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

Population Growth00:57

Population Growth

27.8K
Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
27.8K
Modeling with Differential Equations01:25

Modeling with Differential Equations

20
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
20
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.0K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

492
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:
492
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

752
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
752
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

900
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:
900

You might also read

Related Articles

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

Sort by
Same author

Incubation Period of Pertussis During a School-based Outbreak, South Korea, 2024.

The Journal of infectious diseases·2026
Same author

Simulating population compliance with pandemic interventions using large language models.

medRxiv : the preprint server for health sciences·2026
Same author

Rural-to-urban migrant worker mobility shaped measles epidemics in China.

PLoS computational biology·2026
Same author

Ascertaining cause-specific emergency department demand using forecast combinations.

BMC emergency medicine·2026
Same author

Estimation in Networks with Spatiotemporally Correlated Noise.

IEEE transactions on automatic control·2026
Same author

Reconstructing the early spatial spread of pandemic respiratory viruses in the United States.

Proceedings of the National Academy of Sciences of the United States of America·2026

Related Experiment Video

Updated: Jan 18, 2026

Estimating Virus Production Rates in Aquatic Systems
10:49

Estimating Virus Production Rates in Aquatic Systems

Published on: September 22, 2010

13.0K

Data assimilation for estimating time-varying reproduction numbers.

Han Yong Wunrow1, Sen Pei2, Jeffrey Shaman2,3

  • 1Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, USA.

Journal of the Royal Society, Interface
|January 15, 2026
PubMed
Summary

Adaptive inflation techniques improve estimates of the time-varying basic reproduction number (R0(t)). These methods enhance accuracy in infectious disease modeling, aiding public health decisions.

Keywords:
data assimilationinfectious disease dynamicsreproduction numbertime-varying parameters

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

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

Published on: December 9, 2015

11.0K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

9.1K

Related Experiment Videos

Last Updated: Jan 18, 2026

Estimating Virus Production Rates in Aquatic Systems
10:49

Estimating Virus Production Rates in Aquatic Systems

Published on: September 22, 2010

13.0K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

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

Published on: December 9, 2015

11.0K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

9.1K

Area of Science:

  • Epidemiology
  • Computational Biology
  • Statistical Modeling

Background:

  • The time-varying basic reproduction number, R0(t), is critical for tracking infectious disease spread.
  • Accurate R0(t) estimation is vital for public health interventions and policy.
  • Existing methods face challenges with covariance underestimation and filter divergence.

Purpose of the Study:

  • To evaluate six methods for estimating R0(t) using synthetic and empirical COVID-19 data.
  • To compare ensemble filter methods with and without inflation techniques.
  • To identify the most robust approach for estimating time-varying transmissibility.

Main Methods:

  • Utilized synthetic data from a stochastic Susceptible-Infected-Recovered (SIR) model.
  • Employed ensemble adjustment Kalman filter (EAKF) and ensemble square root smoother (EnSRS) with adaptive inflation.
  • Compared methods including EpiEstim and EpiFilter using empirical COVID-19 case data.

Main Results:

  • EAKF and EnSRS with adaptive inflation demonstrated superior accuracy in R0(t) estimation.
  • Adaptive inflation effectively mitigated covariance underestimation and filter divergence.
  • These methods proved particularly effective during abrupt changes in transmission rates.

Conclusions:

  • Adaptive inflation techniques enhance the reliability of R0(t) estimation.
  • Improved time-varying parameter inference supports more effective public health strategies.
  • The study highlights the value of advanced filtering methods in infectious disease dynamics.