most popular BART R packages available, namely BART, dbarts, and bartMachine. Our approach is implemented in the R package bartMan (BART Model ANalysis).
基于树的回归和分类已成为现代数据科学的标准工具。贝叶斯加性回归树（BART）因其在处理交互和非线性效应方面的灵活性，尤其受到广泛欢迎。BART 是一种基于贝叶斯树的机器学习方法，既可应用于回归问题，也可应用于分类问题，与其他预测模型相比，它能产生有竞争力或更优越的结果。作为一种贝叶斯模型，BART 允许实践者通过后验分布来探索预测的不确定性。在本文中，我们介绍了探索 BART 模型的新可视化技术。我们构建了常规图来分析模型的性能和稳定性，并创建了新的树状图来分析变量的重要性、交互作用和树状结构。我们采用价值抑制不确定性调色板（VSUP）来构建热图，利用色标共同显示变量重要性和交互作用，以表示后验不确定性。我们的新可视化设计可与现有最流行的 BART R 软件包（即 BART、dbarts 和 bartMachine）配合使用。我们的方法是在 R 软件包 bartMan（BART 模型分析）中实现的。
{"title":"Visualisations for Bayesian Additive Regression Trees","authors":"Alan N. Inglis, Andrew Parnell, Catherine Hurley","doi":"10.52933/jdssv.v4i1.79","DOIUrl":"https://doi.org/10.52933/jdssv.v4i1.79","url":null,"abstract":"Tree-based regression and classification has become a standard tool in modern data science. Bayesian Additive Regression Trees (BART) has in particular gained wide popularity due its flexibility in dealing with interactions and non-linear effects. BART is a Bayesian tree-based machine learning method that can be applied to both regression and classification problems and yields competitive or superior results when compared to other predictive models. As a Bayesian model, BART allows the practitioner to explore the uncertainty around predictions through the posterior distribution. In this paper, we present new Visualisation techniques for exploring BART models. We construct conventional plots to analyse a model’s performance and stability as well as create new tree-based plots to analyse variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our new Visualisations are designed to work with the most popular BART R packages available, namely BART, dbarts, and bartMachine. Our approach is implemented in the R package bartMan (BART Model ANalysis).","PeriodicalId":93459,"journal":{"name":"Journal of data science, statistics, and visualisation","volume":"18 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139796981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tree-based regression and classification has become a standard tool in modern data science. Bayesian Additive Regression Trees (BART) has in particular gained wide popularity due its flexibility in dealing with interactions and non-linear effects. BART is a Bayesian tree-based machine learning method that can be applied to both regression and classification problems and yields competitive or superior results when compared to other predictive models. As a Bayesian model, BART allows the practitioner to explore the uncertainty around predictions through the posterior distribution. In this paper, we present new Visualisation techniques for exploring BART models. We construct conventional plots to analyse a model’s performance and stability as well as create new tree-based plots to analyse variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our new Visualisations are designed to work with the most popular BART R packages available, namely BART, dbarts, and bartMachine. Our approach is implemented in the R package bartMan (BART Model ANalysis).
基于树的回归和分类已成为现代数据科学的标准工具。贝叶斯加性回归树（BART）因其在处理交互和非线性效应方面的灵活性，尤其受到广泛欢迎。BART 是一种基于贝叶斯树的机器学习方法，既可应用于回归问题，也可应用于分类问题，与其他预测模型相比，它能产生有竞争力或更优越的结果。作为一种贝叶斯模型，BART 允许实践者通过后验分布来探索预测的不确定性。在本文中，我们介绍了探索 BART 模型的新可视化技术。我们构建了常规图来分析模型的性能和稳定性，并创建了新的树状图来分析变量的重要性、交互作用和树状结构。我们采用价值抑制不确定性调色板（VSUP）来构建热图，利用色标共同显示变量重要性和交互作用，以表示后验不确定性。我们的新可视化设计可与现有最流行的 BART R 软件包（即 BART、dbarts 和 bartMachine）配合使用。我们的方法是在 R 软件包 bartMan（BART 模型分析）中实现的。
{"title":"Visualisations for Bayesian Additive Regression Trees","authors":"Alan N. Inglis, Andrew Parnell, Catherine Hurley","doi":"10.52933/jdssv.v4i1.79","DOIUrl":"https://doi.org/10.52933/jdssv.v4i1.79","url":null,"abstract":"Tree-based regression and classification has become a standard tool in modern data science. Bayesian Additive Regression Trees (BART) has in particular gained wide popularity due its flexibility in dealing with interactions and non-linear effects. BART is a Bayesian tree-based machine learning method that can be applied to both regression and classification problems and yields competitive or superior results when compared to other predictive models. As a Bayesian model, BART allows the practitioner to explore the uncertainty around predictions through the posterior distribution. In this paper, we present new Visualisation techniques for exploring BART models. We construct conventional plots to analyse a model’s performance and stability as well as create new tree-based plots to analyse variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our new Visualisations are designed to work with the most popular BART R packages available, namely BART, dbarts, and bartMachine. Our approach is implemented in the R package bartMan (BART Model ANalysis).","PeriodicalId":93459,"journal":{"name":"Journal of data science, statistics, and visualisation","volume":"46 7-8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139857031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The objective of this article is to show how daily hospital data can be used to monitor the evolution of the COVID-19 epidemic in France. A piecewise defined dynamic model allows to fit very well the available hospital admission, death and discharge data. The change-points detected correspond to moments when the dynamics of the epidemic changed abruptly. It is therefore a surveillance tool, not a forecasting tool. In other words, it can be used effectively to warn of a restart of epidemic activity, but it is not designed to assess the impact of a new lockdown or the emergence of a new variant.The model, data and fits are implemented in an interactive web application.
{"title":"Using hospital data for monitoring the dynamics of COVID-19 in France","authors":"M. Lavielle","doi":"10.52933/jdssv.v2i7.48","DOIUrl":"https://doi.org/10.52933/jdssv.v2i7.48","url":null,"abstract":"The objective of this article is to show how daily hospital data can be used to monitor the evolution of the COVID-19 epidemic in France. A piecewise defined dynamic model allows to fit very well the available hospital admission, death and discharge data. The change-points detected correspond to moments when the dynamics of the epidemic changed abruptly. It is therefore a surveillance tool, not a forecasting tool. In other words, it can be used effectively to warn of a restart of epidemic activity, but it is not designed to assess the impact of a new lockdown or the emergence of a new variant.The model, data and fits are implemented in an interactive web application.","PeriodicalId":93459,"journal":{"name":"Journal of data science, statistics, and visualisation","volume":"76 1","pages":"46-62"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86948459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shih-Hsiung Chou, P. Turk, M. Kowalkowski, J. Kearns, J. Roberge, J. Priem, Y. Taylor, R. Burns, P. Palmer, A. McWilliams
We developed an interactive web-based, decision support application that can adapt to the rapid pace of change in region-specific pandemic related variables and knowledge, thereby providing timely, accurate insights to inform a large healthcare system’s proactive response to COVID-19 hospital resource planning. We designed the COVID-19 Utilization and Resource Visualization Engine (CURVE) app to be adaptable to real-time changes as the pandemic evolved, enabling decisions to be supported by contemporary local data and accurate predictive models. To demonstrate this flexibility, we sequentially implemented a Susceptible-Infected-Removed (SIR) model that incorporates social-distancing and imperfect detection (SIR-D2), an extended-state-space Bayesian SIR model (eSIR), and a time-series model (ARIMA). CURVE improves upon other pandemic forecasting solutions by providing adaptable decision support that generates locally calibrated forecasts aligned to health system specific data to guide COVID-19 pandemic planning. The app additionally enables systematic monitoring of forecast model performance and realignment that keeps pace with the pandemic’s volatile spread and behavior. CURVE provides a flexible pandemic decision support framework that places the most accurate, locally relevant information in front of decision makers to enable health systems to be proactive and prepared.
{"title":"Implementation of an Adaptable COVID-19 Utilization and Resource Visualization Engine (CURVE) to Depict In-Hospital Resource Forecasts Over Time","authors":"Shih-Hsiung Chou, P. Turk, M. Kowalkowski, J. Kearns, J. Roberge, J. Priem, Y. Taylor, R. Burns, P. Palmer, A. McWilliams","doi":"10.52933/jdssv.v2i7.19","DOIUrl":"https://doi.org/10.52933/jdssv.v2i7.19","url":null,"abstract":"We developed an interactive web-based, decision support application that can adapt to the rapid pace of change in region-specific pandemic related variables and knowledge, thereby providing timely, accurate insights to inform a large healthcare system’s proactive response to COVID-19 hospital resource planning. We designed the COVID-19 Utilization and Resource Visualization Engine (CURVE) app to be adaptable to real-time changes as the pandemic evolved, enabling decisions to be supported by contemporary local data and accurate predictive models. To demonstrate this flexibility, we sequentially implemented a Susceptible-Infected-Removed (SIR) model that incorporates social-distancing and imperfect detection (SIR-D2), an extended-state-space Bayesian SIR model (eSIR), and a time-series model (ARIMA). CURVE improves upon other pandemic forecasting solutions by providing adaptable decision support that generates locally calibrated forecasts aligned to health system specific data to guide COVID-19 pandemic planning. The app additionally enables systematic monitoring of forecast model performance and realignment that keeps pace with the pandemic’s volatile spread and behavior. CURVE provides a flexible pandemic decision support framework that places the most accurate, locally relevant information in front of decision makers to enable health systems to be proactive and prepared.","PeriodicalId":93459,"journal":{"name":"Journal of data science, statistics, and visualisation","volume":"5 1","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86846708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In December 2019, in the city of Wuhan (China), Severe Acute Respiratory Syndrome Coronavirus - 2 (SARS-CoV−2), a virus that causes what is known as Coronavirus Disease 2019 (better known as COVID-19), emerged. In a few months the virus spread around the world becoming a global pandemic that has shaken the world. On Malta (a nation consisting of an archipelago of islands of approximately 500000 people), which is the case study of this analysis, the first case was identified on 7/3/2020. In this paper, we shall fit a piecewise linear trend model to the log-scale of cumulative cases and deaths due to COVID-19 in Malta by implementing the SN-NOT changepoint model. This model combines the self-normalisation (SN) technique, which is used to test whether there is a single change-point in the linear trend of a time series, with the Narrowest Over Threshold algorithm (NOT) to achieve multiple change-point in the linear trend. Through analysis of news reports and other sources of information, estimated change-points are then compared to potential factors such as health restrictions, mass events, government policy and population behaviour that have affected these changes, in order to determine the efffect of these factors on the spread of the disease.
{"title":"Multiple Changepoint Analysis of COVID-19 Infection Progression and Related Deaths in the Small Island State of Malta","authors":"D. Suda, M. Inguanez, Gianluca Ursino","doi":"10.52933/jdssv.v2i7.50","DOIUrl":"https://doi.org/10.52933/jdssv.v2i7.50","url":null,"abstract":"In December 2019, in the city of Wuhan (China), Severe Acute Respiratory Syndrome Coronavirus - 2 (SARS-CoV−2), a virus that causes what is known as Coronavirus Disease 2019 (better known as COVID-19), emerged. In a few months the virus spread around the world becoming a global pandemic that has shaken the world. On Malta (a nation consisting of an archipelago of islands of approximately 500000 people), which is the case study of this analysis, the first case was identified on 7/3/2020. In this paper, we shall fit a piecewise linear trend model to the log-scale of cumulative cases and deaths due to COVID-19 in Malta by implementing the SN-NOT changepoint model. This model combines the self-normalisation (SN) technique, which is used to test whether there is a single change-point in the linear trend of a time series, with the Narrowest Over Threshold algorithm (NOT) to achieve multiple change-point in the linear trend. Through analysis of news reports and other sources of information, estimated change-points are then compared to potential factors such as health restrictions, mass events, government policy and population behaviour that have affected these changes, in order to determine the efffect of these factors on the spread of the disease.","PeriodicalId":93459,"journal":{"name":"Journal of data science, statistics, and visualisation","volume":"14 1","pages":"63-83"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84968521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Covid-19 has sparked a worldwide interest in understanding the dynamic evo- lution of a pandemic and tracking the effectiveness of preventive measures and rules. For this reason, numerous media and research groups have produced com- prehensive data visualisations to illustrate the relevant trends and figures. In this paper, we will look at a selection of Covid 19 data visualisations to evaluate and discuss the currently established visualisation tools in terms of their ability to provide a communication channel both within the data science team and between data analysts, domain experts and a general interested audience. Although there is no set catalogue of evaluation criteria for data visualisations, we will try to give an overview of the different core aspects of visualisation evaluation and their competing principles.
{"title":"Visual Narratives of the Covid-19 pandemic","authors":"A. Wilhelm, Susan Vanderplas","doi":"10.52933/jdssv.v2i7.64","DOIUrl":"https://doi.org/10.52933/jdssv.v2i7.64","url":null,"abstract":"\u0000 \u0000 \u0000 \u0000Covid-19 has sparked a worldwide interest in understanding the dynamic evo- lution of a pandemic and tracking the effectiveness of preventive measures and rules. For this reason, numerous media and research groups have produced com- prehensive data visualisations to illustrate the relevant trends and figures. In this paper, we will look at a selection of Covid 19 data visualisations to evaluate and discuss the currently established visualisation tools in terms of their ability to provide a communication channel both within the data science team and between data analysts, domain experts and a general interested audience. Although there is no set catalogue of evaluation criteria for data visualisations, we will try to give an overview of the different core aspects of visualisation evaluation and their competing principles. \u0000 \u0000 \u0000 \u0000","PeriodicalId":93459,"journal":{"name":"Journal of data science, statistics, and visualisation","volume":"4 1","pages":"84-113"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80828714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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