Data visualization using Plotly and comparative analysis has an impact on COVID-19.
The image is from the internet. Data insights on the outbreak of coronaviruses compared to the outbreak of SIRS.
The World Health Organisation and several countries have published latest results on the impact of the novel corona-viruses over the past few months. The introduction is about something.
I am using Plotly for visualization. Plotly is a visualization tool that supports interactive graphs and is a great tool for data science beginners. I was excited to find this data source that I was going to use to see a visualization of the fatality trend, I have been looking at many sources to understand it. The aim is to understand how visualization helps to derive informative insights from data sources.
The data-set sources are accumulated, processed and the latest updates are available on the JHUCSSE’s page. Data-set
The data set has details. The terms of use are stated in the link. Data will only be used for research.
This csv file contains information on the affected countries, which helps to identify the spread of the virus, as well as the number of deaths and recoveries. The country co-ordinates are provided for analysis. Data is reported daily.
There is a time series data which contains the counts on infections, deaths and recoveries. The time series data has individual files for each case and needs to be processed before visualization. The country co-ordinates are used for time series visualization on plots. There are 2. The data is time-series.
There are important notes. The data set used for the following charts can be found in the link below.
A code template can be used for other data sources. I would encourage readers to try other charts in Plotly and to modify the codes according to application requirements. The data was used for the analysis on 3rd March. The data or insights derived from the analysis for medical guidance should not be used in commerce. It’s only for learning purpose.
There is an analysis. One important aspect of showing your findings is to use a set of charts that show key insight from the data rather than showing too much information.
There are 2. The analysis looked at mortality, infections, and recovery rates. COVID-19 has a global impact.
There are 4. A comparative analysis of the impact of COVID-19. There are 3. There was an analysis of the spread of COVID-19 between January and March.
The geographical scatter plot from Plotly was used to understand the impact of the virus. The code for this interactive plot is located in the shared link, which will give a more clear interactive visualization. COVID-19 has a global impact.
Observation. The global impact of COVID-19.
The analysis looked at mortality, infections, and recovery rates. Even though the disease has spread to a larger number of people in China, the number of deaths is low and there has been a lot of recovered patients.
It would be good to exclude China since it has a higher rate of infections than other countries. The following charts will be applied. I have used various charts to show how information can be mined.
There are cases that are infectious across countries. There are confirmed cases using the pie chart.
The highest number of affected cases is found in mainland china. China, South Korea, Italy and Iran have a high number of patients with infections. There are cases that are not from mainland China.
There are deaths reported across countries. There are 2. The number of deaths is reported using a bar chart.
Even though South Korea has the highest number of confirmed cases compared to Italy and Iran, their mortality rate is far below in comparison. An analysis of recovery is shown below. The number of deaths reported in mainland China is not included.
The recovery rate across countries gives a better idea of how countries are handling the outbreak. The data set in order, “world”, “country”, followed by “province/Region”, shows the different levels of hierarchy. There are 3. The recovery rates are analyzed using tree maps.
Recovery rates are not included in the tree-map. There is a tree-map of recovery rates.
There was an analysis of the spread of COVID-19 between January and March. Iran and Italy have higher number of recoveries than South Korea. More clarity on these numbers could be given from a deep dive into the impact of other attributes.
I only show the trend for infections. A similar trend can be seen for deaths and recoveries in other countries. The consolidated view is shown using a line chart. It’s important to show how quickly the virus has spread in different countries. Plotly is provided in the notebook and it would require some pre-processing on the original data to be visualized.
The analysis is done across countries. Scatter plot analysis across countries.
Observation. There is a time analysis across countries.
The multiple line chart was analyzed. A rapid increase in the number of cases within a few days can be seen in some countries, while a steady growth in the number of cases can be observed in China.
An analysis of the impact of COVID-19 vs. The world has affected cases, deaths and recoveries.
An analysis of the time period for infections. It is interesting to compare the impact of COVID-19 to Severe Acute Respiratory Syndrome. kaggle data source obtained the dataset for SARS. Overall infections and mortality cases were only contained in the data. I looked at the impact of the two viruses in a window frame and predicted their impact over the next three months.
There is a time analysis for reported deaths. There have been reported cases of COVID-19 vs the disease.
Observation. There are reported deaths across the world.
Conclusion COVID-19 has a higher mortality rate than the other way around. This case could be affected by the advancement in transport.
Links to codes and data sets. This article gives a detailed analysis on how COVID-19 has impacted the world and how the derived insights can be used for downstream analysis. Key data insights can be deduced from the charts.
To learn more about patient gender, ethnicity, and age and how it causes the fatality rate, a dashboard of interactive charts is provided. Future work will be done.
There are references.