Journal of Data Science, Statistics, and Visualisation 2022-11-28T09:48:59+00:00 Patrick J.F. Groenen Open Journal Systems <p>The journal welcomes contributions to practical aspects of data science, statistics and visualisation, and in particular those which are linking and integrating these subject areas. Papers should thus be oriented towards a very wide scientific audience, and can cover topics such as machine learning and statistical learning, the visualisation and verbalization of data, big data infrastructures and analytics, interactive learning, advanced computing, and other important themes. JDSSV is an open access journal that charges no author fees.</p> Implementation of an Adaptable COVID-19 Utilization and Resource Visualization Engine (CURVE) to Depict In-Hospital Resource Forecasts Over Time 2021-07-23T08:01:31+00:00 Shih-Hsiung Chou Philip Turk Marc Kowalkowski James Kearns Jason Roberge Jennifer Priem Yhenneko Taylor Ryan Burns Pooja Palmer Andrew McWilliams <p>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.</p> 2022-11-28T00:00:00+00:00 Copyright (c) 2022 Journal of Data Science, Statistics, and Visualisation A Spatial SEIR Model for COVID-19 in South Africa 2022-01-06T09:03:25+00:00 Inger Fabris-Rotelli Jenny Holloway Zaid Kimmie Sally Archibald Pravesh Debba Raeesa Manjoo-Docrat Alize le Roux Nontembeko Dudeni-Tlhone Charl Janse van Rensburg Renate Thiede Nada Abdelatif Sibusisiwe Makhanya Arminn Potgieter <p>The virus SARS-CoV-2 has resulted in numerous modelling approaches arising rapidly to understand the spread of the disease COVID-19 and to plan for future interventions. Herein, we present an SEIR model with a spatial spread component as well as four infectious compartments to account for the variety of symptom levels and transmission rate. The model takes into account the pattern of spatial vulnerability in South Africa through a vulnerability index that is based on socioeconomic and health susceptibility characteristics. Another spatially relevant factor in this context is level of mobility throughout. The thesis of this study is that without the contextual spatial spread modelling, the heterogeneity in COVID-19 prevalence in the South African setting would not be captured. The model is illustrated on South African COVID-19 case counts and hospitalisations.</p> 2022-11-28T00:00:00+00:00 Copyright (c) 2022 Journal of Data Science, Statistics, and Visualisation Using hospital data for monitoring the dynamics of COVID-19 in France 2021-11-29T18:32:24+00:00 Marc Lavielle <pre>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. <br>A piecewise defined dynamic model allows to fit very well the available hospital admission, death and discharge data. <br>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. <br>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.<br>The model, data and fits are implemented in an interactive web application.</pre> 2022-11-28T00:00:00+00:00 Copyright (c) 2022 Journal of Data Science, Statistics, and Visualisation Multiple Changepoint Analysis of COVID-19 Infection Progression and Related Deaths in the Small Island State of Malta 2022-02-28T19:05:56+00:00 David Suda Monique Borg Inguanez Gianluca Ursino <p>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.</p> 2022-11-28T00:00:00+00:00 Copyright (c) 2022 Journal of Data Science, Statistics, and Visualisation Visual Narratives of the Covid-19 pandemic 2022-06-02T18:49:09+00:00 Adalbert Wilhelm Susan VanderPlas <div class="page" title="Page 1"> <div class="section"> <div class="layoutArea"> <div class="column"> <p>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.</p> </div> </div> </div> </div> 2022-11-28T00:00:00+00:00 Copyright (c) 2022 Journal of Data Science, Statistics, and Visualisation