AdeAdeB/Monitoring_Trends_of_Lassa_Fever_in_Nigeria

Public health impact analysis project demonstrating how trend monitoring and pre/post intervention evaluation can support infectious disease program decision-making in Nigeria. # Monitoring_Trends_of_Lassa_Fever_in_Nigeria ## Project Overview - This project demonstrates how routine surveillance data can be used to monitor infectious disease trends and evaluate the impact of public health interventions. - Using a simulated but realistic dataset reflecting Lassa fever patterns in Nigeria (2019-2023), the analysis examines changes in confirmed cases, deaths, and case fatality rate (CFR) before and after aaa modeled intervention introduced in early 2021. - This project is designed to showcase a practical, decision-support approach suitable for Monitoring & Evaluation (M&E) teams, public health NGOs, and program managers. ☑️ Objectives - Monitor temporal trends in confirmed Lassa fever cases. - Assess the impact of a public health intervention using pre/post comparison. - Evaluate changes in disease severity using CFR. - Translate statistical findings into clear, actionable insights. 📂 Data Description - Time period: 2019-2023(monthly) - Geographic scope: Six Nigerian states - Key variables: - Confirmed cases - Deaths - Case Fatality Rate (CFR) - Intervention phase (Before / After) - Rolling average of cases ❎ Note: The dataset is simulated for analytical demonstration and does not represent official surveillance data. 🔍 Methods - Descriptive time-series analysis - Rolling averages to smooth short-term fluctuations - Pre- vs post-intervention comparison - Visual trend inspection with intervention marker - All analysis was conducted in Python using reproducible methods. 📈 Key Insights - Confirmed cases were higher and more volatile before the intervention period. - A strong and sustained reduction in average monthly cases was observed after the intervention. - CFR showed visible fluctuations but became more stable post-intervention. - Results suggest improved disease control following program implementation. 🧠 Programmatic Implications - Supports evidence-based program evaluation - Enables early detection of adverse …