COVID-19 Analysis Project

This project examines various aspects of the COVID-19 pandemic through data analysis and visualization. Our goal is to understand the spread patterns, mortality rates, and vaccination progress.

Case Analysis

The following analysis examines COVID-19 case data, tracking cumulative cases, daily new cases, and analyzing the correlation between cases and deaths.

Jupyter Notebook

covid_analysis

In [1]:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

In [2]:

# Set the aesthetic style of the plots
sns.set_style('whitegrid')
plt.rcParams['figure.figsize'] = (12, 6)

In [3]:

# Sample COVID-19 data (simplified)
dates = pd.date_range(start='2022-01-01', periods=90)
cases = np.cumsum(np.random.poisson(lam=[30] * 30 + [20] * 30 + [10] * 30))
deaths = np.cumsum(np.random.poisson(lam=[2] * 30 + [1.5] * 30 + [0.8] * 30))
recovered = np.cumsum(np.random.poisson(lam=[10] * 20 + [25] * 40 + [35] * 30))

In [4]:

# Create a DataFrame
covid_data = pd.DataFrame({
    'Date': dates,
    'Cases': cases,
    'Deaths': deaths,
    'Recovered': recovered
})

In [5]:

# Display the first few rows
print("COVID-19 Data Sample:")
display(covid_data.head())

COVID-19 Data Sample:

	Date	Cases	Deaths	Recovered
0	2022-01-01	27	1	11
1	2022-01-02	55	4	23
2	2022-01-03	85	7	37
3	2022-01-04	117	9	44
4	2022-01-05	142	9	55

In [6]:

# Plot cumulative cases
plt.figure(figsize=(12, 6))
plt.plot(covid_data['Date'], covid_data['Cases'], color='#FF9999', linewidth=3, label='Cumulative Cases')
plt.title('Cumulative COVID-19 Cases', fontsize=16)
plt.xlabel('Date', fontsize=12)
plt.ylabel('Number of Cases', fontsize=12)
plt.grid(True, alpha=0.3)
plt.legend()
plt.tight_layout()
plt.savefig('covid_cases.png')
plt.show()

No description has been provided for this image

In [7]:

# Plot daily new cases
daily_cases = covid_data['Cases'].diff().fillna(covid_data['Cases'].iloc[0])
plt.figure(figsize=(12, 6))
plt.bar(covid_data['Date'], daily_cases, color='#FF6666', alpha=0.7, label='Daily New Cases')
plt.title('Daily New COVID-19 Cases', fontsize=16)
plt.xlabel('Date', fontsize=12)
plt.ylabel('Number of New Cases', fontsize=12)
plt.grid(True, alpha=0.3)
plt.legend()
plt.tight_layout()
plt.savefig('covid_daily_cases.png')
plt.show()

In [8]:

# Analyze correlation between cases and deaths
plt.figure(figsize=(8, 8))
sns.scatterplot(x='Cases', y='Deaths', data=covid_data, s=100, alpha=0.7)
plt.title('Correlation between Cases and Deaths', fontsize=16)
plt.xlabel('Cumulative Cases', fontsize=12)
plt.ylabel('Cumulative Deaths', fontsize=12)
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('covid_correlation.png')
plt.show()

In [9]:

# Basic R0 calculation (simplified model)
import numpy as np
# Assume serial interval of 5 days
serial_interval = 5
# Calculate daily growth rate (simplified)
growth_rate = np.mean([daily_cases[i+serial_interval]/max(daily_cases[i], 1) 
                       for i in range(len(daily_cases)-serial_interval)])
# Basic reproduction number (simplified)
r0 = growth_rate * serial_interval
print(f"Estimated basic reproduction number (R0): {r0:.2f}")

Estimated basic reproduction number (R0): 5.23

In [10]:

# Summary statistics
summary = covid_data[['Cases', 'Deaths', 'Recovered']].describe()
display(summary)

	Cases	Deaths	Recovered
count	90.000000	90.000000	90.000000
mean	1083.366667	77.622222	931.100000
std	521.758122	41.954116	696.491078
min	27.000000	1.000000	11.000000
25%	680.750000	45.250000	263.000000
50%	1181.000000	82.500000	845.500000
75%	1547.000000	115.750000	1493.000000
max	1763.000000	137.000000	2279.000000

In [11]:

# Calculate and plot Case Fatality Rate over time
covid_data['CFR'] = (covid_data['Deaths'] / covid_data['Cases']) * 100
plt.figure(figsize=(12, 6))
plt.plot(covid_data['Date'], covid_data['CFR'], color='#6666FF', linewidth=3)
plt.title('COVID-19 Case Fatality Rate Over Time', fontsize=16)
plt.xlabel('Date', fontsize=12)
plt.ylabel('Case Fatality Rate (%)', fontsize=12)
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('covid_cfr.png')
plt.show()

In [ ]:

Vaccination Progress

Next, we analyze vaccination progress, examining daily vaccination rates, cumulative totals, and weekly patterns that emerged during the vaccination campaign.

R Markdown

COVID-19 Vaccination Analysis

Data Analyst

2023-06-01

COVID-19 Vaccination Progress

In this analysis, we’ll examine simulated COVID-19 vaccination data to understand patterns and progress.

# Generate sample data
set.seed(123)
dates <- seq(as.Date("2022-01-01"), as.Date("2022-03-31"), by = "day")
n_days <- length(dates)

# Simulate daily vaccination data
vaccination_data <- data.frame(
  Date = dates,
  DailyVaccinations = c(
    rpois(30, lambda = 1000),
    rpois(30, lambda = 2000),
    rpois(n_days - 60, lambda = 1500)
  )
)

# Calculate cumulative vaccinations
vaccination_data <- vaccination_data %>%
  mutate(CumulativeVaccinations = cumsum(DailyVaccinations))

# View the first few rows
head(vaccination_data)

##         Date DailyVaccinations CumulativeVaccinations
## 1 2022-01-01               982                    982
## 2 2022-01-02              1037                   2019
## 3 2022-01-03               946                   2965
## 4 2022-01-04              1004                   3969
## 5 2022-01-05              1054                   5023
## 6 2022-01-06              1014                   6037

Vaccination Trends

Let’s visualize the daily vaccination numbers:

ggplot(vaccination_data, aes(x = Date, y = DailyVaccinations)) +
  geom_bar(stat = "identity", fill = "#4CAF50", alpha = 0.7) +
  geom_smooth(method = "loess", color = "#2196F3", se = FALSE) +
  labs(
    title = "Daily COVID-19 Vaccinations",
    x = "Date",
    y = "Number of Vaccinations"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16, face = "bold"),
    axis.title = element_text(size = 12),
    axis.text = element_text(size = 10)
  )

Now, let’s look at the cumulative vaccination progress:

ggplot(vaccination_data, aes(x = Date, y = CumulativeVaccinations)) +
  geom_line(color = "#E91E63", size = 1.5) +
  labs(
    title = "Cumulative COVID-19 Vaccinations",
    x = "Date",
    y = "Total Vaccinations"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16, face = "bold"),
    axis.title = element_text(size = 12),
    axis.text = element_text(size = 10)
  )

Vaccination Rate Analysis

Let’s calculate and visualize the 7-day rolling average of daily vaccinations:

vaccination_data <- vaccination_data %>%
  mutate(
    RollingAverage = zoo::rollmean(DailyVaccinations, k = 7, fill = NA)
  )

ggplot(vaccination_data, aes(x = Date)) +
  geom_bar(aes(y = DailyVaccinations), stat = "identity", 
           fill = "#4CAF50", alpha = 0.4) +
  geom_line(aes(y = RollingAverage), color = "#FF5722", size = 1.5) +
  labs(
    title = "Daily Vaccinations with 7-Day Rolling Average",
    x = "Date",
    y = "Number of Vaccinations"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16, face = "bold"),
    axis.title = element_text(size = 12),
    axis.text = element_text(size = 10)
  )

Weekly Patterns

Let’s examine if there are patterns in vaccination rates by day of the week:

vaccination_data <- vaccination_data %>%
  mutate(
    Weekday = weekdays(Date)
  )

# Reorder weekdays
vaccination_data$Weekday <- factor(
  vaccination_data$Weekday, 
  levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")
)

ggplot(vaccination_data, aes(x = Weekday, y = DailyVaccinations)) +
  geom_boxplot(fill = "#9C27B0", alpha = 0.7) +
  labs(
    title = "Vaccination Rates by Day of Week",
    x = "Day of Week",
    y = "Number of Vaccinations"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16, face = "bold"),
    axis.title = element_text(size = 12),
    axis.text = element_text(size = 10),
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

Conclusion

This analysis demonstrates the vaccination trends over the first quarter of 2022. We can observe that:

Vaccination rates increased significantly in February compared to January
There appears to be a weekly pattern with lower rates on weekends
By the end of March, we reached approximately 135,148 total vaccinations

Key Findings

Based on our analysis, we can draw several important conclusions:

The case fatality rate decreased over time, suggesting improvements in treatment protocols.
Vaccination rates showed clear weekly patterns with lower rates on weekends.
The estimated basic reproduction number (R0) provides insights into the virus’s transmissibility.

Next Steps

Future analysis will incorporate demographic data to understand risk factors and vaccine equity issues. We also plan to analyze long-term trends as more data becomes available.

[DEMO] COVID-19 Data Analysis