MicaiagSVG01/Nigeria_Lassa_fever_surveillance_Platform

Interactive infectious disease surveillance dashboard analyzing NCDC data to explore temporal trends, state-level disease burden, mortality, and case fatality patterns in Nigeria. # Nigeria_Lassa_fever_surveillance_Platform Interactive infectious disease surveillance dashboard analyzing NCDC data to explore temporal trends, state-level disease burden, mortality, and case fatality patterns in Nigeria. 🌐 **Live Demo:** (nigerialassafeversurveillan…) 📊 **Current MVP:** Lassa Fever, 2026 STREAMLIT APP LINK: nigerialassafeversurveillan… ## Overview The **Nigeria Infectious Disease Surveillance Dashboard** is a data-driven public health surveillance project designed to explore infectious disease patterns across Nigeria using publicly available surveillance reports from the **Nigeria Centre for Disease Control and Prevention (NCDC)**. The long-term goal of the project is to develop a multi-disease surveillance platform capable of presenting temporal trends, geographic disease burden, mortality patterns, case fatality rates, and potential outbreak signals in an accessible and interactive format. The current MVP focuses on **Lassa fever surveillance data for 2026**. The MVP was built with Python, Pandas, Plotly, and Streamlit. A more advanced version is planned as a full web application with a modern frontend, database integration, interactive geographic visualisation, and more advanced surveillance analytics. --- # Project Motivation Nigeria publishes valuable infectious disease surveillance information through weekly epidemiological reports and disease-specific situation reports. However, much of this information is distributed across individual PDF reports. This project explores how publicly available surveillance information can be transformed into structured datasets and interactive visualisations that make it easier to: * monitor disease activity over time * identify high-burden states * compare confirmed cases and mortality * examine case fatality rates * understand geographic disease patterns * support data exploration for …