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Data Source

The dataset was provided by the Mexican government. This dataset contains an enormous number of anonymized patient-related information including pre-conditions. The raw dataset consists of 21 unique features and 1,048,576 unique patients.

The covid_sub.csv contains data from 20% of the population, and we will provide data Covid Data_full.csv for the full population to check the reproducibility of code.

Project Description

The Covid-19 report has four analysis parts.

  • code/01_demography.R from L9otus is Demographic Analysis.

    • Analyze the distribution of different demographic characteristics, such as age and gender.
      • Pie chart: Displaying the proportion of two genders.
      • Bar chart and box plot: Displaying the distribution of different ages.
    • Examine the distribution of pregnant and immunosuppressed patients, focusing on their proportions among hospitalized or not.
      • Classification table: Displaying the proportion of immunosuppressed/non-immunosuppressed patients in each patient_type.
      • Classification table: Displaying the proportion of pregnant/non-pregnant in each patient_type.

  • code/02_health_outcome.R from kmg1024 is Case Severity and Health Outcomes Analysis

    • Distribution of COVID-19 Cases by Classification Levels output/classification_bar_chart.png
      • Generates a bar chart showing the number of cases categorized as mild, moderate, and severe.
    • Relationship Between Pre-existing Health Conditions and Case Severity output/diabetes_severity_scatter_plot.png
      • Creates a scatter plot illustrating the association between conditions like diabetes and hypertension with the severity of COVID-19 cases.
    • Classification Distribution Table output/classification_table.csv
      • Summarizes the number and percentage of cases in each classification level.
    • Patient Type Outcomes Table output/patient_outcomes_table.csv
      • Compares mortality rates, ICU admissions, and intubation rates between different patient types (e.g., hospitalized vs. returned home).

  • code/03_association.R from zye229zye is Association Analysis

    • severity_results.csv
      • Contains logistic regression results for ICU admission factors (e.g., diabetes, renal chronic disease, hypertension).
    • severity_odds_ratios.png
      • Visualizes the odds ratios from the logistic regression analysis of ICU admissions.
    • mortality_results.csv
      • Summarizes logistic regression results for mortality factors (e.g., age, hypertension, diabetes, obesity).
    • mortality_odds_ratios.png
      • Displays odds ratios for factors influencing mortality.
    • tobacco_obesity_results.csv
      • Contains logistic regression results for the impact of tobacco use and obesity on ICU admissions.
    • tobacco_obesity_odds_ratios.png
      • Visualizes odds ratios for tobacco use and obesity in predicting ICU admissions.
    • tobacco_obesity_stacked_bar.png
      • Shows a stacked bar chart of ICU admissions categorized by tobacco use and obesity status.

  • code/04_temporal_trend.R from Shicy621 is Temporal Trends and Outcome Analysis

    • Case Trends Over Time daily_cases.png weekly_cases.png monthly_cases.png
      • Visuals and summary statistics showing case counts across intervals (daily, weekly, monthly), with insights on significant fluctuations during key periods.
    • Mortality and Hospitalization Trends mortality_hospitalization_rates.png
      • Charts showing mortality and hospitalization rate changes over time, highlighting any significant increases or decreases across different periods.
    • ICU Admission and Intubation Rates icu_intubation_rates.png
      • Analysis of ICU and intubation rates during seasonal and pandemic peaks, with comparisons to low-demand periods to assess resource strain.
    • Date-of-Death and Severe Case Prediction death_trends.png
      • Death trends over time, with potential predictive insights for severe cases, aimed at aiding resource planning for peak periods.
    • Summary summary_statistics.rds
      • Key findings and recommendations on managing resources effectively during high-demand intervals.

The analysis parts are combined in covid_report.Rmd.

code/00_render_report.R is used for rendering the combined report from the command line