Marlyn-Mayienga/Predicting-Pneumonic-Plague-Dynamics-with-Google-Search-Trends

This project combines weekly epidemiological case counts of pneumonic plague in Madagascar (Aug–Nov 2017) with Google Trends data for related search terms to explore how online interest tracks—and even predicts—disease spread. # 📗 Table of Contents - 📖 Predicting Pneumonic Plague Dynamics with Google Search Trends - This Python project combines weekly epidemiological case counts of pneumonic plague in Madagascar (Aug–Nov 2017) with Google Trends data for related search terms to explore how online interest tracks—and even predicts—disease spread ​. It uses pandas and numpy to clean and merge time series, matplotlib/seaborn for visualization, and scikit‑learn to build and evaluate both univariate and multivariate linear regression models based on search‑term volumes and lagged case data. Through correlation analyses, error‑metric comparisons, and reflective discussion on public‑health applications and ethical considerations, the work demonstrates the power—and the pitfalls—of digital surveillance in informing outbreak response. - 🛠 PYTHON - [Python 3.x - `pandas` – data manipulation and analysis - `numpy` – numerical operations - `matplotlib / seaborn` – data visualization - `scikit-learn` – regression modeling and metrics - Jupyter Notebook – exploratory analysis and reporting] - Key Features - Time‑Series Integration & Visualization - Combines weekly pneumonic plague case counts with Google Trends search‑volume data and produces line plots to visualize outbreak dynamics alongside public interest. - Predictive Regression Modeling - Implements both univariate and multivariate linear regressions using (a) search‑term volumes and (b) lagged epidemiological case counts, then evaluates each model’s predictive power via correlation and mean absolute error metrics ​ - Public‑Health Synthesis & Ethical Reflection - Interprets model results for practical decision‑making (e.g., early warning systems), discusses real‑world applications (media monitoring, outbreak response), and addresses ethical concerns around data privacy and representativeness - 💻 Getting Started - To get a local copy up and running, follow these steps. - Prerequisites - Python 3.x - Jupyter Notebook - Git - Setup - Clone this r …