From Wastewater Sampling and Pathogen Detection to Viral Genomic Surveillance, AI Forecasting & Public Health Intelligence
Wastewater-Based Epidemiology (WBE) is an emerging approach to infectious disease surveillance that enables researchers to monitor pathogen circulation, track viral activity and assess community-level public health trends through wastewater analysis.
This 3-day hands-on workshop provides beginner-to-advanced training in wastewater pathogen surveillance, molecular detection, viral monitoring, genomic epidemiology and predictive public health analytics. Participants will explore surveillance data for SARS-CoV-2, influenza A, RSV and other emerging pathogens while learning to identify unusual viral activity patterns and interpret potential public health risks.
Using Python and Google Colab, participants will work with real-world wastewater surveillance datasets, perform statistical trend analysis, explore viral genomic monitoring results and develop basic forecasting models. The workshop combines environmental microbiology, molecular epidemiology and computational analytics to build practical research skills in wastewater-based public health surveillance.
To equip participants with theoretical knowledge and practical computational skills in wastewater-based epidemiology, pathogen surveillance, viral genomic monitoring and predictive public health analytics using Python, Google Colab and publicly available surveillance datasets.
Understand the fundamentals of wastewater-based epidemiology and its applications in infectious disease surveillance.
Explore wastewater sampling, molecular pathogen detection and viral quantification techniques.
Analyze multi-pathogen wastewater surveillance datasets using Python and Google Colab.
Examine viral activity trends, surveillance indicators and statistical early warning signals.
Understand wastewater genomic surveillance, viral variant identification and lineage monitoring.
Apply statistical and machine learning approaches to wastewater viral activity forecasting.
Develop research-oriented visualizations and interpret surveillance findings for public health applications.
Session Focus: Build a strong understanding of wastewater-based pathogen surveillance, molecular detection principles and practical epidemiological data processing.
Topics Covered:
Introduction to wastewater-based epidemiology, public health applications and disease surveillance.
Wastewater sampling, sewer catchments, composite sampling and population representation.
Major wastewater pathogens: SARS-CoV-2, influenza A, RSV, norovirus and emerging infections.
Fundamentals of pathogen detection using RT-qPCR, digital PCR and molecular assays.
Understanding viral RNA concentrations, detection limits, cycle thresholds and measurement uncertainty.
Introduction to PMMoV normalization, sample recovery controls and quality assessment.
Exploring CDC NWSS and WastewaterSCAN datasets and surveillance indicators.
Importing, cleaning, filtering and organizing wastewater surveillance data using Python.
Visualizing pathogen activity across surveillance sites and monitoring periods.
Hands-On 1: Wastewater Pathogen Detection Data & Surveillance Analysis
Participants will analyze a prepared wastewater dataset, including public viral activity indicators and a small teaching dataset of molecular assay results. Using Python, they will clean and organize records, compare pathogen trends, interpret detection results and produce location-wise surveillance visualizations.
Tools: Google Colab, Python, pandas, NumPy, Matplotlib, CDC NWSS.
Practical Output: Cleaned surveillance dataset, pathogen detection summary and wastewater viral activity graphs.
Session Focus: Advance from basic surveillance analysis to multi-pathogen viral monitoring, statistical trend detection and wastewater genomic epidemiology.
Topics Covered:
Multi-pathogen wastewater surveillance for respiratory and gastrointestinal viruses.
Advanced viral trend analysis using time-series data and rolling averages.
Understanding site-specific viral activity baselines and standardized surveillance indicators.
Detecting unusual viral activity through statistical thresholds and anomaly detection.
Comparing temporal and geographical pathogen activity patterns.
Introduction to wastewater sequencing, variant detection and genomic surveillance.
SARS-CoV-2 lineage monitoring and introduction to Freyja-based variant deconvolution.
Exploring trends in norovirus, influenza and emerging wastewater pathogens.
Interpreting surveillance variability, uncertainty and potential early warning signals.
Hands-On 2: Viral Trend Analysis, Anomaly Detection & Genomic Surveillance Visualization
Participants will extend the Day 1 dataset to examine viral activity trends, calculate rolling statistics, identify unusual signal changes and visualize pathogen trends. A prepared Freyja output will be used to illustrate changes in SARS-CoV-2 lineage composition.
Tools: Google Colab, Python, pandas, SciPy, Plotly, prepared Freyja results.
Practical Output: Multi-pathogen trend dashboard, exploratory early warning indicators and viral lineage composition visualization.
Google Colab: Fully compatible when sequencing results are preprocessed
Advanced research note: Freyja is introduced through genuine sequencing-analysis results, not represented as performing lineage prediction from CDC viral activity levels. Full lineage deconvolution remains a separate, more computationally demanding workflow.
Session Focus: Apply statistical forecasting and machine learning to wastewater viral activity and integrate the findings into a public health surveillance report.
Topics Covered:
Principles of predictive epidemiology and wastewater-based outbreak signal forecasting.
Time-series feature engineering, lag variables and rolling-window analysis.
Statistical forecasting using autoregressive models and exponential smoothing.
Introduction to machine learning-based wastewater viral activity prediction.
Random Forest regression for time-series data and model comparison.
Time-aware model validation, prediction error and forecast uncertainty.
Comparing wastewater indicators with clinical surveillance data when available.
Developing interactive pathogen trend visualizations and research dashboards.
Translating model predictions into cautious public health interpretations.
Hands-On 3: Wastewater Viral Activity Forecasting & Public Health Dashboard
Participants will build a short-term forecasting model using historical wastewater data, compare its performance with a simple baseline, and visualize forecast results. They will then prepare a final dashboard combining viral activity, exploratory warning signals and model evaluation.
Tools: Google Colab, Python, pandas, statsmodels, scikit-learn, Plotly.
Practical Output: Validated baseline forecasting model, interactive wastewater surveillance dashboard and final public health research report.
This workshop is designed for:
Students: Undergraduate and postgraduate students in Biotechnology, Microbiology, Environmental Science, Public Health and Life Sciences.
Research Scholars: PhD scholars and researchers working in environmental microbiology, molecular epidemiology, virology and infectious diseases.
Academicians: Faculty members and academic researchers interested in wastewater surveillance and computational epidemiology.
Public Health Professionals: Epidemiologists, disease surveillance specialists and public health laboratory professionals.
Environmental Researchers: Wastewater treatment researchers, environmental scientists and water quality monitoring professionals.
Bioinformatics & Data Science Professionals: Researchers and analysts interested in pathogen monitoring, epidemiological modeling and predictive public health analytics.
Healthcare & Clinical Researchers: Professionals involved in infectious disease research, molecular diagnostics and population health surveillance.
Prerequisites: Basic knowledge of microbiology, environmental science or public health is recommended. Prior advanced programming experience is not required, as the workshop includes guided hands-on exercises using Google Colab.
November 13, 2026
IST 4:30 PM
November 11, 2026 – November 13, 2026
IST 5:30 PM
Upon successful completion of the workshop, participants will be able to:
Process and analyze real-world wastewater pathogen surveillance datasets.
Interpret molecular detection results and viral activity indicators.
Visualize SARS-CoV-2, influenza A, RSV and other available pathogen surveillance trends.
Perform statistical trend analysis and identify unusual viral activity patterns.
Interpret prepared wastewater genomic surveillance and viral lineage data.
Build and evaluate basic wastewater viral activity forecasting models using Python.
Develop surveillance dashboards and prepare data-driven public health research reports.
Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).
Watch the recording in your dashboard and send your questions to the mentor through the support desk.
Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.
Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy
Registration open
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