Demographic and Comorbidities Data Description of Population in Mexico with SARS-CoV-2 Infected Patients(COVID19): An Online Tool Analysis

Galván-Tejada, Carlos E.; Zanella-Calzada, Laura A.; Villagrana-Bañuelos, Karen E.; Moreno-B́aez, Arturo; Luna-García, Huizilopoztli; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Gamboa-Rosales, Hamurabi

doi:10.3390/ijerph17145173

Cited by 4 publications

(13 citation statements)

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“…However, their tool does not display estimates or other variables (environmental, vaccine status, etc.). The app created by Galván-Tejada et al offers greater interactivity and completeness than the former but falls short of reporting updated vaccination data as well as other variables, even though they provide valuable demographic information on top of a very thorough analysis of the pandemic in Mexico [7]. Another useful online web application for updated country-specific analysis and visualization is "COVID19-World" by Tebé et al used for basic epidemiological surveillance covering time trends and projections, population fatality rate, case fatality rate, and basic reproduction number [8].…”

Section: Introductionmentioning

confidence: 99%

PandemonCAT: Monitoring the COVID-19 Pandemic in Catalonia, Spain

Chaudhuri

Giménez-Adsuar

Sáez

et al. 2022

IJERPH

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Background: The principal objective of this paper is to introduce an online interactive application that helps in real-time monitoring of the COVID-19 pandemic in Catalonia, Spain (PandemonCAT). Methods: This application is designed as a collection of user-friendly dashboards using open-source R software supported by the Shiny package. Results: PandemonCAT reports accumulated weekly updates of COVID-19 dynamics in a geospatial interactive platform for individual basic health areas (ABSs) of Catalonia. It also shows on a georeferenced map the evolution of vaccination campaigns representing the share of population with either one or two shots of the vaccine, for populations of different age groups. In addition, the application reports information about environmental and socioeconomic variables and also provides an interactive interface to visualize monthly public mobility before, during, and after the lockdown phases. Finally, we report the smoothed standardized COVID-19 infected cases and mortality rates on maps of basic health areas ABSs and regions of Catalonia. These smoothed rates allow the user to explore geographic patterns in incidence and mortality rates. The visualization of the variables that could have some influence on the spatiotemporal dynamics of the pandemic is demonstrated. Conclusions: We believe the addition of these new dimensions, which is the key innovation of our project, will improve the current understanding of the spread and the impact of COVID-19 in the community. This application can be used as an open tool for consultation by the public of Catalonia and Spain in general. It could also have implications in facilitating the visualization of public health data, allowing timely interpretation due to the unpredictable nature of the pandemic.

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Section: Introductionmentioning

confidence: 99%

PandemonCAT: Monitoring the COVID-19 Pandemic in Catalonia, Spain

Chaudhuri

Giménez-Adsuar

Sáez

et al. 2022

IJERPH

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“…Galván‐Tejada et al proposed online visualization tools that allow an analysis of demographic data of the Mexican population. The purpose of these tools is to provide fast and up‐to‐date graphical information based on a data set directly from the National Government's Health Secretary, which is updated daily with information on SARS‐CoV‐2 30 . In the relevant literature, several predictive algorithms 34‐36 and statistical models 37,38 were applied to analyze Covid‐19 related data to estimate effect of various parameters related to Covid‐19 infections.…”

Section: Introductionmentioning

confidence: 99%

“…Data visualization is a powerful tool for exploring findings in data analysis 28‐30 . It basically provides a potential to unveil the important points and remarkable patterns of the dataset.…”

Section: Introductionmentioning

confidence: 99%

“…Data visualization is a powerful tool for exploring findings in data analysis. [28][29][30] It basically provides a potential to unveil the important points and remarkable patterns of the dataset. Moreover, it may assist researchers and policymakers to discuss the statistical results on their analysis.…”

mentioning

confidence: 99%

“…The purpose of these tools is to provide fast and up-to-date graphical information based on a data set directly from the National Government's Health Secretary, which is updated daily with information on SARS-CoV-2. 30 In the relevant literature, several predictive algorithms [34][35][36] and statistical models 37,38 were applied to analyze Covid-19 related data to estimate effect of various parameters related to Covid-19 infections. These studies considered predictive factors such as disease spread rate, infection risk, climate effects using deep learning-based algorithms.…”

mentioning

confidence: 99%

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The effect of population and tourism factors on Covid‐19 cases in Italy: Visual data analysis and forecasting approach

Uğuz

Yağanoğlu

Ozyer

et al. 2021

Concurrency and Computation

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At the beginning of 2020, the new coronavirus disease (Covid-19), a deadly viral illness, is declared as a public health emergency situation by WHO. Consequently, it is accepted as pandemic that affected millions of people worldwide. Italy is one of the most affected countries by Covid-19 disease among the world. In this article, our main goal is to investigate the effect of intensity of Covid-19 cases based on the population size and tourism factors in certain regions of Italy by visual data analysis. The regions of Lombardia, Veneto, Campania, Emilia-Romagna, Piemonte are the top five regions covering 58.50% of the total Covid-19 cases diagnosed in Italy. It has been shown by visual data analysis that population and tourism factors play an important role in the spread of Covid-19 cases in these five regions. In addition, a prediction model was created using Bi-LSTM and ARIMA algorithms to forecast the number of Covid-19 cases occurring in these five regions in order to take early action. We can conclude that these northern regions have been affected mostly by Covid-19 and the distribution of the resident population and tourist flow factors affected the number of Covid-19 cases in Italy. K E Y W O R D S coronavirus, Covid-19, forecasting method, visual data analysis 1 INTRODUCTION A new coronavirus disease that can cause fatal effects on humans, much like the Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS), emerged in late 2019. The symptoms of the disease first became evident in China in December 2019, and the disease was diagnosed worldwide as Covid-19 at the beginning of the January 2020. 1 According to the Covid-19 timeline drawn up by the World Health Organization (WHO) Covid-19, patients in China who were eventually diagnosed as having Covid-19 on December 31, 2019 showed symptoms similar tothose of pneumonia. The disease was then reported to be a new coronavirus disease. The WHO established its Incident Management Support Team to address the spread and impacts of the new coronavirus disease on January 1, 2020. On January 13, 2020, it was reported that the first Covid-19 case outside of China occurred in Thailand on January 8, 2020. In addition, as per official statements, the spread of the coronavirus disease was attributed to one patient who traveled to Wuhan, China and returned to Thailand. 2 The WHO's status report on January 30, 2020, revealed a total of 7.818 cases of Covid-19 worldwide, 82 of which were located in 18 countries outside China. 3 According to the WHO's case report of January 31, 2020, Covid-19 had spread to an additional country, taking the total to 19 countries outside China. 4 The country that reported these new Covid-19 cases was Italy. With the addition of Italy, the number of European countries that had reported Covid-19 cases rose to four. Reports of Covid-19 in Italy started with two people, who mentioned the city of Wuhan as being part of their travel histories. Marta et al. explained that these two cases, which were recorded in Rome, includ...

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Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review

Kim,

Cánovas-Segura,

Campos

et al. 2024

Technologies

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In recent years, the proliferation of health data sources due to computer technologies has prompted the use of visualization techniques to tackle epidemiological challenges. However, existing reviews lack a specific focus on the spatial and temporal analysis of epidemiological data using visualization tools. This study aims to address this gap by conducting a scoping review following the PRISMA-ScR guidelines, examining the literature from 2000 to 2024 on spatial–temporal visualization techniques when applied to epidemics, across five databases: PubMed, IEEE Xplore, Scopus, Google Scholar, and ACM Digital Library until 24 January 2024. Among 1312 papers reviewed, 114 were selected, emphasizing aggregate measures, web platform tools, and geospatial data representation, particularly favoring choropleth maps and extended charts. Visualization techniques were predominantly utilized for real-time data presentation, trend analysis, and predictions. Evaluation methods, categorized into standard methodology, user experience, task efficiency, and accuracy, were observed. Although various open-access datasets were available, only a few were commonly used, mainly those related to COVID-19. This study sheds light on the current trends in visualizing epidemiological data over the past 24 years, highlighting the gaps in standardized evaluation methodologies and the limited exploration of individual epidemiological data and diseases acquired in hospitals during epidemics.

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Demographic and Comorbidities Data Description of Population in Mexico with SARS-CoV-2 Infected Patients(COVID19): An Online Tool Analysis

Cited by 4 publications

References 10 publications

PandemonCAT: Monitoring the COVID-19 Pandemic in Catalonia, Spain

PandemonCAT: Monitoring the COVID-19 Pandemic in Catalonia, Spain

The effect of population and tourism factors on Covid‐19 cases in Italy: Visual data analysis and forecasting approach

Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review

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