Visualizing the Impact of COVID-19 and the Vaccination Data in 2021

COVID-19 has been a hot topic in recent years. While numerous visualizations have showcased the distribution of COVID-19 cases and deaths, few demonstrate the temporal relationships between cases, deaths, and COVID-19 vaccinations. Our visualization aims to fill this gap by showcasing the temporal evolution of COVID-19 cases, deaths, and vaccinations in the U.S. while also comparing them geographically by U.S. states.

Our dataset was obtained from two different organizations: the New York Times and Our World in Data. The New York Times dataset focuses on COVID-19 cases and deaths within each county/state of the US in 2021, while the dataset from Our World in Data contains information on the various vaccination data of each state throughout the year. We chose to focus on 2021 since that is when the first data was collected for the us_state_vaccinations.csv, in addition to the reason that the majority of vaccination data from 2022 are not as consistent and missing a lot.

Our visualizations are targeted towards individuals who want to learn more about the timeline of COVID-19 cases, deaths, and vaccinations data and the complex relationships among them. This includes public health officials who need to make informed decisions regarding interventions to mitigate the spread of COVID-19, journalists and media organizations who want to report accurate information about the pandemic to the public, and the general public who are interested in understanding the impact of COVID-19 on their local communities.

We implemented our visualizations using Python's Altair and Streamlit libraries, using drop-down selection bars, time/date sliders, multi-select widgets, and various types of linked views. These interactive features are implemented using built-in functions from the Streamlit and Altair libraries, including st.selectbox, st.multiselect, st.slider, alt.selection_interval, and alt.selection_single. The details of the code that we wrote to implement these visualizations can be found on our project's GitHub page (https://github.com/Tony-Xiayi-Ding/COVID-19-Visualizations).

Our visualizations showed that the temporal evolution of COVID-19 cases and deaths exhibited a striking similarity, with a rather consistent trend over time. Additionally, the cases and deaths count generally remained at much slower increasing rates during seasons with higher temperatures and at much higher increasing rates during colder months, highlighting the complex interplay of demographic and seasonal factors in shaping the pandemic in the U.S. Moreover, the overall trend for case fatality rate was decreasing for most states, and states that were close to each other shared similar trends of case fatality rate. Furthermore, states with higher average temperatures shared similar trends in case fatality rates that were quite different from those states that were relatively colder. Lastly, as total vaccinations per hundred increased over time, the relative case fatality rate dropped, and coastal states were found to have slightly higher total vaccinations per hundred values, potentially due to their higher population densities and that the residents in those states are more aware of the importance of getting vaccinated due to their elevated chances of contracting COVID-19.

References:

1. New York Times. (2022). Covid-19-data/US-counties-2021.csv. GitHub. Retrieved February 12, 2023, from https://github.com/nytimes/covid-19-data/blob/master/us-counties-2021.csv

2. Our World in Data. (2023). Covid-19-data/US_state_vaccinations.CSV. GitHub. Retrieved February 12, 2023, from https://github.com/owid/covid-19-data/blob/master/public/data/vaccinations/us_state_vaccinations.csv

3. U.S. Department of Health and Human Services. (2023). What is a FIPS code and why do I need one? National Institutes of Health. Retrieved February 12, 2023, from https://nitaac.nih.gov/resources/frequently-asked-questions/what-fips-code-and-why-do-i-need-one