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

271
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:
271
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

648
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:
648
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

17.1K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
17.1K
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

282
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...
282
Causality in Epidemiology01:21

Causality in Epidemiology

1.1K
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...
1.1K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.2K
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...
8.2K

You might also read

Related Articles

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

Sort by
Same author

What Lies INSIDE: Chemometric Insights on the Penetration Depth of Near-Infrared Radiation in Spectral Imaging Configurations.

Analytical chemistry·2026
Same author

Advanced Multimodal Imaging in Granulomatous Uveitis: From Differential Diagnosis to Treatment Monitoring and Surgical Integration.

Journal of clinical medicine·2026
Same author

OCT predictors discerning progression to neovascular vs atrophic age-related macular degeneration.

Eye (London, England)·2026
Same author

Impact of AI-enhanced three-dimensional OCT scans on disease activity assessment in patients with nAMD: the RAZORBILL study.

The British journal of ophthalmology·2026
Same author

Impact of visit schedule on estimated success rates in glaucoma surgical studies.

The British journal of ophthalmology·2026
Same author

Defining a standards framework for ophthalmology real-world data methodologies through an expert-led Delphi consensus.

Eye (London, England)·2026

Related Experiment Video

Updated: Nov 1, 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

10.8K

Forecasting the COVID-19 Epidemic by Integrating Symptom Search Behavior Into Predictive Models: Infoveillance Study.

Alessandro Rabiolo1, Eugenio Alladio2, Esteban Morales3

  • 1Department of Ophthalmology, Gloucestershire Hospitals NHS Foundation Trust, Cheltenham, United Kingdom.

Journal of Medical Internet Research
|June 22, 2021
PubMed
Summary

Integrating Google Trends data with traditional metrics improves COVID-19 epidemic forecasting. This approach enhances prediction accuracy for confirmed cases and deaths, aiding public health surveillance.

Keywords:
COVID-19Google TrendsSARS-CoV-2Shiny web applicationbig datacoronavirusdigital healthinfodemiologyinfoveillancepredictive modelssymptomstime series

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.3K
Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.5K

Related Experiment Videos

Last Updated: Nov 1, 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

10.8K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.3K
Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.5K

Area of Science:

  • Epidemiology
  • Public Health
  • Data Science

Background:

  • Previous research indicated a link between web search trends and COVID-19 metrics.
  • The predictive power of models incorporating digital search trends for COVID-19 remained uncertain.

Purpose of the Study:

  • To examine the correlation between Google Trends symptom searches and COVID-19 cases/deaths.
  • To develop predictive models for the COVID-19 epidemic using both search trends and traditional metrics.

Main Methods:

  • Developed an interactive web application for analyzing Google Trends and COVID-19 data across 188 countries.
  • Utilized Principal Component Analysis (PCA) for dimensionality reduction and three time series models for 14-day forecasting.
  • Compared prediction accuracy using Root Mean Square Error (RMSE), evaluating models with and without Google Trends data.

Main Results:

  • Optimal time lag for search trends varied by country and topic, generally within 15 days.
  • Models incorporating Google Trends showed improved PC1 prediction compared to those without (median RMSE 1.56 vs. 1.87).
  • 7-day moving average models significantly outperformed raw data models (median RMSE 0.90 vs. 2.27).

Conclusions:

  • Digital search trends can enhance COVID-19 epidemic nowcasting and forecasting.
  • These search trends can serve as a valuable component of COVID-19 surveillance systems.
  • A free web application is available for real-time outbreak prediction and epidemic dynamics estimation.