2b:null 2c:["$","title",null,{"children":"Unmasking the Pandemic: An Analytical View of COVID-19 Across USA | Northeastern University John A. Guerra Gomez classes | Observable"}] 31:T43a,## Introduction The COVID-19 pandemic has left an indelible mark on the world, reshaping societies, economies, and healthcare systems. In this project, I explored the evolution of COVID-19 across the United States from May 2020 to May 2021 through a series of carefully crafted data visualizations. Using line charts, heatmaps, scatterplots, and geographic maps, I analyzed key metrics such as cases, deaths, recoveries, and their percentages relative to the population across states. Each visualization offers a unique perspective on the pandemic’s trajectory, enabling the identification of patterns, anomalies, and regional differences. This project not only provides a visual narrative of COVID-19's spread but also demonstrates the power of data visualization in uncovering trends, facilitating comparisons, and telling compelling stories. By combining statistical analysis with interactive visuals, this work aims to shed light on the complex and dynamic nature of the pandemic in the USA, offering a deeper understanding of its impact at both state and national levels.32:T557,## Insight from the Scatterplot
Pandemic Paradox: Michigan and Oklahoma’s Diverging Paths in April 2021 - As we know the number of cases per month peaked during the months of November, December 2020 and January 2021, from the line charts we saw a "Flattening of the curve". Upon checking the statistics for following months all seem to be normal with slight variations in all the months except April 2021. In the month of April in 2021, the Percentage of deaths and cases were very similar among all states (with less than 0.15% of the population dying and 1% of the population catching cases). However, there are two major outliers - Michigan and Oklahoma. Michigan while having one of the lowest percentage of its people being affected - 0.2% - puzzlingly has the highest percentage of deaths in the same month - 0.046% - triple that of any other state. On the other hand, Oklahoma is the polar opposite, Having a lower number of deaths - 0.015% (a third that of Michigan) - with a large percentage of its population being affected - 1.83%. The Detroit Free Press wrote that the daily cases were decreasing, however the new variant is also affecting the younger population. This could have been a big reason for the increase in deaths in April 2021 since the older population were the first to be vaccinated, leaving the younger population vulnerable.33:[] 2f:{"id":"b2e3046ffacbbc56","slug":null,"title":"Unmasking the Pandemic: An Analytical View of COVID-19 Across USA","thumbnail":"5d898a28e8a45c4afffa028c3c3854d6531724b321fb16b293f9dced30e0ff93","access_level":"public","create_time":"$D2024-12-08T20:10:42.520Z","update_time":"$D2024-12-09T01:41:26.218Z","view_time":null,"first_public_time":"$D2024-12-09T00:52:19.001Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"afbaa6d30585fd36","login":"nujguerra","name":"Northeastern University John A. 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Guerra Gomez classes","avatar_url":"https://avatars-next.observableusercontent.com/04e5ef72b3edcda1296894b5d1ecaa9c92ef5d52d69e65e4c115de5534928fe9","tier":"education_2026","role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Unmasking the Pandemic: An Analytical View of COVID-19 Across USA","cells":[{"id":0,"value":"# Unmasking the Pandemic: An Analytical View of COVID-19 Across USA","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":39,"value":"This is a data visualization project done by [Jonathan Tarun Rajasekaran](https://observablehq.com/d/8586f881717414c5) from Northeastern University, Oakland for the course [CS7295 - Special Topics in Data Visualization](https://johnguerra.co/classes/data_visualization_fall_2024/) under Professor [John Alexis Guerra Gomez](https://johnguerra.co/) in the Fall 2024 Semester.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":149,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":42,"value":"## COVID - 19 Evolution over time\n\nInitially I created a simple Line Chart to visualize the evolution of COVID-19 in the USA over time. This line chart elegantly depicts the progression of Covid-19 across the USA from May 2020 to May 2021. Each state is represented by a distinct, color-coded line, with data points corresponding to monthly statistics for various metrics. These metrics include total and monthly cases, deaths, recoveries, and their corresponding percentages, allowing users to explore the data from different perspectives. By enabling the comparison of these statistics across states, the chart facilitates the identification of trends, patterns, and potential outliers, offering valuable insights into how the pandemic unfolded in different regions. This visualization serves as a powerful tool for understanding the dynamics of Covid-19’s spread, providing both a broad overview and the ability to drill down into specific state-level experiences. Here is what I found interesting. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":35,"value":"viewof unitsy","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"viewof selectedStates","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"linechart","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":46,"value":"## Insight from the Line Chart\n
\nA Delayed Response: North Dakota’s Role in November’s COVID-19 Spike:\nA lot of states started getting a huge surge in cases sometime around the month of November 2020, however North Dakota was affected the most, with 4.47% of its people being affected in just the month of November in 2020, while all other states had lower than 3.75% of their population affected in the same month. North Dakota was likely affected so bad since they did not have any big surges during the months of June and July in 2020 (Having only 0.12% and 0.37% of its population being affected, a number that seemed very small owing to its smaller population), this happened since the state did not take effective mitigation measures early on and only made masks mandatory IN the month of November, as mentioned in this [article.](https://www.mprnews.org/story/2020/11/14/north-dakota-mandates-masks-capacity-limits-as-virus-surges) \n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":49,"value":"## Noticing Patterns and finding Anomalies\n\nHowever due to the sheer number of states in the USA, I could not successfully look at any patterns and notice differences since a lot of the lines overlapped on the chart. So, I went ahead and made a heatmap - making it much easier to look at each individual state easily. This heatmap represents Covid-19 statistics for each state in the USA across May 2020 to May 2021. This idiom is much clearer for comparison of different states, since the datapoints do not over lap like they do on line charts. The color saturation is used to represent the metric chosen, with lower saturation being a lower value and higher saturation being a higher value - as shown on the scale. The heatmap allows us to identify easily which states were affected differently that others a little better than the line graph.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":55,"value":"viewof unitsy2","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":60,"value":"viewof selectedStates2","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"heatmap","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":80,"value":"## Insight from the Heatmap\n
\nWhy Hawaii and the Caribbean Were Spared While New York Suffered:\nThe Caribbean islands as well as the island of Hawaii seem to be the least affected - all of them had lower than 3% of their population affected while the other states were between 7% and 15% - owing to their secludedness, however among the other states the trends in the cases seem to be very similar, but the states of New Jersey and New York lost a lot more of their population compared to other states much earlier - losing about 0.16% of their population by June 2020 whereas the other states had only lost less than 0.1% of theirs. This is very likely due to their insanely high population density, making social distancing near impossible. It could also be due to New York being a travel hub.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":87,"value":"## A weird tradeoff between cases and deaths\n\nWhile these two visualizations helped me identify patterns and anomalies, they don't allow for viewing patterns between cases and deaths due to COVID-19 simultaneously. To do so, I created a Brushable Scatteplot - allowing us to select any portion of data on it, which is separately displayed as a bar chart for a closer look.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":95,"value":"viewof selectedMonth","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":100,"value":"viewof selectedStat2","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":112,"value":"viewof selectedStates3","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":108,"value":"idiom34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":120,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":125,"value":"## Geographic View\n\nLast but not least, I wanted to see how different regions inside the USA were affected, so I decided to create a geographic visualization of the same data. This is what I found.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":129,"value":"map","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":131,"value":"## Insight from the Map\n
\nEastern vs. Western U.S. in December 2020: A Tale of Two COVID-19 Experiences - \nWe know that December 2020 was one of the worst and harshest months of COVID-19. Looking at the statistics on the map. Most parts were affected significantly, with Tennessee and California being the highest - 3% and 2.79% of their population being affected respectively. The Eastern part was all quite similarly affected, however the few states in the North-West were least affected in this harsh month - Washington, Oregon and Montana - all under 1.7% of their population being affected.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":135,"value":"## Conclusion\n\nThrough this project I learned a lot about visualization, what are the do's and don'ts, and more about how get feedback and improve on my work through the usability study. These visualizations that I made allowed me to successfully explore and analyze the evolution of COVID-19 in the USA. I have listed links to my documents and videos below, do take a look for a more thorough explanation of my project!","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":145,"value":"## Links\n- [Usability Study Report](https://docs.google.com/document/d/1VySMiJ28zECK7ZZQZGcCXIB1yAW2GkQfjJe9YqXw-KM/edit?usp=sharing)\n- [Tweet](https://x.com/JonathanTa84074/status/1865934637278900414)\n- [Technical Video Explaining the Project](https://youtu.be/akolcbViIoY)\n- [General Video](https://youtu.be/sG9EwXABOeU)\n- [Collection of Notebooks - Detailed Walkthrough of each Visualization](https://observablehq.com/collection/@nujguerra/covid-19-report-usa)\n- [Dataset](https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports_us)\n- [Presentation Slides](https://docs.google.com/presentation/d/1FgdEt3QpE0YW3PRZJzKK5YgmNGW6mvHneU5KBedW40k/edit?usp=sharing)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":10,"value":"import {linechart} from \"cc59ff211770a517\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"import {viewof selectedStates} from \"cc59ff211770a517\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"import {viewof unitsy} from 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