A New Interactive Tool to Visualize and Analyze COVID-19 Data: The PERISCOPE Atlas

Pala, Daniele; Parimbelli, Enea; Larizza, Cristiana; Cheng, Cindy; Ottaviano, Manuel; Pogliaghi, Andrea; Đukić, Goran; Jovanović, Aleksandar; Milićević, Ognjen; Urošević, Vladimir; Cerchiello, Paola; Giudici, Paolo; Bellazzi, Riccardo

doi:10.3390/ijerph19159136

Cited by 11 publications

(9 citation statements)

References 18 publications

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“…combining multiple datasets on COVID-19 PHSM, COVID-19 deaths, and GDP; e.g. the PERISCOPE Data Atlas [29,30]) [31]. To create a cohesive dataset, data harmonization can be understood as resolving differences along at least three dimensions [32][pg.…”

Section: Resultsmentioning

confidence: 99%

A General Guide for Harmonizing Data: Drawing Lessons from Harmonizing COVID-19 PHSM Data

Cheng

Messerschmidt

Bravo

et al. 2023

Preprint

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Data harmonization is an important method for generating the requisite datasets to support big data analyses. To date however, articles about data harmonization are field-specific and highly technical, making it difficult for researchers to derive general principles for how to engage in and contextualize data harmonization efforts. This article provides general guidance and criteria for researchers who are considering undertaking such efforts or seek to evaluate the quality of existing ones. We derive these guidelines from the extant literature and our own experience in harmonizing data for the emergent and important new field of COVID-19 public health and safety measures (PHSM). We further introduce the methodology we employed for engaging in this data harmonization as a blueprint for researchers interested in engaging in manual data harmonization.

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Section: Resultsmentioning

confidence: 99%

A General Guide for Harmonizing Data: Drawing Lessons from Harmonizing COVID-19 PHSM Data

Cheng

Messerschmidt

Bravo

et al. 2023

Preprint

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“…For example, the Pennsylvania West Nile program is still live but not being regularly updated. Another web map, the Periscope Atlas, was newly created for the COVID-19 epidemic in Europe [ 24 ]. Both authors [ 23 , 24 ] did not cite Pennsylvania Department of Health’s Enterprise Data Dissemination Informatics Exchange, but we reviewed it for this assessment.…”

Section: Resultsmentioning

confidence: 99%

LionVu: A Data-Driven Geographical Web-GIS Tool for Community Health and Decision-Making in a Catchment Area

Geyer

Lengerich

2023

Geographies

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In 2018, the Penn State Cancer Institute developed LionVu, a web mapping tool to educate and inform community health professionals about the cancer burden in Pennsylvania and its catchment area of 28 counties in central Pennsylvania. LionVu, redesigned in 2023, uses several open-source JavaScript libraries (i.e., Leaflet, jQuery, Chroma, Geostats, DataTables, and ApexChart) to allow public health researchers the ability to map, download, and chart 21 publicly available datasets for clinical, educational, and epidemiological audiences. County and census tract data used in choropleth maps were all downloaded from the sources website and linked to Pennsylvania and catchment area county and census tract geographies, using a QGIS plugin and Leaflet JavaScript. Two LionVu demonstrations are presented, and 10 other public health related web-GIS applications are reviewed. LionVu fills a role in the public health community by allowing clinical, educational, and epidemiological audiences the ability to visualize and utilize health data at various levels of aggregation and geographical scales (i.e., county, or census tracts). Also, LionVu is a novel application that can translate and can be used, for mapping and graphing purposes. A dialog to demonstrate the potential value of web-based GIS to a wider audience, in the public health research community, is needed.

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“…PERISCOPE researchers thus carry out theoretical and experimental research to contribute to a deeper understanding of the short and long-term impacts of the pandemic and the measures adopted to contain it. A core component of this project is the ongoing effort to build a comprehensive COVID-19 Atlas [2] to inform users about the dynamics, effects and consequences of the COVID-19 outbreak. The ATLAS has a dual focus: visual analytics to illustrate the results of predictive models and data analysis, and a WebGIS for geographical exploration of data.…”

Section: The Periscope Projectmentioning

confidence: 99%

Effect of European COVID-containment policies on epidemiological monitoring indicators

Podrecca

Parimbelli

Pala

et al. 2022

Preprint

Self Cite

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Since its initial appearance on the western world in early 2020, the COVID-19 pandemic has proven to be a catastrophic event that has seriously endangered the world’s population. While governments have tried their best to respond appropriately to the changing rhythms of the pandemic, they have largely been unprepared to deal with a calamity of such proportion. This is in no small part due to the lack of sufficient or adequately fine-grained data necessary for forecasting the pandemic’s evolution. To remedy this gap, researchers from around the world have been collecting data about different aspects of COVID-19’s evolution and government responses to them so as to provide the foundation for informative models and tools that can be used to mitigate the current pandemic and possibly prevent the spread of future ones. Indeed, a number of research initiatives were launched in the early stages of the pandemic with this goal, including the PERISCOPE (Pan-European Response to the ImpactS of COVID-19 and future Pandemics and Epidemics) Project, funded by the European Commission. PERISCOPE aims to investigate the broad socio-economic and behavioral impacts of the COVID-19 pandemic, with the goal of making Europe more resilient and prepared for future large-scale risks. The purpose of this paper, which is part of work carried on within the PERISCOPE project, is to provide a first analysis at the European level of the effect of policy actions on the spread of the virus. To do so, the relationship between a novel index, the Policy Intensity Index, and four epidemiological variables collected by the European Centre for Disease Control and Prevention will be assessed. After a first exploratory correlational and causal analysis, a comprehensive Pan-European population model is proposed: based on Multilevel Vector Autoregression, the model aims at identifying effects that are common to all European countries while treating country-specific policies as covariates explaining the different evolution of the pandemic in 9 selected countries, tracked through four epidemiological monitoring indicators.

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A New Interactive Tool to Visualize and Analyze COVID-19 Data: The PERISCOPE Atlas

Cited by 11 publications

References 18 publications

A General Guide for Harmonizing Data: Drawing Lessons from Harmonizing COVID-19 PHSM Data

A General Guide for Harmonizing Data: Drawing Lessons from Harmonizing COVID-19 PHSM Data

LionVu: A Data-Driven Geographical Web-GIS Tool for Community Health and Decision-Making in a Catchment Area

Effect of European COVID-containment policies on epidemiological monitoring indicators

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