COVID-19 visualization and exploratory data analysis
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Ria Rawal
Abstract
The outbreak of the novel coronavirus was detected in Wuhan, China, and it has already spread to more than 200 countries now. The novel coronavirus has been named SARS-CoV-2 by the World Health Organization. More than 25 countries had reported their first case of this novel coronavirus and the total number of cases reported worldwide was around 10,000 by the end of January 2020. The WHO declared the outbreak a Public Health Emergency of International Concern on 30 January 2020, and a pandemic on 11 March 2020. There are abundant data available on the Internet regarding coronavirus, including data from official sources such as Government Handles as well as the international organizations such as the WHO and UNICEF. Due to these large amounts of raw data, it is somewhat laborious to dig out the actual facts and grasp the situation of the pandemic. In the wake of this problem, visualization of the raw data helps in providing a clear picture of the pandemic to general masses. This chapter visualizes certain parameters and performs the analysis based on different criteria such as hospitalization and fatality rate which would then be compared across 14 states of the USA. The purpose of this visualization is to help in interpreting patterns so that quick and appropriate actions can be taken in advance which in turn will control the spread of the virus to a greater extent.
Abstract
The outbreak of the novel coronavirus was detected in Wuhan, China, and it has already spread to more than 200 countries now. The novel coronavirus has been named SARS-CoV-2 by the World Health Organization. More than 25 countries had reported their first case of this novel coronavirus and the total number of cases reported worldwide was around 10,000 by the end of January 2020. The WHO declared the outbreak a Public Health Emergency of International Concern on 30 January 2020, and a pandemic on 11 March 2020. There are abundant data available on the Internet regarding coronavirus, including data from official sources such as Government Handles as well as the international organizations such as the WHO and UNICEF. Due to these large amounts of raw data, it is somewhat laborious to dig out the actual facts and grasp the situation of the pandemic. In the wake of this problem, visualization of the raw data helps in providing a clear picture of the pandemic to general masses. This chapter visualizes certain parameters and performs the analysis based on different criteria such as hospitalization and fatality rate which would then be compared across 14 states of the USA. The purpose of this visualization is to help in interpreting patterns so that quick and appropriate actions can be taken in advance which in turn will control the spread of the virus to a greater extent.
Chapters in this book
- Frontmatter I
- Contents V
- Knowledge engineering for industrial expert systems 1
- Machine learning integrated blockchain model for Industry 4.0 smart applications 13
- Prototyping the expectancy disconfirmation theory model for quality service delivery in federal university libraries in Nigeria 26
- Design of chatbot using natural language processing 60
- Algorithm development based on an integrated approach for identifying cause and effect relationships between different factors 80
- Risk analysis and management in projects 96
- Assessing and managing risks in smart computing applications 122
- COVID-19 visualization and exploratory data analysis 145
- Business intelligence and decision support systems: business applications in the modern information system era 156
- Business intelligence implementation in different organizational setup evidence from reviewed literatures 173
- Conceptualization of a modern digital-driven health-care management information system (HMIS) 187
- Knowledge engine for a Hindi text-to-scene generation system 201
- Index 229
Chapters in this book
- Frontmatter I
- Contents V
- Knowledge engineering for industrial expert systems 1
- Machine learning integrated blockchain model for Industry 4.0 smart applications 13
- Prototyping the expectancy disconfirmation theory model for quality service delivery in federal university libraries in Nigeria 26
- Design of chatbot using natural language processing 60
- Algorithm development based on an integrated approach for identifying cause and effect relationships between different factors 80
- Risk analysis and management in projects 96
- Assessing and managing risks in smart computing applications 122
- COVID-19 visualization and exploratory data analysis 145
- Business intelligence and decision support systems: business applications in the modern information system era 156
- Business intelligence implementation in different organizational setup evidence from reviewed literatures 173
- Conceptualization of a modern digital-driven health-care management information system (HMIS) 187
- Knowledge engine for a Hindi text-to-scene generation system 201
- Index 229
