Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Steps in Outbreak Investigation

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:

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Statistical Methods for Analyzing Epidemiological Data

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:

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...

Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....

Kaplan-Meier Approach

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...

You might also read

Related Articles

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

Sort by

Same author

Clocks out of sync: Increased epigenetic aging variability in multiple sclerosis.

Journal of the neurological sciences·2026

Same author

Infinitesimal homeostasis in mass-action systems.

Journal of mathematical biology·2026

Same author

Inferring structure and parameters of stochastic reaction networks with logistic regression.

PloS one·2026

Same author

Leg strength and neural activation deficits in older adults with multiple sclerosis.

Multiple sclerosis and related disorders·2026

Same author

The use of the EM algorithm for regularization problems in high-dimensional linear mixed-effects models.

Statistical methods in medical research·2025

Same author

Model fit vs. predictive reliability: a case study of the 1978 influenza outbreak.

Scientific reports·2025

Search research articles

Related Experiment Video

Updated: Mar 2, 2026

High-throughput Detection Method for Influenza Virus

High-throughput Detection Method for Influenza Virus

Published on: February 4, 2012

Short-term Bayesian influenza forecasting in a hospital environment using a linear Kalman filter.

Shankar Kaleeswaran Mani¹, Grzegorz A Rempala², Eben Kenah²

¹OhioHealth,Columbus,Ohio,USA,; Division of Biostatistics, College of Public Health, The Ohio State University,Columbus,Ohio,USA.

Journal of Theoretical Biology

|February 28, 2026

Summary

Hospitals can now better forecast weekly influenza (flu) cases and tests using a Bayesian Kalman filter. This method reliably predicts flu patient volume up to four weeks in advance, aiding hospital resource management.

Keywords:

Bayesian Kalman filter Hospital capacity management Influenza epidemic forecasting MCMC

More Related Videos

Monitoring Influenza Virus Survival Outside the Host Using Real-Time Cell Analysis

Monitoring Influenza Virus Survival Outside the Host Using Real-Time Cell Analysis

Published on: February 20, 2021

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Related Experiment Videos

Last Updated: Mar 2, 2026

High-throughput Detection Method for Influenza Virus

High-throughput Detection Method for Influenza Virus

Published on: February 4, 2012

Monitoring Influenza Virus Survival Outside the Host Using Real-Time Cell Analysis

Monitoring Influenza Virus Survival Outside the Host Using Real-Time Cell Analysis

Published on: February 20, 2021

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Area of Science:

Epidemiology
Health Informatics
Biostatistics

Background:

Accurate forecasting of influenza (flu) cases is crucial for hospital operations, including staffing and supply management.
Timely patient care relies on predictable flu-related patient volumes.

Purpose of the Study:

To describe a practical application of a Bayesian Kalman filter for forecasting weekly flu tests and positive cases in a hospital setting.
To evaluate the filter's effectiveness in predicting flu patient volume.

Main Methods:

Utilized a Bayesian Kalman filter approach.
Incorporated real-time hospital data and historical flu patterns.
Applied the method to data from a large Ohio hospital system.

Main Results:

The Bayesian Kalman filter provided dependable forecasts of weekly flu tests and positive cases.
The model accurately predicted flu-related patient volume up to four weeks ahead.
Demonstrated a practical and effective forecasting tool for hospital settings.

Conclusions:

A Bayesian Kalman filter offers a reliable method for forecasting weekly influenza trends in hospitals.
This approach enhances hospital preparedness and resource allocation for flu season.
The study highlights the value of integrating real-time data with historical patterns for predictive health analytics.