"Study of Correlations Between Temperature, Pollution, and Flu Treatment Response: A Large-Scale Analysis"

Main Objective
Evaluate the effectiveness of a new treatment for seasonal flu by identifying potential correlations between:

Patients’ demographic and clinical data,

Environmental data (temperature, pollution),

Weekly clinical results (blood samples).

Code source: https://github.com/SergeMbela/techHealth

1. Data Collection and Structuring Legal Compliance and Data Anonymization

The data used in this project is anonymized according to the following standards:

• GCP (Good Clinical Practice): Ensures that data is handled according to good clinical practices.

• GDPR (General Data Protection Regulation): Ensures personal data confidentiality is protected according to European Union rules.

• HIPAA (Health Insurance Portability and Accountability Act): A U.S. law that protects individuals’ health information confidentiality and security. It defines standards for handling Protected Health Information (PHI) and applies to healthcare providers, insurance companies, and other entities dealing with health data.

Data Sources:

Patients (100,000): ID, city, age, sex, allergies (penicillin, peanuts, pollen), vaccination status, treatment response.

• Climate & Pollution: Daily temperature and pollution index by city.

• Clinical Results: Weekly blood samples (leukocytes, CRT, lymphocytes, and neutrophils) — indicators of immune response, viral load, etc.

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• Patient distribution by city: To avoid bias, patient allocation across cities must reflect actual population sizes. That is, more populated cities should receive more patients — proportionally to their population size.

2. Data Preparation and Cleaning

• Standardizing formats (dates, cities, units of measurement).
• Handling missing values (imputation or deletion depending on the case).
• Aggregation by week and by city for environmental data. **

• Feature Engineering (Creation of Useful Variables)**

• Demographic Variables: Age group categorization, urban density (city).

• Clinical Factors: Presence/absence of allergies as binary variables.

• Environmental Factors: 7-day moving averages of temperature and pollution.

• Treatment Response: Binary classification (positive/negative response) or continuous clinical score.

4. Exploratory Data Analysis (EDA)

• Visualization: Trend curves (treatment response vs temperature/pollution).
• Correlations: Correlation matrices, Pearson/Spearman tests between clinical, demographic, and environmental variables.
• Heatmaps: To visualize geographic disparities.

5. Statistical Modeling and Machine Learning
Objectives:

• Identify predictive factors of a good treatment response.
• Measure the impact of climate and pollution on treatment effectiveness.
• Potential Models:
• Logistic Regression: Binary prediction (response or no response to treatment).
• Random Forest / XGBoost: For classification and feature importance.
• Time Series Analysis: Temporal impact of weather/pollution.
• Mixed Effects Models: To model fixed effects (age, sex) and random effects (city, day).

6. Treatment Effectiveness Evaluation
Comparison between treated vs untreated groups (if a control group is included).

• Analysis of changes in blood biomarkers.
• Recovery rate or reduction in viral load.

7. Results Reporting and Recommendations

• Summarize the most significant correlations (e.g., high pollution → lower treatment response?).
• Map geographic areas where the treatment is most/least effective.

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