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

Residuals and Least-Squares Property

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

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.

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,...

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:

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...

You might also read

Related Articles

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

Sort by

Same author

Shrinky Dinks-Based Bionic Meta-Microneedles for Advancing Wound Healing.

ACS applied materials & interfaces·2026

Same author

A comparative study on the pharmacological effects of Achyranthes root against atherosclerosis in ApoE<sup>-/-</sup> mice based on the spectrum-effect relationship.

Laboratory animal research·2026

Same author

Real-Time Soft Tissue Deformation Framework for Haptic-Enabled Robotic Surgical Training in Virtual Reality.

Annals of biomedical engineering·2026

Same author

AI-Derived Smart Microneedle Systems for Advanced Wound Management: From Intelligent Sensing to Closed-Loop Therapy.

Macromolecular bioscience·2026

Same author

Ordered micro-nano structures: synergistic integration of microneedles and photonic crystals for advanced biomedical systems.

Journal of materials chemistry. B·2026

Same author

Computational-Aided Mutagenesis of Lipase CRL1 with Promoted Stability and Activity for Biosynthesis of Vitamin E Succinate.

Journal of agricultural and food chemistry·2026

Search research articles

Related Experiment Video

Updated: Nov 10, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

Maximum likelihood-based extended Kalman filter for COVID-19 prediction.

Jialu Song¹, Hujin Xie¹, Bingbing Gao²

¹School of Engineering, RMIT University, Melbourne, VIC 3000, Australia.

Chaos, Solitons, and Fractals

|April 7, 2021

Summary

This study introduces a novel dynamic prediction method for COVID-19 spread using time-dependent parameters in the SEIRD model. The approach enhances epidemiological modeling accuracy for better public health strategies.

Keywords:

COVID-19 modelling Extended Kalman filter Maximum likelihood estimation SEIRD model Time-dependent model parameters

More Related Videos

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

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

Published on: December 9, 2015

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19

Published on: February 16, 2022

Related Experiment Videos

Last Updated: Nov 10, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

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

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

Published on: December 9, 2015

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19

Published on: February 16, 2022

Area of Science:

Epidemiology
Mathematical Modeling
Public Health

Background:

Accurate prediction of COVID-19 spread is crucial for epidemic control.
Existing models often use constant parameters, failing to capture real-world dynamics.
Dynamic modeling is needed to reflect the evolving nature of infectious disease spread.

Purpose of the Study:

To develop a new method for dynamic prediction of COVID-19 spread.
To incorporate time-dependent parameters into epidemiological models.
To improve the accuracy of COVID-19 spread estimation and forecasting.

Main Methods:

Discretization of the susceptible-exposed-infected-recovered-dead (SEIRD) model in the time domain.
Construction of nonlinear state-space equations for dynamic estimation.
Application of maximum likelihood estimation for online parameter estimation.
Utilization of an extended Kalman filter for dynamic spread estimation.

Main Results:

The proposed method effectively estimates time-dependent model parameters.
Dynamic COVID-19 spread is accurately estimated using the developed approach.
Simulations for China and the United States demonstrate the method's efficacy.
The model shows strong performance in both COVID-19 modeling and prediction.

Conclusions:

The novel dynamic prediction method offers improved accuracy over static models.
Time-dependent parameter estimation is key to realistic epidemiological modeling.
This approach provides a valuable tool for public health officials in managing COVID-19.
The method is validated by its application to real-world pandemic data.