euphoricking/malaria-incidence-nigeria

# Malaria Incidence in Nigeria: A Geospatial Analysis Welcome to our project repository! This project focuses on analyzing and visualizing malaria incidence data in Nigeria using advanced geospatial techniques and interactive data visualization. We combine two key components in this repository: 1. Data Disaggregation Notebook: A Jupyter Notebook that processes national malaria data by disaggregating it to the state level using population, Land Use Land Cover (LULC), and environmental data (temperature and rainfall). This notebook calculates weighted risk scores to allocate malaria cases across Nigeria's states. 2. Interactive Dashboard: A Dash-based web application that presents our findings via an interactive choropleth map, trend charts, bar charts, and key performance indicator (KPI) cards. The dashboard dynamically fetches data from a PostgreSQL database and allows filtering by year, indicator, and state. # Project Overview Nigeria continues to face one of the highest malaria burdens globally, significantly impacting vulnerable populations such as children under five and pregnant women. Our project employs Geographic Information Systems (GIS) alongside interactive story mapping to understand the spatial and temporal distribution of malaria across Nigeria’s 36 states and the Federal Capital Territory. By integrating diverse data sources—including population statistics, LULC classifications, and environmental data (temperature and rainfall)—we are able to: 1. Pinpoint high-risk regions, 2. Analyze seasonal trends from 2010 to 2020, and 3. Provide insights that can help shape targeted public health interventions. The project builds on previous research and data resources, such as the Malaria Atlas Project and official environmental datasets, to offer a robust, data-driven perspective on malaria transmission. # Technologies Used Our project leverages a suite of modern tools and libraries: 1. Python: For data processing and analysis. 2. Jupyter Notebook: F …