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

131
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:
131
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

371
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:
371
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

563
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...
563
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
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...
43
Censoring Survival Data01:09

Censoring Survival Data

98
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
98

You might also read

Related Articles

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

Sort by
Same author

Modelling climatic and temporal dynamics of dengue transmission in Bangladesh using deep learning models.

PLOS global public health·2026
Same author

Bifurcations and optimal control in Nipah virus epidemiology.

PloS one·2026
Same author

State-by-state influenza outbreaks and oversee: A Markov chain study of California and North Carolina, USA.

PLOS global public health·2025
Same author

Assessing the efficacy of cash incentive policies in enhancing remittance inflows: Evidence from Bangladesh.

PloS one·2025
Same author

Bifurcation analysis of an influenza A (H1N1) model with treatment and vaccination.

PloS one·2025
Same author

Mathematical Study of a Resource-Based Diffusion Model with Gilpin-Ayala Growth and Harvesting.

Bulletin of mathematical biology·2022

Related Experiment Video

Updated: Jul 8, 2025

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

10.7K

Downscaling epidemiological time series data for improving forecasting accuracy: An algorithmic approach.

Mahadee Al Mobin1,2, Md Kamrujjaman1

  • 1Department of Mathematics, University of Dhaka, Dhaka, Bangladesh.

Plos One
|December 14, 2023
PubMed
Summary

Data scarcity in healthcare hinders forecasting. A new Stochastic Bayesian Downscaling (SBD) algorithm generates realistic synthetic data from aggregated datasets, improving epidemiological predictions and reducing forecasting errors significantly.

More Related Videos

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.0K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

Related Experiment Videos

Last Updated: Jul 8, 2025

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

10.7K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.0K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

Area of Science:

  • Epidemiology
  • Data Science
  • Biostatistics

Background:

  • Healthcare and epidemiological datasets often suffer from scarcity and discontinuity, complicating decision-making and forecasting.
  • Traditional forecasting methods like ARIMA and SARIMA struggle with aggregated data, leading to unsatisfactory results.
  • Artificial data synthesis offers a promising solution for overcoming data limitations in time series analysis.

Purpose of the Study:

  • To introduce a novel Stochastic Bayesian Downscaling (SBD) algorithm for regenerating downscaled time series from aggregated data.
  • To preserve the statistical characteristics and aggregated sums of the original data during the downscaling process.
  • To demonstrate the algorithm's utility in epidemiological time series analysis using real-world case studies.

Main Methods:

  • Development of the Stochastic Bayesian Downscaling (SBD) algorithm, employing a Bayesian approach.
  • Application of the SBD algorithm to generate downscaled time series from aggregated epidemiological data.
  • Validation of synthesized data against original data for statistical properties, trend, seasonality, and residuals.

Main Results:

  • The SBD algorithm successfully regenerated downscaled time series from aggregated data, maintaining key statistical properties.
  • Case studies using Dengue and COVID-19 data from Bangladesh showed strong agreement between synthesized and original data.
  • Forecasting Dengue infections improved significantly, with error reduction up to 72.76% using synthetic data compared to aggregated data.

Conclusions:

  • The Stochastic Bayesian Downscaling (SBD) algorithm is effective in addressing data scarcity and discontinuity in epidemiological time series.
  • Synthesized data generated by SBD accurately reflects the statistical nuances of the original data.
  • SBD enhances forecasting accuracy, offering a valuable tool for public health decision-making and scenario planning.