Online dashboard and data analysis approach for assessing COVID-19 case and death data

The 2019-2020 global pandemic has been caused by a disease called coronavirus disease 2019 (COVID-19). This disease has been caused by the Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2). By April 30 2020, the World Health Organization reported 3,096,626 cases and 217,896 deaths, which implies an exponential growth for infection and deaths worldwide. Currently, there are various computer-based approaches that present COVID-19 data through different types of charts, which is very useful to recognise its behavior and trends. Nevertheless, such approaches do not allow for observation of any projection regarding confirmed cases and deaths, which would be useful to understand the trends of COVID-19. In this work, we have designed and developed an online dashboard that presents actual information about COVID-19. Furthermore, based on this information, we have designed a mathematical model in order to make projections about the evolution of cases and deaths worldwide and by country.


Introduction
Coronavirus disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), which is a virus strain that causes respiratory illness. This virus was identified in December, 2019 and its first infection case was reported on December 30, 2019 in Wuhan city located in Hubei providence, China 1 . The World Health Organization (WHO) recognized the disease as a pandemic on March 11 2020 2 . The WHO have reported the following numbers of cases and deaths worldwide: January 31, 9,847 cases and 213 deaths 3  Based on these percentiles, we can observe an exponential growth of the disease.
Based on this previous data, it is important to analyze the evolution of the disease in order to make decisions that tackle the growth rate of cases and deaths. Therefore, in this work, we have designed and developed an online dashboard which presents two types of results: (1) actual information about the COVID-19 using different deployment strategies; and (2) based on the actual information, a projection in order to estimate the future behavior of the disease calculated worldwide and by country. To achieve this projection, we have designed a mathematical model based on previous data reported by the WHO.

COVID-19
Dashboard presents full information about COVID-19 focusing on cases and deaths worldwide and by country. In addition, it includes a mathematical model in order to calculate projections for future cases and deaths. The data source used in COVID-19 Dashboard is taken from the WHO (https:// covid19.who.int/) and World Population Review (https://www. worldpopulationreview.com/). The first data set is accessed by COVID-19 Dashboard through the URL https://dashboardsdev.sprinklr.com/data/9043/global-covid19-who-gis.json. This information is in JSON format; then, the system includes the algorithms to decode this format before processing the information.The second data set is downloaded in CSV format and included in the persistence layer of the system.This project deploys different graphics to analyze the information from different perspectives 11 . These graphics are generated based on the JavaScript library Chartkick 2.7.2 (https://www.chartkick. com/), which is supported by Google Charts (https://developers. google.com/chart).
To perform the projection for estimating future cases and deaths, we developed a mathematical model focused on a quadratic equation 12 based on data of present day and 30 previous days. With the quadratic equation, projected cases and deaths for the next days up to 90 days are calculated. The quadratic equation obtained for each projection is deployed supported by the JavaScript library Mathjax (https://www.mathjax.org/).

Modeling projections for cases and deaths
For the mathematical model, let three points P 1 = (x 1 , y 1 ), P 2 = (x 2 , y 2 ), and P 3 = (x 3 , y 3 ) such that y 1 corresponds to number of cases or deaths of the date 30 days prior to the present day, x 1 = 0, y 2 corresponds to number of cases or deaths of the date 15 days prior to the present day, x 2 = 15, y 3 corresponds to number of cases or deaths of the present day, and x 1 = 30. Based Since x 1 = 0 from (1): Subtracting (2) from (4): Isolating the variable b from (5): Subtracting (2) from (3): Replacing b in (7): Isolating the variable a from (8): Finally, isolating the variable c from (4): Therefore, based on a, b, and c applied to the quadratic equation, the projected cases and deaths are calculated. In addition, calculated quadratic equations will be adjusted when new information is reported by the WHO. Furthermore, COVID-19 Dashboard offers a projection for 90 days; nevertheless, for some countries, the quadratic equation could rise to a maximum point; in these cases, the algorithms will stop calculating and thus the projected days will be lower than 90 days.
Consequently, COVID-19 Dashboard allows analyzing confirmed cases, deaths, and mortality rates as well as projecting cases and death deploying information in: a) geographic charts to understand the country distribution of the disease, b) bar charts to recognize the most impacted countries, c) column charts to present the evolution of the disease over time, and d) line charts to estimate the behavior of the disease.
Operation COVID-19 Dashboard is an online responsive system; therefore, it is accessible for users via a web browser through desktops, laptops, smart phones, and tablets. Its front-end can automatically adjust to any screen resolution in order to deploy the information in the best possible way.

COVID-19
Dashboard is able to process the data provided by the WHO related to COVID-19 rates and World Population Review related to the current world population. This system deploys up-to-date information as follows: • Cases and deaths by country. This information is presented in two ways: geographic charts and bar charts in order to recognize the most impacted countries.
• Cases and deaths by country per million people. This information is also presented by geographic and bar charts.
• Mortality rate by country. For this information, the mortality rate is calculated for each country and is presented by geographic and bar charts.
• Cases and deaths by date worldwide and by selected country. This information is calculated based on the daily information reported by each country. This is presented in two column charts. The first column chart presents the daily cases, while the second column chart presents the cumulative cases.
• Projected cases and deaths worldwide and by selected country. This information is calculated using the quadratic equation obtained through the developed mathematical model (as above). It is presented using a line chart with two series. The first presents the actual cases or deaths for the last 31 days. The second presents the projected cases or deaths up to 90 days after the last day reported.
As mentioned above, the number of cases, deaths, and mortality rates are deployed using geographic charts. Figure 2  Consequently, it is necessary to sort the countries based on the actual number of deaths in addition to the color coding. Figure 3 presents a fragment of a bar chart with the number of deaths per country sorted by number of deaths. This chart shows that the United States has more than the double of deaths of Italy and the United Kingdom. In addition, Spain and  France have almost the same number, but a little less than Italy and United Kingdom, followed by Brazil, while Belgium, Mexico, Germany, Iran, and Canada have similar number of deaths but much less than Spain and France. Finally, Netherlands, India, China, Turkey, Russia, and Sweden have less but a still important number of cases.
It is also important to assess information about COVID-19 based on the population by country. Figure 4 presents a geographic chart with the number of cases by countries per million people. Although United States has the largest number of cases, this chart presents that countries such as Spain, Ireland, and Belgium have more cases per million than United States, while Italy, Switzerland, United Kingdom, Portugal, and Sweden have also a large number of cases per million.
Additionally, the COVID-19 Dashboard shows the mortality rate per country, as shown in Figure 5. Although the United States has the largest number of deaths, it also has a very large   Nevertheless, it is important to take into account that this country has reported little information. Countries such as France, Belgium, Italy, United Kingdom, and Spain are in the top 10 of this list, which implies that they have a high mortality risk, perhaps due to the high number of active cases.
Further information to be analyzed is cases and deaths per date. Figure 6 presents two charts. Figure 6(a) shows the number of confirmed cases per day in Colombia, while (b) shows cumulative cases per day in Colombia. Figure 6(a) shows that from April 1 2020, the number of cases was stable with peaks in the last few days, while Figure 6(b) shows stable behavior with a small increment of cumulative cases from April. It implies that COVID-19 keeps impacting health in Colombia and cases will increase until its behavior changes.
Based these previous results, it is really important to know the trend of cases and deaths worldwide and by country. Thus, COVID-19 Dashboard includes a very important contribution, i.e. the mathematical model already described, which allows projection for future cases and deaths worldwide and by country. Figure 7 presents four charts with cases and death projections. This projection presents a exponential growth, which implies that COVID-19 in Colombia will continue to impact public health.
• Figure 7(b) presents projected cases in Russia. This projection performed on May 29 th 2020 produced the quadratic equation:  (12) This projection presents a logarithmic growth, which implies that deaths for COVID-19 in Russia are decreasing considerably and might stop in 61 days.
• Figure 7(c) presents projected deaths in India. This projection performed on May 29 th 2020 produced the quadratic equation: ( ) 2 1.37 82.30 1,007.00 This projection presents a exponential growth, which implies that deaths for COVID-19 in India are still increasing.
• This projection presents a logarithmic growth, which implies that deaths for COVID-19 worldwide might start decreasing in the first week of July.
Quadratic equations are presented with coefficients with two decimals; however, all calculations are done with the real calculated values for their coefficients.

COVID-19
Dashboard allows understanding COVID-19 behavior and evolution from different perspectives. Based on those perspectives, it is possible to assess not only the actual number of cases and deaths worldwide and by country, but also the most impacted countries by observing the number of cases and deaths per million people and mortality rate. COVID-19 Dashboard offers a projection of future cases and deaths, which is an important tool to estimate the disease's evolution. Based on these projections, health organizations around the world might take action in order to minimize the impact, as well as estimate the date when the COVID-19 might stop impacting various countries.

Data availability
Underlying data Data used for this project has been taken from the following public sources available in JSON and Comma-separated values (csv) format respectively: • COVID-19 map data by the WHO https://covid19. who.int/.

Olmer Garcia-Bedoya
Jorge Tadeo Lozano University, Bogota, Colombia The authors present a Dashboard's development to analyze the COVID-19 contagious and death base in open data available. The paper is well-written and presents interesting results clearly and concisely. I have enjoyed reading this paper.
I have the following comments for the authors that should be addressed before indexing: The model presented to predict the time series's tendencies is easy to understand and has a fast implementation. However, I recommend reviewing time series analysis like ARIMA, recurrent neural network, or systems dynamics, which let to include more data in the estimation of the tendencies of the time series. ○ It will be interesting to review initiatives like presented in Kaggle wherein collaborative works, have presented alternatives since the data science field could help to combat the COVID-19 disease.
○ Is the rationale for developing the new software tool clearly explained? Yes

Is the description of the software tool technically sound? Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Partly

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Partly