Assignment 3: COVID-19 Strain Dynamics – BCB520_Data

Description of Data

The covid_ts dataset tracks the daily counts of COVID-19 infections and deaths across U.S. states, broken down by different virus strains. It includes several key columns: the date, state, total infections, deaths, and daily case counts for each COVID-19 strain— Original, Alpha, Delta, and Omicron. The date column records the day of the entry, while the state column tells you which U.S. state the data pertains to. The total_infections column gives the overall count of COVID-19 cases, and deaths shows the number of lives lost to the virus. The strain-specific columns break down the daily cases for each variant: Original, Alpha, Delta, and Omicron.

The dataset begins in early 2020, during the first wave of the pandemic, and continues through the various waves caused by the emergence of new strains. These variants, like Alpha, Delta, and Omicron, had different levels of contagiousness and impacts on public health, which is reflected in their infection and death rates. For instance, the Alpha variant, emerging in late 2020, was more easily spread than the original strain, while Delta, which appeared in 2021, caused a major global surge. Omicron, emerging toward the end of 2021, was highly transmissible but led to fewer severe cases compared to previous strains.

By analyzing this dataset, we can track how each variant’s dominance shifted over time and across regions. It provides insight into when and where each strain became the most prevalent, shedding light on regional variations in how COVID-19 spread. Additionally, the dataset allows us to explore how mortality rates changed with each variant. By comparing death counts for each strain, we can gain a better understanding of how the severity of the virus evolved. Overall, the dataset is a powerful tool for both public health policy makers and epidemiological researchers. It allows us to visualize how the virus spread geographically and how different variants impacted different regions at various times. For example, using pie charts, we can easily show the proportion of each strain in different states, offering a clear picture of how the pandemic unfolded across the U.S. over time.

# Print summary to verify strain distribution
summary_stats <- covid_ts %>%
  group_by(state) %>%
  summarise(
    total_cases = sum(total_infections, na.rm = TRUE),
    total_original = sum(Original, na.rm = TRUE),
    total_alpha = sum(Alpha, na.rm = TRUE),
    total_delta = sum(Delta, na.rm = TRUE),
    total_omicron = sum(Omicron, na.rm = TRUE)
  )
print(summary_stats)

# A tibble: 50 × 6
   state       total_cases total_original total_alpha total_delta total_omicron
   <chr>             <int>          <int>       <int>       <int>         <int>
 1 Alabama          109073          66055       24671       13639          4708
 2 Alaska           109220          65955       24546       13698          5021
 3 Arizona          108820          65488       24718       13603          5011
 4 Arkansas         109766          66186       24756       13912          4912
 5 California       109730          66432       24537       13634          5127
 6 Colorado         109579          66615       24612       13650          4702
 7 Connecticut      110075          66941       24446       13823          4865
 8 Delaware         109294          66325       24951       13421          4597
 9 Florida          109710          66115       24964       13752          4879
10 Georgia          109771          65969       25106       13906          4790
# ℹ 40 more rows