Development of a Dashboard for the Evolution of COVID-19

UNIVERSIDAD COMPLUTENSE DE MADRID FACULTAD DE INFORMÁTICA DEPARTAMENTO DE SISTEMAS INFORMÁTICOS Y COMPUTACIÓN Trabajo Fin de Grado en Ingeniería Informática Desarrollo de un Dashboard para la Evolución de la COVID-19 Development of a Dashboard for the Evolution of COVID-19 Dirigido por: Sonia Estévez Martín Adrián García Sánchez Harold Luis Pascua Cajucom Dale Francis Valencia Calicdan Curso académico 2020-21 Convocatoria Junio Acknowledgements First of all, we want to give thanks to our project director, Sonia Estévez Martin, who has always helped us when we have needed it and has given us guidelines to develop the project in the best possible way. We also want to mention Victoria López, who has also helped us in planning the work and choosing adequate metrics for the correct preparation. We also want to acknowledge all the efforts made on the part of the partner group, composed of Iván Jesús Saugar Peinado, Sergio Sánchez Ortiz, and Álvaro San José Guerra, who have provided us with the datasets with which to work, a fundamental part for the development of the project and the achievement of the objectives. Finally, we want to thank all family and friends for all the moral support given throughout this project. 2 Abstract The SARS-CoV-2 coronavirus is responsible for the COVID19 disease that has caused economic and health issues around the world. To enforce measures that curbs the growth of this disease, this pandemic must be monitored. The objective of this project is to create an online, open source dashboard that monitors the evolution of COVID-19 by country and by its principal indicators. To do so, this project has developed a series of Scrum sprints, together with various technologies, such as RStudio, Shiny, and Google Sites. The dashboard created serves to analyze data and to present it in a clear manner because of mastery of different tools, methodologies, and analytical techniques that have been used. This dashboard differs from other existing dashboards such that it specializes in presenting indicators such as transmission index, danger index, and cumulative incidence in addition to raw data like positive cases, deaths, and recoveries. As a result, a dashboard was created that can inform users about the progress of the pandemic in graphic and map forms, especially under the aforementioned indicators. Keywords COVID-19, Coronavirus, Dashboard, RStudio, Shiny, Data Analysis, Accumulated Incidence, R0, Danger Index Resumen El SARS-CoV-2 coronavirus es responsable de la COVID-19, la cual ha causado problemas económicos y sanitarios alrededor de todo el mundo. Para hacer cumplir medidas que frenen el crecimiento de la enfermedad, la pandemia debe ser monitorizada. El objetivo de este proyecto es crear un dashboard online y de código abierto que monitorice la evolución del COVID-19 por país y por sus principales indicadores. Para lograr esto, el proyecto ha sido elaborado mediante una serie de iteraciones de Scrum, unidas a varias tecnologías como RStudio, Shiny y Google Sites. El dashboard creado sirve para analizar los datos y para presentarlos de una manera clara, debido al dominio de las diferentes herramientas, metodologías y técnicas analíticas que han sido utilizadas. Este dashboard difiere de otros existentes en que se especializa en presentar algunos indicadores como son el índice de transmisión, el índice de peligrosidad y la incidencia acumulada además de datos brutos como son los casos positivos, las muertes y los recuperados. Como resultado, ha sido creado un dashboard que informa a los usuarios sobre el progreso de la pandemia mediante mapas y gráficos, especialmente sobre los indicadores mencionados anteriormente. Palabras Clave COVID-19, Coronavirus, Dashboard, RStudio, Shiny, Análisis de datos, Incidencia Acumulada, R0, Índice de peligrosidad Contents 1 Introduction 8 1.1 Motivation............................. 8 1.2 Objectives............................. 9 1.3 WorkPlan............................. 10 1.4 Document Structure . . . . . . . . . . . . . . . . . . . . . . . 11 2 State of the Art 13 2.1 Types of COVID-19 Dashboards . . . . . . . . . . . . . . . . . 13 2.1.1 Types of Dashboards by Geographic Scope . . . . . . . 14 2.1.2 Types of Dashboards by Type of Data Used . . . . . . 17 2.2 Applications for Dashboard Creation and Configuration . . . . 21 2.3 Technical Requirements for Creating a Dashboard . . . . . . . 23 3 Data of Interest for the Project 25 3.1 Existing Databases of Interest . . . . . . . . . . . . . . . . . . 25 3.2 Graphs of Interest . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3 Variables and Indicators of Interest . . . . . . . . . . . . . . . 31 3.3.1 Variables.......................... 31 3.3.2 Indicators ......................... 33 3.3.3 Smoothed Variables and Indicators . . . . . . . . . . . 35 4 Data Preparation 36 4.1 Databases Used as Bases for Database Creation . . . . . . . . 36 4.2 Dataset Creation for Project Use . . . . . . . . . . . . . . . . 37 4.2.1 FirstDataset ....................... 37 4.2.2 Second Dataset . . . . . . . . . . . . . . . . . . . . . . 37 4.2.3 FinalDataset....................... 39 4.3 Dataset Quality Evaluation . . . . . . . . . . . . . . . . . . . 39 4 CONTENTS 5 5 Project Development 41 5.1 ProjectPlanning ......................... 41 5.1.1 Development Methodology . . . . . . . . . . . . . . . . 41 5.1.2 GoogleSites........................ 42 5.2 ProductBacklog ......................... 43 5.3 FirstSprint ............................ 44 5.4 SecondSprint........................... 49 5.5 ThirdSprint............................ 55 5.6 FourthSprint ........................... 59 5.7 RStudio Libraries Used . . . . . . . . . . . . . . . . . . . . . . 67 5.8 Scrum Sprint Schedule . . . . . . . . . . . . . . . . . . . . . . 68 6 Results 70 6.1 FinalDataset ........................... 70 6.2 Dashboard and Website . . . . . . . . . . . . . . . . . . . . . 72 6.3 Example Analysis: Comparing Spain and the Philippines . . . 75 6.4 Integrating the Dashboard to Other Websites . . . . . . . . . 79 7 Individual Contributions 80 7.1 Contributions made by Adrián García Sánchez . . . . . . . . . 80 7.2 Contributions made by Dale Francis Valencia Calicdan . . . . 82 7.3 Contributions made by Harold Luis Pascua Cajucom . . . . . 84 8 Conclusions and Future Work 86 8.1 Conclusions ............................ 86 8.2 FutureWork............................ 87 List of Figures 2.1 ISCIII COVID-19 Dashboard (Application Used: Shiny) . . . 15 2.2 Covidly Dashboard (Application Used: Mapbox) . . . . . . . . 16 2.3 Global Map found in the COVID-19 Dashboard by JHU (Application Used: ArcGIS Dashboards) . . . . . . . . . . . . . . 16 2.4 U.S. Map found in the COVID-19 Dashboard by JHU (Application Used: ArcGIS Dashboards) . . . . . . . . . . . . . . . . 17 2.5 WHO COVID-19 Dashboard (Application Used: Sprinklr) . . 18 2.6 Covid-19 TrialsTracker Dashboard (Application Used: Tableau) 19 2.7 Covid-Trials.org Dashboard (Application Used: Cytel) . . . . 19 2.8 SeroTracker Dashboard (Applications Used: Recharts and Leaflet) 20 2.9 The Vaccine Tracker (Applications Used: Gatsby and React) . 20 2.10 Typical architecture of a Dashboard . . . . . . . . . . . . . . . 23 3.1 Rolling 7-day average of COVID-19 cases as of October 31, 2020 26 3.2 Table of confirmed cases and deaths for each country in the past14days............................ 27 3.3 Line graph of the cumulative confirmed COVID-19 cases per millionpeople........................... 28 3.4 Daily new confirmed COVID-19 cases per million people, Oct 31,2020 .............................. 29 3.5 BoxPlot.............................. 30 5.1 Scrum process diagram . . . . . . . . . . . . . . . . . . . . . . 42 5.2 Initial designs (ordered from top to bottom, left to right) . . . 45 5.3 Final prototype design. Map View 1 . . . . . . . . . . . . . . 46 5.4 Final prototype design. Map View 2 . . . . . . . . . . . . . . 46 5.5 Final prototype design. Map View 3 . . . . . . . . . . . . . . 47 5.6 Final prototype design. Graph View . . . . . . . . . . . . . . 47 6 LIST OF FIGURES 7 5.7 Sprint 1 result (graph view) . . . . . . . . . . . . . . . . . . . 48 5.8 Sprint 1 result (map view) . . . . . . . . . . . . . . . . . . . . 48 5.9 Revised prototype design (graph view) . . . . . . . . . . . . . 51 5.10 Revised prototype design (map view) . . . . . . . . . . . . . . 51 5.11 Sprint 2 result (line graph) . . . . . . . . . . . . . . . . . . . . 52 5.12 Sprint 2 result (box plot) . . . . . . . . . . . . . . . . . . . . . 53 5.13 Sprint 2 result (map view) . . . . . . . . . . . . . . . . . . . . 54 5.14 Revised prototype design (graph view) . . . . . . . . . . . . . 56 5.15 Revised prototype design (map view) . . . . . . . . . . . . . . 56 5.16 Sprint 3 result (graph view) . . . . . . . . . . . . . . . . . . . 58 5.17 Sprint 3 result (map view) . . . . . . . . . . . . . . . . . . . . 58 5.18 Dashboard design (graph view) . . . . . . . . . . . . . . . . . 61 5.19 Dashboard design (map view) . . . . . . . . . . . . . . . . . . 62 5.20 Website design (home view) . . . . . . . . . . . . . . . . . . . 63 5.21 Website implementation (home view) . . . . . . . . . . . . . . 65 5.22 Website implementation(about us view) . . . . . . . . . . . . . 65 5.23 Website implementation (about this project view) . . . . . . . 66 5.24 Dashboard architecture . . . . . . . . . . . . . . . . . . . . . . 68 5.25 Realized sprint dates . . . . . . . . . . . . . . . . . . . . . . . 69 6.1 Inc of Spain from July 2020 to May 2021, as shown in Dashboard 72 6.2 Inc of Spain from July 2020 to May 2021 . . . . . . . . . . . . 72 6.3 Website with integrated Dashboard . . . . . . . . . . . . . . . 73 6.4 Functions available in the Graph View . . . . . . . . . . . . . 74 6.5 Graphs comparing Spain and the Philippines under R0 . . . . 76 6.6 Map comparing Spain and the Philippines under R0 on June 8,2020............................... 76 6.7 Graphs comparing Spain and the Philippines under DI . . . . 77 6.8 Map comparing Spain and the Philippines under DI on June 8,2020............................... 77 6.9 Graphs comparing Spain and the Philippines under Inc . . . . 78 6.10 Map comparing Spain and the Philippines under Inc on June 8,2020............................... 79 Chapter 1 Introduction COVID-19 [1] is an infectious disease caused by the coronavirus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). It is held responsible for the pandemic that was happening around the world declared by the World Health Organization (WHO) on March 11, 2020. An article published by WHO [2] suggested that the start of the outbreak of this virus originated from Wuhan, China at a local market around late December 2019. Evidently, the COVID-19 pandemic brought devastating effects to everyone around the globe. Public health facilities were brought to their limits [3], [4], economies worldwide took a massive hit [5], and, most importantly, many lives have been lost. WHO had stated that, as of November 1, 2020, nearly 46 million positive cases and 1.2 million deaths have been reported from around the world [6]. As such, the global phenomenon has challenged every state and country to formulate their own countermeasures and impede the rate at which citizens are being infected. While some of these implementations have been proven to be effective, other factors such as inexperienced health workers, poor health systems, lack of social distancing, and scarce knowledge of the coronavirus make this problem all the more difficult to resolve. 1.1 Motivation Various dashboards, both online and offline, have already been developed for analytical use, with some having been developed to visually represent the development of the COVID-19 pandemic in numerous aspects. Various features 8 CHAPTER 2. STATE OF THE ART 15 Figure 2.1: ISCIII COVID-19 Dashboard (Application Used: Shiny) may or may not be detailed. For example, a dashboard that uses data stored in the cloud will have the capacity to present data in a degree of specificity similar to dashboards of national scope, thanks to the ability of cloud-based platforms to have a virtually limitless amount of storage [9], while dashboards that are more limited in storage may opt to show summaries of data for each country instead. Covidly [10] (see Figure 2.2) is an example of a dashboard with a global scope, projecting data, such as cases, deaths, recoveries, and other variables related to the pandemic, summarized for each country. Hybrid Scope While there are dashboards that either have a national scope or a global one, there are certainly dashboards that are a mix of the two, carrying both national data of various countries and local data of certain countries, as seen in the two maps presented by the COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) [11] that show both the national data of different countries and the data of the counties in the United States of America (see Figures 2.3 and 2.4). CHAPTER 2. STATE OF THE ART 16 Figure 2.2: Covidly Dashboard (Application Used: Mapbox) As with dashboards of global scope, the specificity of the data on a percountry level may depend on the capacity of the database being used for the application. Figure 2.3: Global Map found in the COVID-19 Dashboard by JHU (Application Used: ArcGIS Dashboards) CHAPTER 2. STATE OF THE ART 17 Figure 2.4: U.S. Map found in the COVID-19 Dashboard by JHU (Application Used: ArcGIS Dashboards) 2.1.2 Types of Dashboards by Type of Data Used In terms of the data that are often analyzed and presented in existing dashboards, it can be observed that dashboards use one of the four: epidemiological data, data regarding clinical trials, data that describe the seroprevalence of the Coronavirus in a certain area, and data on the progress of vaccine development. Epidemiological Data Tracker This is the type of dashboard that is usually associated with the term “COVID-19 dashboard”. dashboards centered around epidemiological data focus mostly on the number of confirmed cases and deaths, shown both on a daily basis and as an accumulation over a certain period of time. Other data related to the development of the pandemic, including testing rate, hospitalization count, intensive care unit (ICU) admission count, and recoveries, may also be included and presented to provide users a more in-depth analysis. As previously mentioned, Epidemiological Data Trackers are the most common type of COVID-19 dashboards. Hence, many well-known dashboards, such as the Dashboard of JHU and the World Health Organization (WHO) COVID-19 Dashboard [12] (see Figure 2.5) are good examples. CHAPTER 2. STATE OF THE ART 18 Figure 2.5: WHO COVID-19 Dashboard (Application Used: Sprinklr) Clinical Trial Tracker This type of dashboard aims to present users with updates regarding the clinical trial research of COVID-19 intervention methods like vaccines, antiviral drugs, and even traditional Chinese medicine. Usual presented data include trial ID, trial location, trial start date, trial completion date (if applicable), type of treatment, presented outcome and the URL to the results. Examples include the Covid-19 TrialsTracker [13] (see Figure 2.6), a product of The DataLab at the University of Oxford. It uses data from the International Clinical Trials Registry Platform (ICTRP) to display clinical trials related to the treatment of COVID-19 through tables and graphs. Another example is Covid-Trials.org [14, 15](see Figure 2.7), whose functionality is similar, with the exception of being able to display the data in a map as well. Seroprevalence Dashboard Seroprevalence Dashboards, such as SeroTracker [16] (see Figure 2.8), focus on seroprevalence of COVID-19 antibodies. This means that dashboards of this type present the frequency of individuals in a certain population that contain COVID-19 antibodies. CHAPTER 2. STATE OF THE ART 19 Figure 2.6: Covid-19 TrialsTracker Dashboard (Application Used: Tableau) Figure 2.7: Covid-Trials.org Dashboard (Application Used: Cytel) For health authorities, seroprevalence may be an interesting variable to observe, as it plays a role in antibody testing, a crucial factor in both monitoring the evolution of the Coronavirus and estimating the fatality rate caused by infections [17]. CHAPTER 2. STATE OF THE ART 20 Figure 2.8: SeroTracker Dashboard (Applications Used: Recharts and Leaflet) Vaccine Tracker This type of dashboard tracks the progress of the development of vaccines against the Coronavirus. The name of the vaccine and the current status/phase of the vaccine in the development process is included. Additionally, vaccines can be filtered according to their current status. A good example of this is The Vaccine Tracker [18] (see Figure 2.9). Figure 2.9: The Vaccine Tracker (Applications Used: Gatsby and React) CHAPTER 2. STATE OF THE ART 21 2.2 Applications for Dashboard Creation and Configuration There are numerous applications available on the market that specialize on the creation and configuration of dashboards, both paid and free. For illustration purposes, six well-known applications are discussed: Shiny, Power BI, Grafana, Sisense, Domo, and ArcGIS. Shiny Shiny [19] is an open-source package based on R [20], a language and environment commonly used for statistical programming that allows an easy creation and configuration of interactive web-based applications, including dashboards. Shiny applications can be made using RStudio [21], the integrated development environment (IDE) of R, and they can also be extended through CSS, htmlwidgets, and JavaScript. A huge advantage that Shiny has over other applications for dashboard creation is that it is free to use, without imposing limitations of any sort on its complete functionality. Any user can create a fully functional dashboard through Shiny without incurring any costs. Developed products can either be deployed to the Shinyapps.io cloud, with both free and paid options [22], or be integrated on-premises with an existing server. Power BI Power BI [23] is a business intelligence (BI) software created by Microsoft. It allows data modeling and visualization through the use of artificial intelligence (AI). In addition, Power BI provides interoperability with other Microsoft applications such as Azure and Excel, end-to-end data protection, and numerous data connectors that permit connections to data sources such as Azure SQL Database and Excel. While Power BI is a paid service, an option of a free trial is also available for those who want to get an idea of how the application works first hand. Grafana Grafana [24] is a free and open-source platform that features support for over 30 databases, including Graphite, InfluxDB, and Prometheus, for CHAPTER 2. STATE OF THE ART 22 use in a single dashboard and has a wide range of dashboards and plugins available in its official library. It also allows the development of dashboards via collaboration and the definition of alerts (for example, when a certain threshold value is surpassed for the data being monitored) that notifies the users. Sisense Sisense [25] is a cloud-native platform that gives its users the functions necessary to create powerful analytical solutions such as embedded analytics, data mashups, access controls at system, object, data, and process levels, and customized alerts. It offers Products can be deployed either via a private cloud, on commodity hardware, or by taking a hybrid approach. Sisense also offers data connectors to AWS, Snowflake, and Google BigQuery. Being a BI platform that focuses on analytics of enterprise data, this application does not offer any free versions. Domo Domo [26] is a cloud-based BI platform that consists of three layers: data integration, business intelligence and analytics, and the creation of intelligent applications. It boasts of fast performance, unlimited scalability, data automation, and a large array of data connectors, from databases to even social networks, allowing users from businesses to software developers to focus on creating the product without worrying about the underlying infrastructure. As with Power BI, Domo also has a free trial option for users who want to try its capabilities in connecting, transforming, and visualizing data. ArcGIS Dashboards An online and a desktop platform created by Esri, ArcGIS Dashboards [27] allows the creation of dashboards that feature location-based analytics as its main advantage over other platforms. It also has flexibility, configurability, a suite of available and ready-to-use data visualization tools like maps, lists, and charts, as well as tools that allow users to interact with dashboards created through this application. This application usually requires to be purchased for use, but it also offers a 21-day free trial period, given that it is for non-production use only. CHAPTER 2. STATE OF THE ART 23 2.3 Technical Requirements for Creating a Dashboard The typical architecture of a dashboard consists of four layers, as shown in Figure 2.10 [28]. As such, these will make up the technical requirements that must be complied in order to create a fully functioning dashboard. Figure 2.10: Typical architecture of a Dashboard Front End Being the part of the application with which the user interacts the most, it is undeniably the most important part of the dashboard. The interface implemented in this layer must be user-friendly and interactive. Careful layout planning and selection of graphs and other types of visual data play a significant role in helping the users understand and interpret clearly the data being shown. In addition, accessibility features, such as appropriately sized text and labels and graph annotations, must also be considered. Back End The back end is where the server performs most of its operations. It contains various scripts and source codes for performing analyses and transformations on the data collected, as well as scripts for making predictions CHAPTER 2. STATE OF THE ART 24 and decisions based on the data. In the case of dashboards, the main goal of this layer is to prepare all data for visualization in the front end. Mechanisms for data storage, such as databases used by the server, can also be found here. As such, knowledge of basic database concepts such as creation, insertion, deletion, aggregation, and projection are a must. It is also important to know the advantages and disadvantages of relational and non-relational databases to determine which type of database is best suited to the application. In the case of dashboards, relational databases, many of which are created using SQL, are ideal as this makes data analysis and presentation easier to do. Data Acquisition As explained by its name, the data acquisition layer consists of scripts whose objective is to obtain data from source systems and place that data as metric values in the storage found in the back end. There are multiple methods of acquiring data from these sources, from static analysis to data mining. Hence, knowledge of these processes, as well as knowledge of the structures found in the source systems, is a prerequisite for this layer to function properly. Source Systems While source systems are not actually part of the dashboard, they still hold a crucial part in its operations. These systems store data that may be acquired to perform analyses, transformations, and visualizations. Examples include code repositories like GitHub and even databases from other websites. CHAPTER 3. DATA OF INTEREST FOR THE PROJECT 31 3.3 Variables and Indicators of Interest Since the beginning of the COVID-19 pandemic, various models have been developed in an attempt to accurately describe and predict the evolution of the virus. These models include different variables, some of which were discovered and calculated by means of scientific investigations. As the model to be used for the dashboard is inspired by the flow network models presented by López and Cukić [34, 35], as well as Kucharski et al [36], the team would use not only variables commonly used in existing databases for COVID-19 surveillance, but also those that are related to the model and can be derived mathematically from the commonly found variables, such as accumulated cases and deaths, as well as indicators such as the transmission and danger indices and the accumulated incidence, which will be discussed shortly. 3.3.1 Variables Daily Positive Cases This pertains to the number of positive COVID-19 cases recorded in a 24-hour interval. Furthermore, this variable can be classified according to the the type of diagnostic test used to confirm the positive cases [37]: •Antigen Tests: Using samples collected through nasal or throat swabs, antigen tests are used to detect a protein that makes up part of the coronavirus and are useful for identifying cases that are approaching states of peak infection. Compared to the other types of tests, antigen tests are cheaper and faster, but are also less accurate, being able to produce false positives and false negatives. •Molecular/PCR Tests: Also using nasal and throat swabs, molecular tests focus on identifying the genetic makeup of the coronavirus using different methods, with the most famous one being the polymerase chain reaction (PCR). While some molecular tests have shown false negative results for up to 20% of the time, this type of test remains more accurate in identifying positive cases than antigen tests. CHAPTER 3. DATA OF INTEREST FOR THE PROJECT 32 Deaths These are the number of deaths caused by the coronavirus disease. We assume these figures are reported by the hospitals daily. Recoveries These are patients who have been discharged from the hospital or those that are home-quarantined and have been considered recovered by their attending physicians. This variable will remain useful when taking into account the total number of recoveries reported. Accumulated Positive Cases As explained in its name, this variable gathers the total number of positive cases reported so far. This is calculated by retrieving the sum of daily positive case counts reported from start to present. Active Cases These are positive cases that are neither counted as a death nor a recovery, meaning that these are ongoing cases of COVID-19. As a person infected with COVID-19 may experience mild to severe symptoms, it is recommended that not all active COVID-19 cases be considered as hospitalizations, but rather be broken down into the following categories: •Home-Quarantined: Patients who experience mild symptoms are not recommended to be hospitalized (to allow hospitals to accommodate cases of higher severity), so they are quarantined at home instead. They are usually monitored by their respective physicians through remote means such as examinations over the phone. •Hospitalized: Patients who experience severe symptoms and are in need of hospital care fall under this category. These patients stay in this state until they recover and are deemed safe to discharge or until their condition worsens into a critical state. •Critical: Critical cases involve patients who are experiencing extreme symptoms and require immediate attention by being admitted into intensive care units (ICUs). CHAPTER 3. DATA OF INTEREST FOR THE PROJECT 33 Number of Tests Done This consists of the accumulated number of tests performed in a certain area. This will include both tests that returned positive results as well as negative ones. To produce more comprehensible numbers, this number may be presented as a ratio of tests per 1M/100,000 inhabitants. Number of Cases per 1M/100,000 Inhabitants Comparing the number of cases (be it active cases, number of deaths, or number of recoveries) may be difficult to understand for the average user. This variable aims to project these numbers as a ratio of number of cases to number of inhabitants, providing a density of cases within a part of the population. Population-Weighted Density (PWD) PWD is one of the variables that explain the spread of the COVID-19 pandemic [38]. As this variable describes the density at which an average person lives, it can be observed that this influences the rate of deaths caused by the coronavirus. Increased social distancing results in a reduction in PWD (albeit temporarily), indicating effectiveness in measures taken by a unit of government. 3.3.2 Indicators Transmission Index (R0) The transmission index [34] represents the power of virus transmission and can be inferred by calculating the ratio between the number of infections recorded at a certain day tand the number of infections recorded 14 days before t, as seen in Equation 3.2. A value of 1 or greater indicates danger in the transmission of the virus while a value of less than 1 indicates no danger. c R0(t) = Inf(t) Inf(t−14) (3.2) CHAPTER 3. DATA OF INTEREST FOR THE PROJECT 34 Cumulative Incidence per 100,000 Inhabitants (Inc) As seen in Equation 3.3, this index determines the cumulative number of positive COVID-19 cases in the past 14 days per 100,000 inhabitants [34, 35]. As this variable takes into consideration the population of the country, it is useful for making fairer comparisons between the situations of different countries with regards to the pandemic. However, its disadvantage comes from the fact that this indicator only considers the number of infected people. Inci= ( 14 X j=1 Ci−j)∗100000/P, ∀i≥15 (3.3) Danger Index (DI) The danger index [34] is used to determine the level of danger a flow network presents. Using Equation 3.4, the DI obtained indicates little to no danger if its value is equal to or less than 0, while a higher DI indicates a more dangerous flow network. While both R0 and Inc only considers the number of infected cases, DI has an advantage such that it considers the number of positive cases, the number of deaths, and the number of recoveries, each of which can be weighed to provide more adequate values depending on the situation. However, compared to the Inc indicator, the population of the country is not taken into consideration. DI(t) = Inf(t) + F(t)−Rec(t) = X x∈V fx,INF (t) + X x∈V fx,F (t)−X x∈V fx,R(t) (3.4) CHAPTER 3. DATA OF INTEREST FOR THE PROJECT 35 3.3.3 Smoothed Variables and Indicators In obtaining data on COVID-19, there are times when large peaks are followed by sudden drops in value, possibly resulting into noisy data [39]. To counter this, data smoothing is applied by using an algorithm to remove noise from a dataset and allow patterns to stand out more clearly. In this case, the variables and indicators mentioned previously were smoothed by calculating the 7-day moving averages, found in Equation 3.5. ∀i≥4, mm(xi) = 1 7 i+3 X k=i−3 (xi)(3.5) Chapter 4 Data Preparation After having a list of possible sources of data for the project, the data would need to be prepared for use in the dashboard. This preparation process includes operations such as unifying the format of data, cleaning out invalid data, and placing the results together in a file that the dashboard can access and use to present the data visually. In this part of the project, a collaboration was initiated with another group, whose goal is to search and analyze available information on the COVID-19 pandemic. They would ultimately be in charge of providing the dataset to be used by the dashboard. 4.1 Databases Used as Bases for Database Creation As previously explained, the partner group was in charge of obtaining available information on the situations of each country. While most information can be found on the website of each country’s health agency (which was the case for major countries with considerably reliably governments such as Spain, Japan, and Russia), the following repositories are highlighted due to the fact that they contain information that has already been compiled and is constantly updated for convenience in data gathering: •https://github.com/datasets/covid-19 •https://github.com/cssegisanddata/covid-19 36 CHAPTER 4. DATA PREPARATION 37 4.2 Dataset Creation for Project Use 4.2.1 First Dataset In order to initialize the the development of the project, another dataset must be temporarily used for testing the functionality of the dashboard. For this purpose, a dataset provided by the project directors that contained data on the pandemic situation in 14 countries was used. The following columns are found in this dataset: •COUNTRY: country in which the data was recorded •FECHA: date of recording •CONTAGIADOS: daily number of positive cases of COVID-19 •FALLECIDOS: daily number of deaths caused by COVID-19 •HOSPITALIZADOS: daily number of hospitalizations due to COVID-19 •UCIs: daily number of COVID-19 patients admitted into ICUs 4.2.2 Second Dataset Created by the partner group, this dataset is separated into two files: one containing data pertaining to the United States alone and another containing data for the rest of the world. Compared to the previous dataset, this contains information on more countries, as well as more columns that consider variables that were previously not present, such as the number of active cases and the danger index [34, 35]. Another difference is that, when applicable, data is also organized by the provinces of the involved countries. This dataset consists of the following columns: •Country_Region: country/region in which the data was recorded •Province: province of the country in which the data was recorded, if available •Lat: latitudinal coordinate of the area’s location •Long: longitudinal coordinate of the area’s location CHAPTER 4. DATA PREPARATION 38 •Last_Update1: date of recording •Confirmed: cumulative number of confirmed positive COVID-19 cases •Deaths: cumulative number of deaths caused by COVID-19 •Recovered: cumulative number of COVID-19 recoveries •Active: cumulative number of active COVID-19 cases •Daily_Confirmed: daily number of confirmed positive COVID-19 cases •Daily_Deaths: daily number of deaths caused by COVID-19 •Daily_Recovered: daily number of COVID-19 recoveries •Daily_Active: daily number of active COVID-19 cases •IP: danger index •R0: transmission index •Daily_ConfirmedMM: moving average of the confirmed positive COVID19 cases •Daily_MuertosMM: moving average of the deaths cause by COVID-19 •Daily_RecuperadosMM: moving average of the COVID-19 recoveries •Daily_ActivosMM: moving average of the active COVID-19 cases •IPMM: moving average of the danger index •R0MM: moving average of the transmission index Meanwhile, the dataset pertaining to the United States contained additional columns: •Daily_Tested: daily number of people who took the COVID-19 test •Daily_Hospitalized: daily number of people admitted to the hospital due to COVID-19 •People_Tested: cumulative number of people who took the COVID-19 test CHAPTER 4. DATA PREPARATION 39 •People_Hospitalized: cumulative number of people admitted to the hospital due to COVID-19 4.2.3 Final Dataset Upon studying the previous dataset, the following issues were encountered: •The dataset contained columns deemed necessary for the dashboard implementation (i.e. Lat, Long). •The files containing the dataset has a total size equal to 50MB. This was a potential problem with regards to the limit in the size of the application to be uploaded in the server. •Some of the data contain names of countries that do not match with those used in the dashboard, particularly its map functionality. •The INC indicator, which was a variable of interest, was not included. •The calculation of the moving average was done by region, this was a problem as the dashboard being implemented was organized by country. The team had created an R script named dataTransform.R to solve these issues. This script parses the data from both dataset files and performs the necessary transformations (i.e. column renaming, data merging, calculation of extra variables and smoothing variables and indicators using moving averages), creating the final dataset, datasetCODA.csv. The structure of this dataset is explained in the Chapter 6. 4.3 Dataset Quality Evaluation Creating the dataset for the dashboard involved multiple tasks that required not only time and effort, but also the required knowledge to perform such tasks and in order to redact the dataset efficiently. Not only does the dataset have to be well-defined, it also has to be reliable. One important task in this process is the evaluation of the quality of these datasets. It gives the idea of how valid the information inside a dataset can be. This can be estimated by some qualitative factors present in the data. CHAPTER 4. DATA PREPARATION 40 An important aspect about dashboards is that part of their quality and effectiveness is derived from the dataset it uses to present information. As such, it is important to evaluate at least the final dataset used. Working with this dataset, the following observations, some of which led to modifications in the project, were made: •Some countries do not have updated data. •Some values, like recordings of negative deaths, do not make sense and can be attributed to errors in recording data. •Most countries contain outlier data due to inconsistencies such as incorporating data recorded in a certain day into that of a later date. •Data from countries such as North Korea, Antarctica, Western Sahara and Turkmenistan are unavailable and therefore could not be shown in the dashboard. CHAPTER 5. PROJECT DEVELOPMENT 47 Figure 5.5: Final prototype design. Map View 3 Figure 5.6: Final prototype design. Graph View CHAPTER 5. PROJECT DEVELOPMENT 48 Improvements to Dashboard Implementation As agreed in the prototype design, the dashboard was split into two screens/tabs, one for presenting users the graphs that show the evolution of the different variables in various countries (see Figure 5.7) and another one for displaying the cumulative numbers of each country in a map (see Figure 5.8). Additionally, the graph function allows users to see the evolution of the pandemic in multiple countries or multiple variables to allow comparisons. Figure 5.7: Sprint 1 result (graph view) Figure 5.8: Sprint 1 result (map view) CHAPTER 5. PROJECT DEVELOPMENT 49 Sprint Evaluation At the end of the sprint, some of the tasks included in the backlog for this iteration were not implemented, such as the updating List View UI in the Map View. Instead, only an Information Panel UI that contained no data was displayed. A meeting was then held with the project director to provide updates on the progress of the project, as well as present the dashboard in its state after the sprint. Feedback was received from the director, which would then be taken into consideration upon beginning the next sprint. Some of these included changing the names of the countries in the map view to their English names, as they were originally presented as their respective names in their own languages, selecting more than just two countries or variables, including a legend for graphs, and presenting a summary of all data in the information panel by means of presenting, for example, the countries with the most accumulated number of positive cases. 5.4 Second Sprint Sprint Planning After conducting a meeting at the end of the first sprint, we have selected tasks to be included in the second sprint backlog as shown in Table 5.3. Also learning from our experience in the first sprint, some of the tasks have been slightly modified to accommodate the limitations presented by Shiny, such as difficulties in layering components of the dashboard UI. As previously done, the tasks have been divided into smaller ones to ensure manageability by the team members. Design As previously stated, the second sprint focuses on adjusting the implementation of the dashboard to the design limitations of Shiny and on refining the dashboard contents as much as possible. As such, the design prototypes have been refined, as seen in Figures 5.9 and 5.10. CHAPTER 5. PROJECT DEVELOPMENT 50 Story Task 1 A. Implement mouse hover function to show data at a specific point in the graph B. Implement legends in the graph C. Show the axis labels bigger D. Fix date axis scale to 1 week E. Fix date axis angle to 45 degrees F. Add controls to visualize graph (zoom in/out, pan, auto-scale) G. Relocate the panel for filters above the graph. 2 A. Set color division to percentages depending on the highest number of accumulated cases of the chosen variable B. Implement mouse hover function to show the country’s name and the value of the variable chosen C. Change the map to show all countries in English 8 A. Limit to 5 countries to choose B. Remove Single Variable/Country Mode Radio Button and set default mode to Multiple Country 9 A. Limit to 5 variables to choose B. Establish a palette of colors to be used for every different line to be drawn in the graph. 10 A. Change UI to slider for both Map and Graph Views B. Put controls to modify both the start and end date in the Graph View 11 A. Investigate other graphs that could well describe some variables B. Implement a radio button UI to choose between some type of graphs C. Implement server functions to visualize selected graph 12 A. Implement a table showing 5 countries with the highest number of accumulated cases of the chosen variable (or the first variable chosen in multiple variables mode) in the Graph View B. Implement the same table in the Map View C. Implement another Information Panel UI in the Graph View that shows the total number of infected and death cases D. Implement server functions to calculate these variables E. Relocate the Information Panel and Filter Select Box UI in the Map View and place it over the map Table 5.3: Second sprint backlog CHAPTER 5. PROJECT DEVELOPMENT 51 Figure 5.9: Revised prototype design (graph view) Figure 5.10: Revised prototype design (map view) CHAPTER 5. PROJECT DEVELOPMENT 52 Improvements to Dashboard Implementation One of the biggest changes introduced in the dashboard implementation of this sprint is the option to select more than 2 countries when selecting the "multiple countries" option. Users would now be allowed to choose up to 5 countries to be displayed in the graph. This limit was placed to allow users to compare different countries without possibly overcrowding the resulting graph with various lines that may become unreadable. Another major change in the graph view of the dashboard is the inclusion of box plots. As discussed in Section 3.2, box plots are used to determine the deviation of the data and the numerical range they occupy, which users may find interesting. The final major change in the graph view of the dashboard is that the information panel from the previous sprint was replaced with two smaller information panels that present summaries about the current pandemic situation globally. For example, users could to see the total number of cases and deaths recorded worldwide. They could also find a list of the top countries in terms of number of positive COVID-19 cases. Figure 5.11: Sprint 2 result (line graph) With respect to the map view of the dashboard, the country names were all renamed to their English names. This was done by replacing the map CHAPTER 5. PROJECT DEVELOPMENT 53 Figure 5.12: Sprint 2 result (box plot) used with the one by Esri, which also changed of the color palette to shades of blue. A legend was also placed to indicate the values a certain color in the map represents. The panel for filters together with the Information Panel were placed over the map to maximize the space to be occupied by the map itself. Finally, the information panel, implemented in the 1st iteration and that contained data, is repurposed to present the countries with the highest number of COVID-19 infections. Sprint Evaluation Feedback from the project director included changing the colors from shades of blue to shades of red, as this would appear more alarming and would give the impression that there is an ongoing threat, the inclusion of digit group separators in the numbers presented in the dashboard to improve readability, and changing the labels in the information panels of the Graph View (i.e. changing "Total cases" to "Global number of cases") to prevent confusion. Additionally, it would present a bigger convenience to users for the dashboard to have a function that captures the graph being shown, be it the entire graph or a portion of it. CHAPTER 5. PROJECT DEVELOPMENT 54 Figure 5.13: Sprint 2 result (map view) CHAPTER 5. PROJECT DEVELOPMENT 55 5.5 Third Sprint Sprint Planning The sprint began by selecting the tasks to be executed, elaborated in the sprint backlog found in Table 5.4 Story Task 1 A. Process the new dataset to adapt it with the functions of the dashboard B. Add the accumulated variables in the select box UI C. Include Danger Index to the list of variables that can be shown as graphs 2A. Change the color palette of the map to indicate intensity of numbers by shades of red 5 A. Implement UI to view Country Table in the Map View B. Implement Update Country Table UI server function C. Implement map click function to enable this feature 12 A. Improve the formatting of numbers by placing digit group separators Table 5.4: Third sprint backlog Design Improving on the dashboard designs created in the previous sprint and considering the feedback received in the previous sprint evaluation, the presentation of the data and the labels found in the graph view of the dashboard were enhanced (see Figure 5.14). The colors used in the map were changed to shades of red and a panel that updates whenever the user clicks on a country in the map to see more information was included (see Figure 5.15). CHAPTER 5. PROJECT DEVELOPMENT 56 Figure 5.14: Revised prototype design (graph view) Figure 5.15: Revised prototype design (map view) CHAPTER 5. PROJECT DEVELOPMENT 63 Figure 5.20: Website design (home view) CHAPTER 5. PROJECT DEVELOPMENT 64 Improvements to Dashboard Implementation The most significant implementation in this sprint is the creation of the website in Google Sites. In the website, both the completely developed dashboard and additional information on both the project itself and the team that made the dashboard were placed. By creating and using a CSS file style.css to indicate the style adjustments and describe how HTML elements will be displayed in the web page (margins, paddings, etc.), the design of the website was made such that it allowed the dashboard to fit within the allocated space when integrated. The CSS file is included along with the source code. Another important change is that the source code was refactored, resulting in a more efficient output of data by the dashboard. The memory usage of the dashboard is also kept below 1 GB, allowing the team to continue using the free plan of Shinyapps.io. Other changes made in this iteration include the addition of the Danger Index, R0 and Inc variables in the graph view of the dashboard. The values under the Danger Index, R0, Inc columns were calculated using Equations 3.5, 3.2, 3.3 found in Chapter 3. In addition, the World Populations Prospects 2019 dataset published by the United Nations [42] was utilized to obtain the populations for each country, which were used to calculate Inc. Some minor design changes, such as the swapping of total and partial tables in the map view of the dashboard and the addition of data points in the plot to allow further visualization of the graph, were also made. CHAPTER 5. PROJECT DEVELOPMENT 65 Figure 5.21: Website implementation (home view) Figure 5.22: Website implementation(about us view) CHAPTER 5. PROJECT DEVELOPMENT 66 Figure 5.23: Website implementation (about this project view) CHAPTER 5. PROJECT DEVELOPMENT 67 Sprint Evaluation In this sprint evaluation, the web page’s design was well-received. The latest developments done in the dashboard’s implementation and its design were all accepted. Minor adjustments, like correcting grammatical errors in labels in the dashboard and changing the radio buttons to a drop-down list for the variables in the map, would have to be made. Some additional information, such as the user manual, were also required in the About This Project page of the website. These minor corrections were then made immediately and were included in the sprint backlog of this iteration. With all criteria listed in the Product Backlog fulfilled and evaluated, the development phase of the dashboard was concluded and deployed for assessment. 5.7 RStudio Libraries Used Aside from the base RStudio library, which is used whenever the RStudio IDE is launched, the following libraries were utilized in the development of the dashboard: •shiny: R framework used to build the application’s UI and server interactions •shinydashboard: used to design the structure of the panels of the application •dplyr: used for parsing, processing and creating data frames in the Rstudio IDE •leaflet: used for creating and visualizing the map •ggplot2: used for creating and visualizing the graph •rgdal: used for parsing a shapefile which is then needed to create and draw polygon layers on the map •reshape2: used for merging data frames more effectively than dplyr CHAPTER 5. PROJECT DEVELOPMENT 68 •htmltools: used for manipulating HTML elements such as the hover tool on the map •plotly: used for the additional features of the graph such as the toolbar and the hover function In summary, the dashboard contains the architecture seen in Figure 5.24. Figure 5.24: Dashboard architecture 5.8 Scrum Sprint Schedule Figure 5.25 presents the dates in which each sprint was executed. The duration of each sprint was approximately two to three weeks. Although the first sprint, that lasted from January 11, 2021 to February 19, 2021, seemed extensive, the effective development time on the project was 2 weeks, as this period coincided with the completion of numerous academic requirements. CHAPTER 5. PROJECT DEVELOPMENT 69 Figure 5.25: Realized sprint dates Chapter 6 Results In this chapter, the results of the development of the dashboard and the corresponding web page are presented. To properly interpret these results, some assumptions must be made. One assumption is that the data provided by each country is updated almost daily. Another assumption is that the data truthfully reflects the situation of the pandemic for each country. The final assumption is that the values recorded for each day do not contain errors that drastically affect previous data. However, in some periods of time, some countries have provided data that violate these assumptions, as they were published in such manner. In the first section, the final dataset is explained, along with its structure. The second section presents the completed dashboard and website, along with a short guide that explains their usage. To further demonstrate the use of the dashboard and the website, the third section presents an example analysis that compares the situation of the pandemic in Spain and in the Philippines. The final section of this chapter provides a guide on integrating the dashboard to other websites. 6.1 Final Dataset The final dataset created for the use of the dashboard was also made available in a public Google Drive folder, found at: •https://tinyurl.com/CODAdataset. This dataset contains the following columns, each of which may be used for further analyses: 70 CHAPTER 6. RESULTS 71 •COUNTRY: country/region in which the data was recorded •DATE: date of recording •DAILY_CONFIRMED: daily number of confirmed positive COVID-19 cases •DAILY_DEATHS: daily number of deaths caused by COVID-19 •DAILY_RECOVERED: daily number of COVID-19 recoveries •ACTIVE: cumulative number of active COVID-19 cases •CUMULATIVE_CONFIRMED: cumulative number of confirmed positive COVID19 cases •CUMULATIVE_DEATHS: cumulative number of deaths caused by COVID19 •CUMULATIVE_RECOVERED: cumulative number of COVID-19 recoveries •DANGER_INDEX: danger index as of the date of recording •R0: transmission index as of the date of recording •INC: cumulative incidence per 100,000 habitants as of the date of recording •SMOOTH_*: smoothed variable/indicator using moving average Additionally, the Inc values of Spain in this dataset (see Figure 6.1) was compared to the graph created using the official published data of the Spanish Ministry of Health [43] (see Figure 6.2) to verify the accuracy of the calculations done to obtain the indicator. The two sets of data cover the same range of dates, from July 2020 to May 2021. As a result, the values found in the dashboard are slightly higher than those found in the official data, which may be attributed to slightly different values used for the population of Spain. Nonetheless, the pattern created by the graph in the dashboard matches that in the official graph, meaning that the calculations were successful. CHAPTER 6. RESULTS 72 Figure 6.1: Inc of Spain from July 2020 to May 2021, as shown in Dashboard Figure 6.2: Inc of Spain from July 2020 to May 2021 6.2 Dashboard and Website The website created throughout the duration of this project (see Figure 6.3) was published online. The following links may be accessed to find the dashboard and website, as well as the source code of the project: CHAPTER 6. RESULTS 79 Figure 6.10: Map comparing Spain and the Philippines under Inc on June 8, 2020 6.4 Integrating the Dashboard to Other Websites As the developed dashboard, being deployed in Shinyapps.io, is cloud-based, users can integrate the dashboard into their own websites by simply adding it as an HTML element using the following code: <iframe height="720" width="100%" frameborder="no" src="https://covid-2019-dashboard.shinyapps.io/dashboard" /> The advantage to this is that, using the height and width attributes, users may also change the dimensions of the dashboard to help it fit in any website layout. Chapter 7 Individual Contributions Throughout the entire development of the project, each member has contributed in every phase, be it through research, through implementation, or through verification. For full transparency, each team member has expounded his own contributions in this chapter. 7.1 Contributions made by Adrián García Sánchez After the establishment of the main theme of the project, the objectives and motivation, I was in charge of establishing the work plan together with the structure of the document, which has allowed us during the whole project to be able to be organized and focused on our objectives, thus avoiding some risks related to the non-achievement of the objectives or to the disorganization and waste of time and effort. In the research phase I focused on the technical requirements for the creation of a dashboard, such as the different layers that every functional dashboard requires. These are the backend, which is the part in charge of making all the logic of a page work, the frontend, which is the part of the software that interacts with the users, and of course the data from which the dashboard is fed. Together with Harold I was in responsible of researching about the existing databases on the internet related to this topic and the most useful types of graphics for this project, such as line graphs, maps or box plots among others. All this allowed us to get an idea of the main points of a good dashboard and those things we should avoid in our project. In the creation of the dataset, I helped in the evaluation of the datasets, 80 CHAPTER 7. INDIVIDUAL CONTRIBUTIONS 81 to find possible risks in their creation or related to duplicates or a bad use of space, since shiny has a 1GB limit for its free plan, which is the one we use. I did this by examining the contents of the dataset thoroughly using R and RStudio. After the research phase, the three of us created the planning to follow, both the tools to use (R studio, Shiny, Google Sites) and the methodology to use (Scrum) and also the duration of each iteration and the division of tasks. I was in charge of distributing and planning each of the iterations, the product backlog that includes the assignment of priorities of the tasks, based on the initial planning and on the problems that arose in the development that made us adapt the planning from time to time. We chose Scrum over other agile methodologies to have this flexibility when changing the planning. I also did the dashboard design for each iteration, based on the limitations encountered in the development and above all to make an intuitive and simple tool for all the public. In the development phase, I helped in implementing the map by studying the leaflet library and the layers that can be included with the map such as the legends and the polygons and shapes the define each country. I also performed testing and debugging tasks to find the bugs and things to improve in the source code. After each of the iterations we met with the director and established the conclusions and prepared the basis for the next iteration. Regarding the website I took care of the creation and the design and organization of the pages, taking into account the many limitations that google sites has, but enough for our purpose. The site was organized focusing on the dashboard, which is the most important piece and occupies most of the main page. We added several extra pages explaining various aspects of the work, such as its user manual or data repositories. We also found ways to contact the members of the group. I also pointed out the possible modifications and adjustments concerning the aesthetics such as the font sizes, the positioning of the panels, the colors, the margins, etc. This is to improve the visualization of the dashboard in the web page. Finally, we established together the conclusions, where we commented all the knowledge acquired during the elaboration of the project, both at the level of tools and data analysis or methodology. Then we defined some future work, where we establish possible lines of action for the continuation of the project. I was also in charge of the modification of the report, in latex and through the Overleaf platform, adjusting the format and fixing the errors. CHAPTER 7. INDIVIDUAL CONTRIBUTIONS 82 7.2 Contributions made by Dale Francis Valencia Calicdan At the start of the project, first thing we did was specify the main topic of this project. We defined the subject this application was about which was the coronavirus, its definition, origin, and the importance of studying its evolution. With that, I defined the objectives this whole project will encircle into and the main goals the team should achieve by the end of this project’s duration. As we go on into the investigation phase of the project, I was in charge of studying the different variables involved in studying the evolution of the coronavirus disease. I spent time reading articles and looking through the various dashboards my colleagues were investigating to identify the common variables and others that were significant. I evaluated them by which I chose the most important ones which could be of interest in developing the dashboard. Upon the end of the investigation phase, we moved on into the development phase. In this phase, we first laid out the plan the team should follow, the methodology to use, which was Scrum methodology, the duration of each iteration, and the partition of tasks. In each iteration I was primarily focused in building the source code of the program in R language, while strictly following the sprint backlog and the design established for every iteration. This also included in researching and studying various libraries which could be of use in developing the dashboard. By the end of every iteration, I also reported the various limitations I have encountered. This led into suggestions on how to improve the dashboard for the next iteration. By the last iteration, I uploaded the dashboard into the dedicated server which included signing up an account, uploading the source code and resources, and finally, deploying the application. At the begginning of this phase, we worked on an initial database provided by the directors of the project, which is a small dataset containing recordings of coronavirus cases of some countries. By the 3rd iteration, the first version of the proposed dataset, created by the other group, was sent to us. The format of this dataset (number of columns, column names, variables available, etc.) was different from the one we previously worked on. I restructured the code and adapted it to the format the proposed dataset used. Later on it was seen that the data in the dataset provided by the partner CHAPTER 7. INDIVIDUAL CONTRIBUTIONS 83 group was organized by regions while the dashboard is structured to show data by country. We also learned that calculations done inside the source code takes a lot of memory. With these findings, the team decided to create another dataset by transforming the ones we were provided. I created the script which performs the necessary transformation of the dataset, as well as the calculations of some variables, to avoid unnecessary operations while the application is deployed in the server. By the end of the development phase, my colleagues were implementing the web page for the dashboard. After discussing some modifications and possible adjustments for the HTML elements of the dashboard, for it to fit in the allocated window in the web page, I created a CSS file and adjusted the margins and paddings of the dashboard as well as some of its aesthetics to further improve its accessibility and usability. After the development phase, I contributed in redacting the documentation of this project, assisting in the chapters I was primarily focused at. I explained the final dataset that was one of the results of this project, and proofread the entire part of Chapter 5 explaining the project’s development. CHAPTER 7. INDIVIDUAL CONTRIBUTIONS 84 7.3 Contributions made by Harold Luis Pascua Cajucom I started by spearheading the research on the different types of dashboards available, as well as the examples for each one. I initially found that most dashboards are used in businesses and that the classifications in that context do not work with the context of monitoring COVID-19 and this led me to analyze the existing dashboards based on their common factors: geographic scope and type of data used. After that, I researched on the types of software tools available, especially for creating and configuring dashboards. I also contributed to the research of data of interest, namely the existent databases of interest and the graphs of interest. These were databases and graphs that may be put into consideration for inclusion in the dashboard. In addition, I also researched on some variables of interest: R0, DI, and Inc. As for the data preparation stage of the project, I focused on evaluating the datasets, checking for points that would become problematic or concerning when integrated with the dashboard to display data. For the initial planning of the implementation of the project, I contributed in deciding the different specifications. For example, we decided to apply the Scrum methodology, as we had some experience with this methodology from our experience in learning Software Engineering. We also decided at this point to implement the dashboard using the RStudio IDE, together with Shiny and the other RStudio libraries used. Finally, we decided to embed the dashboard onto a Google Sites website, as we wanted a website that could easily be configured so we could focus more on the development of the dashboard. Also in the planning stage, we decided on what type of COVID-19 dashboard we wanted the final product to be. Based on the information we received from the research on the different types of dashboards, we decided to mainly focus on epidemiological data with a global scope, as we wanted the dashboard to be reachable to as many people as possible. Throughout the development phase, I contributed to all development sprints by testing and debugging the dashboard and by formulating the conclusions for each sprint. It was through this debug process that we were able to detect the anomalies found during development that would have to be resolved by the following sprints. I also contributed in developing the dashboard by implementing the graphs, CHAPTER 7. INDIVIDUAL CONTRIBUTIONS 85 especially the box plots that was allowed further analysis of variables. By the end of this phase, I managed to refactor the source code to improve its comprehensibility. This includes defining the auxiliary functions found in the functions.Rscript, such as the creation of tables and fetching of information from the dataset. In the presentation of results, I prepared the repository that contained the datasets used for the dashboard, making those datasets available for public use. Additionally, I created the user manual that would help orientate the users on the different available functions of the dashboard and facilitated the example analysis of the situations of Spain and the Philippines. This would help facilitate the users in knowing how to use the dashboard to get the results they need and properly interpreting those results to get insights. Together with my colleagues, we formulated the conclusions made postdevelopment. We mainly focused on the things we learned from the different fields of dashboard development, which includes preparing data, adapting to using different libraries and technologies, and data analysis. We also created the recommendations for future work, some of which were actions that we initially wanted to take but were unable to due to time constraints. Finally, I was in charge of tasks related to the documentation of the project. This included the compilation of sources cited in this work into the bibliography and proofreading the work in general, making sure that it was free of any grammatical errors and that every detail about the project was well-explained. Chapter 8 Conclusions and Future Work 8.1 Conclusions A dashboard was created that can inform users about the progress of the pandemic in graphic and map forms, especially under indicators such as transmission index, danger index, and cumulative incidence. Throughout the project, the team has used R and Shiny, as well as libraries such as Plotly and Leaflet to develop the dashboard. Balsamiq Wireframes was also used to create wireframes and prototypes for the design of both the dashboard and the website. Additionally, LaTeX was learned for the creation of this work. Applying the Scrum methodology in this project was also an important practice learned. As Scrum has become a standard in most companies, learning how to employ this Agile methodology in a practical setting proves to be useful in preparing for work under employment. Another crucial skill taken from this project is the application of various analytical techniques. Having multiple types of graphs present in the dashboard allowed for the understanding of different aspects of the data (e.g. its evolution over time, variance in values). Exploratory data analysis also plays a part due to the nature of the dashboard of being based on a mathematical model. As the sources of data found in the dataset mainly consist of simple recorded values such as positive cases and deaths, indicators such as the R0, DI, and Inc were mathematically derived from these records. The final conclusion is that, as important it is to analyze data thoroughly and extensively, making these data understandable and accessible to the pub86 CHAPTER 8. CONCLUSIONS AND FUTURE WORK 87 lic is equally important. The users of the dashboard will consist of people of different backgrounds and of different perspectives on the pandemic. Hence, it is essential that the same idea is objectively imparted to them by the dashboard to attain its purpose. 8.2 Future Work Despite having successfully created a fully functioning COVID-19 dashboard, the team strongly believes that the project still contains room for improvement. The first recommended action is the automation of the data acquisition process. Currently, the dashboard uses only one dataset, meaning that the data used could easily become obsolete in a matter of days if the dataset is not constantly updated. Manually updating this dataset by uploading a later version after a given time interval is a viable yet strenuous option in resolving this issue. Thus, automating this process will allow the dashboard to stay up to date without requiring a large amount of effort from the team in terms of maintenance. Another recommended action is incorporating animations into the map view of the dashboard, which could amplify its data visualization capabilities. This would allow users to see the evolution of the pandemic in different countries more clearly, providing further interaction to the application. Bibliography [1] World Health Organization. Coronavirus disease (covid-19). https:// www.who.int/emergencies/diseases/novel-coronavirus-2019. Accessed: Oct. 30, 2020. [2] World Health Organization. Origin of sars-cov-2, 26 march 2020. Technical documents, 2020. Accessed: Oct. 30, 2020. [3] Emilio de Benito. Nearly one-third of all intensive care beds in spain occupied by coronavirus patients. https://english.elpais.com/ society/2020-11-04/nearly-one-third-of-all-intensive-carebeds-in-spain-occupied-by-coronavirus-patients.html, Nov 2020. Accessed: Nov. 26, 2020. [4] Ashley Killough and Omar Jimenez. ’busting out of the seams’: West texas hospitals pushed to the limit in unprecedented covid19 surge. https://edition.cnn.com/2020/11/19/us/west-texascovid-19-hospitalizations-surge/index.html, Nov 2020. Accessed: Nov. 26, 2020. [5] Lora Jones, Daniele Palumbo, and David Brown. Coronavirus: How the pandemic has changed the world economy. https://www.bbc.com/ news/business-51706225, Jun 2020. Accessed: Nov. 26, 2020. [6] World Health Organization et al. Covid-19 weekly epidemiological update. 2020. Accessed: Nov. 26, 2020. [7] Sandra Durcevic. Types of dashboards: Strategic, operational & analytical. https://www.datapine.com/blog/strategic-operationalanalytical-tactical-dashboards/, Jul 2020. Accessed: Nov. 9, 2020. 88