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Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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:
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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:
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

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...

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Related Experiment Video

Updated: May 8, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Published on: December 9, 2015

Modelling under-reporting in epidemics.

Kokouvi M Gamado1, George Streftaris, Stan Zachary

  • 1Biomathematics and Statistics Scotland, Kings Buildings, Edinburgh, EH9 3JZ, UK, kokouvi@bioss.ac.uk.

Journal of Mathematical Biology
|August 15, 2013
PubMed
Summary

Ignoring under-reporting in epidemic models leads to inaccurate infection rates and reproduction numbers. This study develops models to accurately estimate epidemic parameters, even with incomplete case data.

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Biostatistics

Background:

  • Under-reporting of infectious cases introduces significant bias in epidemic modeling.
  • Accurate parameter estimation is vital for understanding disease dynamics and implementing control strategies.

Purpose of the Study:

  • To investigate the impact of ignoring under-reporting on epidemic model parameter estimation.
  • To develop and evaluate models that account for under-reporting in stochastic SIR (Susceptible-Infected-Recovered) epidemic models.
  • To assess the estimability of reporting rates and other epidemic parameters under various reporting scenarios.

Main Methods:

  • Stochastic Markovian SIR epidemic modeling incorporating diverse reporting processes.
  • Bayesian inference framework utilizing data augmentation and reversible jump Markov chain Monte Carlo (MCMC) techniques.

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Last Updated: May 8, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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06:55

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Published on: January 8, 2020

  • Analysis of constant, time-dependent, and source-dependent reporting probabilities.

    • Main Results:

    • Ignoring under-reporting leads to under-estimation of the infection rate and reproduction number.
    • Models accounting for under-reporting allow for more accurate estimation of epidemic parameters.
    • The Bayesian approach with MCMC effectively handles incomplete data and complex reporting mechanisms.

    Conclusions:

    • Under-reporting significantly biases epidemic parameter estimation, necessitating its explicit inclusion in models.
    • The developed Bayesian framework provides a robust method for estimating epidemic parameters even with incomplete reporting.
    • Accurate estimation of reporting rates is feasible and crucial for reliable epidemic forecasting and management.