Using dashboards to verify coronavirus (COVID-19) vaccinations can reduce fatality rates in countries/regions: Development and usability study

Abstract

Background: 

The new coronavirus disease 2019 (COVID-19) pandemic is raging worldwide. The administered vaccination has become a significant vehicle against the virus. Three hypotheses were made and required for validation: the number of vaccines administered is related to the country gross domestic product (GDP), vaccines can reduce the fatality rate (FR), and dashboards can present more meaningful information than traditionally static visualizations. Research data were downloaded from the GitHub website. The aims of this study are to verify that the number of vaccination uptakes is related to the country GDP, that vaccines can reduce FR, and that dashboards can provide more meaningful information than traditionally static visualizations.

Methods: 

The COVID-19 cumulative number of confirmed cases (CNCCs) and deaths were downloaded from the GitHub website for countries/regions on November 6, 2021. Four variables between January 1, 2021, and November 6, 2021, were collected, including CNCCs and deaths, GDP per capita, and vaccine doses administered per 100 people (VD100) in countries/regions. We applied the Kano model, forest plot, and choropleth map to demonstrate and verify the 3 hypotheses using correlation coefficients (CC) between vaccination and FRs. Dashboards used to display the vaccination effects were on Google Maps.

Results: 

We observed that the higher the GDP, the more vaccines are administered (Association = 0.68, t = 13.14, P < .001) in countries, the FR can be reduced by administering vaccinations that are proven except for the 4 groups of Asia, Low income, Lower middle income, and South America, as well as the application (app) with dashboard-type choropleth map can be used to show the comparison of vaccination rates for countries/regions using line charts.

Conclusion: 

This research uses the Kano map, forest plot, and choropleth map to verify the 3 hypotheses and provides insights into the vaccination effect against the FR for relevant epidemic studies in the future.

Key Points

• • In this study, the evidence of vaccination administered in countries for COVID-19 that may be associated with GDPs in countries was examined.
• • A Kano map, forest plot, and choropleth map were used to verify the 3 hypotheses.
• • These visualizations addressed public health issues that would impact and contribute to academics in the future.

1. Background

In December 2019, a novel coronavirus disease 2019 (COVID-19) emerged in Wuhan, Hubei, China.^[1–4] As of November 6, 2021, COVID-19 has spread to 187 countries, with a total of 0.25 billion confirmed cases and 5.1 million deaths.^[5,6] In contrast to 2 previously prominent coronavirus endemics in the past 2 decades, severe acute respiratory syndrome coronavirus (SARS-CoV) caused 774 deaths from November 2002 to July 2003,^[7] and Middle East respiratory syndrome coronavirus (MERS-CoV) resulted in 888 deaths until August 2021.^[8]

The rapid transmission of COVID-19 has attracted worldwide attention. Nonetheless, there was initially no appropriate strategy to contain the outbreak of COVID-19.^[2] To date, over 338,971 articles have been published in PubMed^[9] with regard with COVID-19 pandemic that has caused disruptions to societies and their health systems, particularly in low- and middle-income countries.^[10] The top 10 research priorities included health care system access barriers to equitable uptake of COVID-19 vaccination, determinants of vaccine hesitancy, development and evaluation of effective interventions to decrease vaccine hesitancy, and vaccination impacts on vulnerable population/s.^[10] In addition, the safety and efficacy of the COVID-19 vaccine have become issues of much concern due to the urgent need after the development of numerous vaccine research projects.^[11–13]

1.1. The vaccination and fatality rate (FR)

The development of the COVID-19 vaccine and mass vaccination administrations worldwide is anticipated to be the most effective way to fight against the COVID-19 pandemic.^[14–16] To date, 128 candidate COVID-19 vaccines are in clinical development, and 194 are in preclinical development according to a series of vaccine platforms.^[17] In December 2020, the Pfizer vaccine was the first COVID-19 vaccine to receive Emergency Use Authorization from the U.S. Food and Drug Administration. Additionally, after a week, the second Food and Drug Administration-approved COVID-19 vaccine was the Moderna messenger ribonucleic acid-1273 vaccine.^[18] Both Pfizer and Moderna vaccines are messenger ribonucleic acid vaccines and are more than 94% effective against COVID-19.^[10,11]

1.2. The 1st research question

Furthermore, after exceptional efforts, many COVID-19 vaccines have been developed and approved worldwide.^[17,19] Nonetheless, we still encounter many challenges when attempting to successfully fight against COVID-19; for example, the allocation and application (app) of the COVID-19 vaccine were seemingly associated with the wealth gap between countries, constant mutations of the virus may make fast-tracking vaccines ineffective, and the vaccine coverage rate is a crucial issue related to vaccine effectiveness.^[20,21] As such, the first research question about vaccination equality around the world is derived from the statistical report from vaccination: over 7 billion doses of COVID-19 vaccination were reported worldwide,^[22] and more than 3.9 billion people received a dose of a COVID-19 vaccine until November 1, 2021 (i.e., approximately 51.2% of the global population, approximately 93 doses per 100 people).^[23]

For vaccination information released by the statistical report, the vaccine doses administered per 100 people (VD100) in Israel, Chile, and the U.K. were 176, 196, and 156, respectively. However, there was a striking gap regarding vaccination programs in the different regions of the world based on the compiled data. For example, only 8.7% of the population received at least 1 dose of a vaccine in Africa, with the lowest vaccination rate compared to any other continent. Among them, South Sudan and Congo had vaccination rates of <0.1%. In addition, there are still no reported dosage data in some countries.^[23] To date, vaccine resources are relatively scarce due to insufficient supply and increasing demand worldwide. Therefore, equitable procurement, allocation, and administration have become acute dilemmas.^[20] Since most of the world population lives in low- and middle-income countries, it is a considerable challenge to guarantee an adequate vaccine dose in these regions.

1.3. The 2nd research question

The second research question is the effect of vaccination on the FR of the COVID-19 pandemic. Although much scientific evidence supports that vaccine administration is crucial to mitigate the outbreak,^[16,24] some people still have remained hesitant to get vaccinated, which has hindered the increase in vaccine coverage rates.^[25] Vaccine hesitancy makes it even more detrimental for low- and middle-income countries to roll out vaccines.^[26] In addition, to our knowledge, there are few coronavirus vaccine assessments of pragmatic effectiveness in low- and middle-income countries compared to high-income countries.^[27] Thus, further scientific data analysis on FR due to COVID-19 in each country after 1 or 2 vaccination doses is needed for verification.

1.4. The 3rd research question

Many reports of COVID-19 used traditional diagrams and did not adopt the dashboard feature on the Internet. The third research question thus emerged on how to apply dashboard-type visualization to provide further information to readers regarding the COVID-19 pandemic (e.g., the trends of vaccination and FR for countries/regions).

The dashboard is defined as a control panel on a web page that assembles visual messages about an issue or a topic that readers themselves can manipulate.^[28,29] More than 75 articles related to COVID-19 and dashboards were indexed in the PubMed library.^[30] However, these articles merely crammed charts regardless of the inflection points (IP) related to the trend on time-series-based data. For example, too many Figures in an article (e.g., 15, 18, and 20 charts, respectively^[31–33]) make readers confused about the focus of the research results. Dashboard-type visualization is thus required for development, particularly on the topic of vaccination effects against FRs around the world.

1.5. Study aims

Based on the 3 research questions raised in the previous sections, hypotheses were made, including the difference in vaccination rates is associated with the wealth gap between each country, the FR can be reduced by administering vaccinations, and the presentation of pandemic information could be displayed using modern computer modules on dashboards.

The aims of this study are to verify that the number of vaccination uptakes is related to the country gross domestic product (GDP), that vaccines can reduce FR, and that dashboards can provide more meaningful information than traditionally static visualizations.

2. Methods

2.1. Data sources

The COVID-19 cumulative number of confirmed cases (CNCCs) and deaths were downloaded from the GitHub website^[34] for countries/regions on November 6, 2021 (see Additional File 1). Four variables between January 1, 2021, and November 6, 2021, were collected, including CNCCs and deaths, GDP per capita, and VD100 in countries/regions.

All downloaded data are publicly released on the website.^[34,35] Ethical approval was not necessary for this study because all the data were obtained from the GitHub website.

2.2. Data arrangements and data presentation

2.2.1. To verify the 1st hypothesis.

Based on the VD100 data and the GDP in countries/regions, the scatter plot was drawn using the test statistic as Equation 1 to verify the significance of the association between them.

${\displaystyle \begin{array}{l}{\displaystyle t=\mathrm{C}\mathrm{C}\times \sqrt{\frac{n-2}{1-\mathrm{C}\mathrm{C}\times \mathrm{C}\mathrm{C}}},}\end{array}}$

${\displaystyle \begin{array}{l}{\displaystyle \mathrm{C}\mathrm{C}=\frac{n({\displaystyle \sum ^{}}xy)-({\displaystyle \sum ^{}}x)({\displaystyle \sum ^{}}y)}{[{\displaystyle \sum ^{}}{x}^2-{({\displaystyle \sum ^{}}x)}^2][{\displaystyle \sum ^{}}{y}^2-{({\displaystyle \sum ^{}}y)}^2]},}\end{array}}$

Equation 1 is a correlation testing via t test, where CC is the correlation coefficient, and n is the sample size.^[36] In Equation 2, ∑x = Sum of 1st values list, ∑y = Sum of 2nd values list, ∑xy = Sum of the product of 1st and 2nd values, ∑x² = Sum of squares of 1st values, ∑y² = Sum of squares of 2nd values.

The Kano diagram^[37,38] was applied to present their relationship (i.e., whether GDP can be used to predict VD100). Three parts are divided: vaccination-oriented (colored green), 1-dimensional relationship between VD100 and GDP (colored yellow), and GDP-oriented (colored red).

2.2.2. To verify the 2nd hypothesis.

For reporting a correlation between 2 continuous variables, the CC itself can serve as the effect size index—for instance, the 2 variables of the VD100 (starting at the 14 days after the vaccination administration in the countries/regions) and the FR (=death toll ÷ CNCCs). A forest plot^[39–41] was used to verify the vaccination effect against FR. The effect sizes based on correlations in the forest plot are described below:

1. The estimate of the correlation parameter is the sample CC (denoted by CC). The variance of CC is denoted by Equation 3.^[42]

${\displaystyle \begin{array}{l}{\displaystyle V_{\mathrm{C}\mathrm{C}}=\frac{{(1-\mathrm{C}\mathrm{C})}^2}{n-1},}\end{array}}$

1. The correlation was converted into Fisher z scale (not to be confused with the z score used with significance tests), and all analyses were performed using the transformed values. The results, such as the summary effect and its confidence interval, were then converted back to correlations for presentation. This is analogous to the procedure used with odds ratios or risk ratios, where all analyses are performed using log-transformed values and then converted back to the original metric.^[42]
2. The transformation from sample correlation CC to Fisher z is given by Equation 4.

${\displaystyle \begin{array}{l}{\displaystyle z=0.5\times \ln \frac{1+\mathrm{C}\mathrm{C}}{1-\mathrm{C}\mathrm{C}},}\end{array}}$

1. The variance of z is given by Equation 5.

${\displaystyle \begin{array}{l}{\displaystyle V_z=\frac{1}{n-3},}\end{array}}$

The standard error is shown in Equation 6.

${\displaystyle \begin{array}{l}{\displaystyle \mathrm{S}\mathrm{E}\mathrm{z}=\sqrt{V_z},}\end{array}}$

1. The variance and the standard error are used to yield summary effects and confidence limits. We then convert each of these values back to correlation units using Equation 7.

${\displaystyle \begin{array}{l}{\displaystyle \mathrm{C}\mathrm{C}=\frac{e^{2z}-1}{e^{2z}+1},}\end{array}}$

2.2.3. To verify the 3rd hypothesis.

Two types of dashboards were demonstrated in this study, including scatter plots and choropleth maps.^[43]

2.2.3.1. Scatter plot.

To understand the relationship between the IP and CC in VD100 and the FR for countries/regions, a scatter plot was applied to observe differences in CC on VD100 and FR based on IPs between G7 and nonG7 countries.^[44]

2.2.3.2. Choropleth map.

The geospatial distribution of the CC on VD100 and FRs was drawn in a choropleth map. The darker colors in regions indicate a stronger negative relationship between VD100 and FRs using the transformation formula (=(1 − CC) + 2).

2.2.3.3. Line-chart plots provided to know the trend of time-series based data.

Three types of line-chart plots were drawn for a better understanding of the trend and relationship between 2 variables, including frequencies and their corresponding vaccinations, cumulative FRs over time, and the relationship between VD100 and FRs.

To draw 2 variables with different scaling scores on a common y-axis, the formula in Equation 7 was applied.

${\displaystyle \begin{array}{l}{\displaystyle \mathrm{T}\mathrm{h}\mathrm{e}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{m}\mathrm{o}\mathrm{n}\mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{h}\mathrm{e}y\text{-}axis=\frac{(O_i-MIN)}{(MAX-MIN)}\times MA{X}_r,}\end{array}}$

where MAX_r is the maximum in a reference based on VD100. O_i is the observed value at element i on FR. Both MAX and MIN refer to the maximum and minimum, respectively, in contrast to the FR variable^[45,46]

3. The IP days on VD100 and FR

Item response theory^[47,48] based on the Newton–Raphson Iteration Method^[49,50] was applied to compute the IP days on VD100 and FR.

The IP refers to a point on a smooth plane curve where curvature changes sign from an increasing concave (concave downward) to a decreasing convex (concave upward) shape, or vice versa.^[45,46,51] The CNCCs in a country/region can be modeled on an ogive curve to represent the ability to contain COVID-19.^[52] The IP is defined at the moment of the outbreak to decrease after a peak.^[53] In the cases of VD100 and FR, the IP is referred to as the point at which it starts to decline. The longer the IP days are, the more VD100 (or FR) delays there are on the respective trajectory.

3.1. Statistical tools and data analysis

Visual representations of the study results were drawn using the author-made modules in MS Excel. All figures, except Figure 1, were plotted online on Google Maps or the website. The model parameter estimation was executed to search for the IP using the Newton–Raphson Iteration Method^[49,50]; see the details on the MP4 video in Additional File 1. The 3 major tasks are shown in the study flowchart in Figure 1.

Figure 1: . Study flowchart with 3 tasks in this study.

4. Results

4.1. Verification of the number of vaccines administered is related to the country GDP

The association between VD100 and GDP was statistically significant (CC = 0.68, t = 13.14, P < .001), as shown in the Kano diagram (Fig. 2), indicating that a higher GDP drives more vaccinations administered in countries/regions. Meanwhile, the G7 countries are located in top circle, implying that those countries with higher GDP earn more vaccinations for their native citizens. The 1st hypothesis is thus supported in Figure 2. Kano diagrams are divided into 3 parts, including countries/regions which are green in favor of the attribute on the y-axis, red in favor of the attribute on the x-axis, and yellow on the 1-dimensional area.

Figure 2: . Association between GDP and vaccination rate in countries/regions (note. 3 parts are divided in the Kano diagram, including those countries/regions in favor to the attribute in green toward y-axis, in red toward x-axis, and in yellow on the unidimensional area). GDP = gross domestic product.

4.2. To verify the vaccines can reduce the FR

Based on the CC between VD100 and FR, FR can be reduced by administering vaccinations that are proven except for the 4 groups of Asia, Low-income, Lower-middle income, and South America, as shown in Figure 3. The 2nd hypothesis is thus supported in Figure 3.

Figure 3: . Vaccination effects of decreasing the fatality rate across continents, areas, and groups using the correlation coefficients.

4.3. To verify dashboards can provide more meaningful information than the traditionally static visualizations to readers

The scatter plot in Figure 4 tells us that the standardized deviation in VD100 (in green bubbles) is greater than that in FR (in yellow bubbles) and those G7 counties located in quadrant III have a negative CC between VD100 and FR based on shorter IPs (i.e., provided vaccinations and contained FR in relatively earlier days in 2021).

Figure 4: . Distribution of IP days for vaccination and fatality rates against the association between vaccination and fatality rates on axes x and y, respectively. IP = inflection point.

The app with a dashboard-type choropleth map (Fig. 5) in comparison of CC between VD100 and FR for countries/regions is made using line charts (e.g., the trends in vaccinations and FRs in Figure 6 once the country of Australia is clicked). The CC between VD100 and FR in Australia is −0.96 when observing the lower panel in Figure 6 (i.e., different directions in the 2 trends). Thus, the 3rd hypothesis is also supported by viewing Figures 4 to 6.

Figure 5: . Geospatial distribution of correlations between vaccination and fatality rates in countries/regions.

Figure 6: . Line chart plot on vaccination and fatality rates: an example from Australia (corr. = −0.96).

4.4. Online dashboards shown on google maps

All dashboards in figures appear once the QR code is scanned or the links^[54–57] are clicked. Readers are advised to examine the details about the information for each entity.

5. Discussion

5.1. Principal findings

We observed that the higher the GDP, the more vaccines are administered (association = 0.68, t = 13.14, P < .001) in countries, the FR can be reduced by administering vaccinations that are proven except for the 4 groups of Asia, Low income, Lower middle income, and South America, and the app with dashboard-type choropleth map that shows the comparison of vaccination rates for countries/regions using line charts (e.g., the trends in vaccinations, FRs). Thus, the 3 hypotheses were supported in this study.

5.2. Validation of known findings

As of November 12, 2021, approximately 64% of the population in high-income countries has been vaccinated with at least 1 dose, compared to only 6.48% in low-income countries.^[58] Our research also verified that there was a gap in the vaccination rate by GDP inequity. Therefore, the results in Figure 2 supported the 1st hypothesis that countries with higher GDP have vaccinated a significantly higher proportion of people (CC = 0.68, t = 13.14, P < .001).

The stark disparities of COVID-19 vaccinations resulted from multiple factors, including vaccine production, procurement, allocation, distribution, and administration. Low-income countries usually have insufficient vaccine supply and healthcare providers, backward transportation, public health facilities, and refrigeration equipment for vaccine storage.^[59] In addition, the establishment of effective vaccine policies and immunization information systems in low-income countries is crucial to increasing the vaccination rate.

Due to low vaccination coverage, low-income countries have little resistance to the rapid spread of coronavirus variants. The feeble healthcare systems and fragile economies in low-income countries are also worse due to the pandemic, which makes vaccination rollout more challenging. Moreover, vaccine inequality would lead to financial strain over the entire global market. A RAND Europe report estimates that if the poorest countries cannot obtain enough vaccines and reach sufficient herd immunity, delayed vaccination timelines would cost the global economy $153 billion a year in GDP (including a loss of $40 billion in EU and $16 billion in US). Based on the economic estimate and benefit-to-cost ratio in the report, every $1 invested in vaccine supply from wealthier countries would return approximately $4.8.^[60]

According to the global data in Figure 3, a statistically significant negative correlation between vaccination and FR was found (P < .001), which means that a higher VD100 caused fewer FRs, except for the 4 groups of Asia, Low-income, Lower-middle income, and South America. Many previous studies also reported that vaccination could reduce FRs caused by COVID-19.^[14,15,18] However, the vaccine coverage rate is still insufficient to resist the rapid transmission of COVID-19, especially in low-income countries. Even worse, the continued spread of COVID-19 led to a conducive environment for viral evolution and the development of new mutations (e.g., delta variants). The new virus variants might be more contagious, evade vaccine immunity more easily, or have a higher risk of hospital admission and mortality.

In this study, we also demonstrated the use of a dashboard on a website to present the study results. Readers can click on items on the dashboard to access information of interest on their own. The dashboard allows the content of this article to be focused on the issues and results of the study while retaining a large amount of detailed information. Therefore, the third hypothesis (a greater amount of meaningful information can be presented using a dashboard) has also been validated.

5.3. Features of this study

Viewing Figure 2, it is evident that the number of vaccinations is related to the wealth of the country. Instead of using a traditional scatter plot, we applied the Kano diagram with different colors to distinguish the different relationships between GDP and the number of vaccination uptakes. Combined with the CCs and t test results (CC = 0.68, t = 13.14, P < .001), the diagram clearly indicates significantly positive correlations between GDP and vaccination rate.

In several studies, choropleth maps have been used to report the COVID-19 situation across different geographical regions. However, the integration of a choropleth map into a dashboard is relatively rare. As shown in Figure 5, this enables readers to find more detailed information.

Finally, a video was attached to explain the procedures used in the present study for the organization of data in Microsoft Excel, writing of HTML codes, and Google Map visualization on the dashboard (see the MP4 file in Additional File 1). The provision of an abstract video to assist readers in understanding the processes and key methods of the study is also rare among such studies.

5.4. Limitations and recommendations

Currently, COVID-19 vaccination campaigns remain underway in countries around the world, and new coronavirus variants (e.g., delta and gamma) are continuously emerging and spreading. The results of this study are, therefore, merely representative of the pandemic situation from January 1 to November 6, 2021. Further data will be required to determine if changes occur in the proposed hypotheses of this study over time.

Visualization with the dashboard on Google Maps developed in this study faces a number of limitations in practical apps. For instance, application programming interface keys must be obtained from Google before the application programming interface of Google Maps can be displayed on a website. The development of HTML codes for the display of content in Google Maps also requires the assistance of an app engineer, which limits widespread app.

Data on the number of confirmed COVID-19 vaccinations and deaths in various countries were downloaded and converted, based on country population, to the cumulative number of vaccinations per 100 people per day, CNCC per 100,000 people, and cumulative number of deaths per 100,000 people. These data were then compared with the population and GDP data of the countries.^[34] Given the daily changes and updates in pandemic-related data (for instance, the vaccination rate in Taiwan sharply rose from 6% on June 19, 2021, to 42% by August 27, 2021, and 73% by October 31, 2021),^[22] corresponding changes must also be made to the data presented on the dashboard. This also poses a limitation to the applicability of this study.

6. Conclusion

In general, the COVID-19 vaccination rate significantly correlates with the country GDP. In addition, the FR worldwide has decreased due to vaccination. Using the Kano model, forest plot, and choropleth map to display information on dashboards, we successfully validated our 3 proposed hypotheses and were able to focus on the key study results while retaining detailed information. The methods described in this study may serve as a reference point for future research.

Acknowledgments

We thank Enago (www.enago.tw) for English language revision.

Author contributions

Conceptualization: Tung-Hui Jen, Jian-Wei Wu.

Investigation: Willy Chou.

Methodology: Tsair-Wei Chien.