library(usmap)
library(ggplot2)
library(tidyverse)
## ── Attaching packages ───────────────────────── tidyverse 1.3.0 ──
## ✓ tibble  2.1.3     ✓ dplyr   0.8.4
## ✓ tidyr   1.0.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.4.0
## ✓ purrr   0.3.4
## ── Conflicts ──────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

Hypothesis

A simple way to know the difference between an epidemic and a pandemic is to remember the “P” in a pandemic, which means a pandemic has a passport. A pandemic is an epidemic that travels. Not all infectious disease terms are created equal, though often they’re mistakenly used interchangeably. The distinction between the words “pandemic,” “epidemic,” and “endemic” is regularly blurred, even by medical experts. This is because the definition of each term is fluid and changes as diseases become more or less prevalent over time. I will be going a step farther to elaborate on how communicable diseases spread in the populous community and as compared to the less dense communities using Tableau and Rstudio to visualized in the United States of America using Covid 19 asa test case.

Below is a graphical representation of affected cities, counties, and states, the denser and the most affected. The States along the east coast is covered in Red which shows how the pandemic has affected people.Looking at at the visualization, places around the middle-west where it is less populous clearly justifies the hypothesis.

states <- read.csv("~/documents/R/covid-19/us-states.csv")

states %>% 
filter(state %in% "New York") %>% 
  arrange(desc(cases)) %>%
  ggplot(aes(date, cases,)) + geom_col() + coord_flip()

states %>% 
filter(state %in% "New Jersey") %>% 
  arrange(desc(cases)) %>%
  ggplot(aes(date, cases,)) + geom_col() + coord_flip()

Above is a visualization of two selected states that have been affected extremely with the Pandemic periodically. So the next is to dive into the populous counties and compare it to the less populous counties.

counties <- read_csv("~/documents/R/covid-19/us-counties.csv")
## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   county = col_character(),
##   state = col_character(),
##   fips = col_character(),
##   cases = col_double(),
##   deaths = col_double()
## )
counties %>% 
filter(county %in% "Westchester") %>% 
  arrange(desc(cases)) %>% 
  ggplot(aes(date, cases, fill = -cases)) + geom_col() + coord_flip()

Westchester county which houses about 967,506 (2019) according to the US population results in 2019 has been the worst place to be hit with the Pandemic with a total of about 36,199 covid19 cases of and still counting representing approximately 27%.

On the other hand, Orange County in New York has also been hit with the pandemic with 36,071 of its residents out of 384,940 residents living there as shown below

counties %>% 
  filter(county %in% "Orange") %>% 
  arrange(desc(cases)) %>% 
  ggplot(aes(date, cases, fill = -cases)) + geom_col() + coord_flip()

Comparing all to Sibley County in the state of Minnesota with a total population of 14,912 which has currently recorded less than 5 COVID 19 cases.

counties %>% 
  filter(county %in% "Sibley") %>% 
  arrange(desc(cases)) %>% 
  ggplot(aes(date, cases, fill = -cases)) + geom_col() + coord_flip()

In the same scenario, a county like Carter County in Oklahoma with a population of 48,111 has about 15 cases of COVID 19 reported.

counties %>% 
  filter(county %in% "Carter") %>% 
  arrange(desc(cases)) %>% 
  ggplot(aes(date, cases, fill = -cases)) + geom_col() + coord_flip()

Conclusion

The purpose of the case-study simulation was to prove how pandemics spread through a populous community using data visualization. Having compared all these facts together, we can strongly confirm that the most densely populated community is high to be affected when it comes to disease outbreak or Pandemics. All these facts are based on data as visualized in the analysis above.