Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

182
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
182
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

513
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
513
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

85
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
85
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

774
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
774
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

178
Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
178
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

121
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
121

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Implementation of SARS-CoV-2 Wastewater Surveillance Systems in Germany-Pilot Study in the Federal State of Thuringia.

Microorganisms·2026
Same author

Stage-Specific Microbiota Transitions Throughout Black Soldier Fly Ontogeny.

Microbial ecology·2026
Same author

Number of Austrian SARS-CoV-2 infections in the 2024/2025 season: Analysis of national wastewater data.

Public health·2025
Same author

A global database of soil microbial phospholipid fatty acids and enzyme activities.

Scientific data·2025
Same author

Critical review on end-of-pipe technologies for nitrous oxide removal as part of a novel comprehensive concept for greenhouse gas emission mitigation at wastewater treatment plants.

Water science and technology : a journal of the International Association on Water Pollution Research·2025
Same author

Monitoring SARS-CoV-2 Dissemination in Wastewater and Virus Isolation in Cell Cultures: An Integrated Approach for Pathogen Detection and Surveillance.

Journal of cellular and molecular medicine·2025

Related Experiment Video

Updated: Sep 5, 2025

Concentration of Virus Particles from Environmental Water and Wastewater Samples Using Skimmed Milk Flocculation and Ultrafiltration
10:53

Concentration of Virus Particles from Environmental Water and Wastewater Samples Using Skimmed Milk Flocculation and Ultrafiltration

Published on: March 17, 2023

1.7K

Data modelling recipes for SARS-CoV-2 wastewater-based epidemiology.

Wolfgang Rauch1, Hannes Schenk1, Heribert Insam2

  • 1Unit of Environmental Engineering, Department of Infrastructure, University of Innsbruck, Technikerstrasse 13, 6020, Innsbruck, Austria.

Environmental Research
|July 7, 2022
PubMed
Summary

Wastewater-based epidemiology provides crucial pandemic insights. This study offers a framework for data modeling, emphasizing preprocessing and simple methods for accurate signal analysis and short-term forecasting.

Keywords:
Data modellingForecastRegressionSARS-CoV-2SmoothingWastewater-based epidemiology

More Related Videos

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples
09:26

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Published on: June 30, 2023

1.2K
Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
08:26

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

1.8K

Related Experiment Videos

Last Updated: Sep 5, 2025

Concentration of Virus Particles from Environmental Water and Wastewater Samples Using Skimmed Milk Flocculation and Ultrafiltration
10:53

Concentration of Virus Particles from Environmental Water and Wastewater Samples Using Skimmed Milk Flocculation and Ultrafiltration

Published on: March 17, 2023

1.7K
Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples
09:26

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Published on: June 30, 2023

1.2K
Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
08:26

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

1.8K

Area of Science:

  • Environmental science
  • Epidemiology
  • Data science

Background:

  • Wastewater-based epidemiology is a key tool for public health surveillance.
  • The rapid growth of this field has led to diverse, unstandardized data modeling approaches.
  • A coherent framework is needed for robust analysis and communication of wastewater data.

Purpose of the Study:

  • To establish a coherent data modeling framework for wastewater-based epidemiology.
  • To focus on robust, simple, and readily applicable modeling concepts.
  • To demonstrate the importance of data preprocessing and smoothing for signal analysis.

Main Methods:

  • Data preprocessing, including normalization with biomarkers and temporal spacing (weekly downsampling).
  • Data smoothing techniques to represent signal dynamics.
  • Multivariate regression (specifically multiple linear regression) for correlating wastewater signals with epidemic indicators.
  • Application of simple predictive models like exponential smoothing and autoregressive models for forecasting.

Main Results:

  • Data preprocessing, particularly normalization and weekly temporal spacing, is critical for signal analysis.
  • Data smoothing is essential for both communication and advanced analyses like regression and forecasting.
  • Multivariate regression is necessary to explain epidemic dynamics using wastewater signals alongside other indicators.
  • Simple models achieve accurate short-term (7-day) predictions, but accuracy declines rapidly for longer forecast horizons.

Conclusions:

  • A standardized framework using robust, simple modeling techniques enhances wastewater-based epidemiology.
  • Effective data preprocessing and smoothing are foundational for reliable wastewater surveillance.
  • While short-term forecasts are feasible with basic models, long-term prediction remains challenging.