NIGERIA · SCH · PZQ Loading…
All Years (2014–2024)
Disease burden, endemicity & MDA delivery across Nigeria
Implementation Units
LGAs with data
Endemic IUs
LGAs needing PC
Population Requiring PC
Estimated burden
High Endemicity IUs
≥50% prevalence
Moderate Endemicity
10–49% prevalence
States Active
With reported data
Nigeria LGA Map
Endemicity Distribution
LGA-Level Implementation Data — All States
State LGA Endemicity Pop at Risk Treated Coverage % MDA Status
PC Coverage Rate
Epidemiological coverage
Population Treated
Received PZQ
Population Targeted
Programme target
MDA Rounds Delivered
IUs with MDA
National Treatment vs Coverage
Treated vs Requiring PC
PC Coverage History (2014–2024)
All-State Coverage Ranking sorted by coverage %
State-Level Coverage Summary
State Pop at Risk Pop Treated Coverage % IU Count MDA Delivered
Total Purchase Orders
Total POs tracked
PZQ Tablets Delivered
Total tablets delivered
Dispatch Lag
Packing → Shipment (days)
Avg Transit Time
Shipment → Arrival (days)
Last-Mile Time
Arrival → Delivery (days)
Avg Arrival Delay
Actual vs Estimated (days)
MDA Delay
vs Planned Date (days)
Shipment Pipeline by Stage
Drug Quantity Breakdown
Supply Chain Performance Trends (2014–2024)
Delay vs Coverage
Logistics Bottleneck Analysis (Stage-by-Stage Delays)

Stacked distribution of delays across supply chain stages. Identify which segment (Dispatch, Transit, Last-Mile) contributes most to overall MDA delays.

Supply Chain Purchase Order Records
PO Number Year Drug Qty (M tabs) Stage Dispatch Lag Transit Time Last-Mile MDA Delay

Action Intelligence Engine

Data-driven priority recommendations generated from epidemiological and supply chain analysis

Priority State Ranking (Burden + Endemicity)
Coverage Gap Analysis

States in red zone = High burden + Low coverage = priority for next MDA cycle.

Advanced Correlation Analytics
vs
Pearson Correlation ($r$)
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Analyze the statistical relationship between two selected indicators over time.

Historical Trend Comparison
Recommended Programme Actions
Priority State Issue Indicator Recommended Action Urgency
Open to Mid/Senior Roles Globally

Lawrence Oladeji

AI-Native Data Associate

Hire Me See Projects

Business Intelligence

Transforming raw datasets into executive C-Suite narratives. Expert in translating multi-million-dollar portfolios into interactive command centers using Power BI and Tableau.

Power BI / DAX Tableau

Data Engineering

Architecting scalable pipelines and robust data models. Proven track record dropping query latency by 15% across global health databanks using optimized SQL architectures.

PostgreSQL / SQL BigQuery Airflow ETL

Agentic AI & LLMs

Designing autonomous workflows orchestrating complex data routing via LLMs. Pioneering the integration of multi-agentic reasoning frameworks into traditional data domains.

LangGraph CrewAI Python
Executive Summary

I am an AI-Native Data Associate with 3+ years of experience transforming complex datasets into strategic insights for executive decision-making. My proven track record encompasses traditional BI (Power BI, SQL optimization, Data Governance) heavily augmented by deep expertise in Generative AI and LLM orchestration. I am highly adept at designing and deploying robust multi-agentic systems orchestrated with LangGraph and CrewAI to automate complex workflows, slash reporting latency entirely, and skyrocket operational productivity. I am profoundly passionate about architecting scalable, data-driven solutions at the exact intersection of robust data engineering and cutting-edge Agentic AI.

Professional Blueprint

Data Associate
May 2024 – Present
  • Designed and deployed executive-level Power BI dashboards for C-Suite stakeholders, optimizing multi-million-dollar portfolios.
  • Engineered complex SQL data models on BigQuery & PostgreSQL, cutting query latency by 15% across 50+ key indicators.
  • Centralized 56,000+ data points across 1,450+ indicators from world-class sources (World Bank, WHO) to ensure 100% regulatory reporting accuracy.
  • Constructed AI content frameworks utilizing multi-agent reasoning to automate reporting, saving significant overhead blocks.
Junior BI / Data Operations Analyst
2019 – April 2024
  • Synthesized business requirements into actionable data models for E-mobility.
  • Executed statistical modeling predicting demand, cutting lifecycles by 20%.
  • Identified high ROI cost-savings via exploratory spatial geospatial analysis.

Academic Background

MSc in Mechanical Engineering
University of Ibadan · 2023 - 2024
BSc in Mechanical Engineering (2:1 Honors)
Federal Univ. of Agriculture, Abeokuta · CGPA: 3.99/5.0