Public Health Surveillance Systems & Early Warning Analytics Training Workshop

Course overview

The Public Health Surveillance Systems & Early Warning Analytics Training Workshop develops practical skills in disease surveillance, data quality assessment, outbreak signal detection, statistical alert thresholds and public health data visualization. Designed for epidemiologists, surveillance officers, health information professionals and emergency response teams, the workshop focuses on transforming routine health information into actionable early warnings that support timely verification, outbreak preparedness and effective public health response.

The Public Health Surveillance Systems & Early Warning Analytics Training Workshop equips public health professionals, epidemiologists, surveillance officers, health information managers and emergency response teams with practical skills to design, strengthen and evaluate surveillance systems, analyze health data and identify early warning signals of potential public health threats. The workshop covers routine disease surveillance, indicator-based surveillance, event-based surveillance, syndromic surveillance, laboratory reporting, community-based surveillance and digital surveillance platforms. Participants learn how to assess data quality, detect unusual disease patterns, establish alert thresholds, analyze trends, visualize surveillance indicators and communicate actionable findings to public health decision-makers.

Course objectives

• Explain the principles, components and functions of public health surveillance systems.
• Develop a practical early warning analytics framework for a public health surveillance programme.
• Prepare surveillance bulletins, situation reports and early warning summaries.
• Strengthen alert verification, escalation, notification and response workflows.
• Integrate information from health facilities, laboratories and community reporting systems.
• Develop dashboards and visualizations for real-time or routine surveillance monitoring.
• Apply statistical and analytical techniques to detect anomalies in surveillance data.
• Identify unusual disease patterns, unexpected increases and potential outbreak signals.
• Establish and interpret alert thresholds and early warning indicators.
• Clean, organize and analyze routine disease surveillance datasets.
• Differentiate between indicator-based, event-based, syndromic, laboratory-based and community-based surveillance.
• Assess surveillance data quality, completeness, timeliness, sensitivity and representativeness.

Who should attend

Course content

• Module 1: Foundations of Public Health Surveillance Definition and purpose of surveillance, core surveillance functions, public health intelligence, surveillance cycle, priority diseases and events, surveillance objectives, routine and emergency surveilla
• Module 2: Types of Surveillance Systems and Their Applications Indicator-based surveillance, event-based surveillance, syndromic surveillance, laboratory-based surveillance, sentinel surveillance, active and passive surveillance, community-based surveilla
• Module 3: Surveillance Data Sources and Information Flows Health facility registers, outpatient and inpatient records, laboratory reports, community health information, pharmacy and mortality data, electronic health records, notifiable disease reporting,
• Module 4: Surveillance Case Definitions and Reporting Protocols Suspected, probable and confirmed cases, standardized case definitions, case classification, notification requirements, event verification, zero reporting, reporting frequency, completeness o
• Module 5: Surveillance Data Management and Quality Assurance Data structures and data dictionaries, data validation rules, missing and duplicate records, inconsistent dates and classifications, outlier detection, data reconciliation, completeness and time
• Module 6: Descriptive Epidemiology and Surveillance Indicators Person-place-time analysis, incidence and prevalence indicators, case counts and rates, morbidity and mortality trends, age and sex stratification, geographical comparisons, positivity rates,
• Module 7: Early Warning Signals and Alert Thresholds Principles of early warning systems, baseline disease patterns, expected versus observed cases, alert thresholds, epidemic channels, historical baselines, seasonal patterns, percentage increases and rat
• Module 8: Statistical Methods for Anomaly Detection Moving averages, standard deviation-based thresholds, historical comparison methods, seasonal adjustment concepts, statistical process monitoring, aberration detection, outlier analysis, comparison of cu
• Module 9: Syndromic Surveillance and Event-Based Intelligence Syndrome definitions, early indicators before laboratory confirmation, symptom-based reporting, unusual clinical presentations, community reports and rumor tracking, media and hotline signals,
• Module 10: Digital Surveillance Systems and Public Health Informatics Electronic disease notification, digital reporting workflows, mobile data collection, electronic health information systems, interoperability, data exchange standards, system access con
• Module 11: Data Visualization, Dashboards and Geospatial Surveillance Surveillance dashboard design, trend charts and epidemic curves, indicator scorecards, alert summaries, geographic distribution of reported cases, hotspot visualization, disease mapping
• Module 12: Early Warning Analytics and Forecasting Short-term disease trend analysis, baseline forecasting, simple time-series methods, expected disease patterns, monitoring changes in transmission, incorporating reporting delays, nowcasting concepts, int
• Module 13: Alert Verification, Risk Assessment and Response Coordination Initial alert assessment, event verification procedures, case investigation, laboratory coordination, risk assessment, escalation protocols, outbreak response activation, incident ma
• Module 14: Surveillance System Evaluation and Performance Improvement Surveillance system attributes, simplicity and flexibility, acceptability, sensitivity, positive predictive value, representativeness, timeliness and stability, data quality indicators,
• Module 15: Practical Early Warning Analytics Capstone Importing and cleaning a sample surveillance dataset, calculating weekly disease indicators, establishing a historical baseline, identifying unusual increases, applying alert thresholds, visualizing tr

General notes

• Expected Learning Outcomes By the end of the training, participants will be able to assess surveillance data quality, interpret disease trends, identify unusual patterns, establish alert thresholds, analyze early warning signals, visualize surveillance indicators and support timely public health investigations. Participants will also understand how to distinguish a statistical signal from a verified outbreak, reduce avoidable false alerts, document decisions and strengthen communication between surveillance, laboratory, clinical and emergency response teams.
• Training Methodology The workshop combines expert presentations, facilitated discussions, case studies, hands-on data analysis, surveillance simulation exercises, dashboard development, group assignments and a capstone project. Participants work with sample or appropriately anonymized surveillance data to practice signal detection, threshold interpretation, data quality assessment and alert management. Exercises can be adapted to routine disease surveillance, event-based reporting, emergency response and national or county-level public health information systems.
• Recommended Software and Tools Excel, Power BI, Python, R, SQL, QGIS, DHIS2, KoboToolbox, Epi Info, electronic health information systems, GPS-enabled devices, WHO surveillance guidance, WHO Early Warning, Alert and Response resources, enterprise AI tools.
• Practical Exercises and Expected Outputs Clean a routine disease surveillance dataset — validated surveillance dataset, calculate reporting completeness and timeliness — surveillance performance scorecard, analyze weekly disease counts — descriptive surveillance report, establish historical alert thresholds — early warning threshold worksheet, detect unusual increases in case counts — alert detection summary, construct epidemic curves — disease trend visualization, review community and laboratory event reports — event verification register, map reported disease cases — surveillance distribution map, develop an early warning dashboard — interactive surveillance dashboard, design an alert escalation workflow — alert management procedure, evaluate a sample surveillance system — surveillance evaluation matrix, prepare a weekly surveillance bulletin — public health intelligence report, complete the capstone exercise — early warning analytics report and surveillance improvement plan.
• An applied professional training programme combining surveillance system strengthening, public health data analytics, early warning signal detection, digital surveillance tools and alert response workflows to improve the timeliness and effectiveness of public health decision-making.

Frequently asked questions

Schedule & fees