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

Pulmonary Tuberculosis IV01:26

Pulmonary Tuberculosis IV

423
Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
Several diagnostic approaches are used to detect TB. The conventional method is the Tuberculin Skin Test (TST), also known as the Mantoux test. However, this method has...
423
Pulmonary Tuberculosis V01:28

Pulmonary Tuberculosis V

465
Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
Latent tuberculosis infection occurs when TB bacteria are present in a person's body, but are not causing illness or symptoms. It is not contagious, and preventive treatment is crucial to avoid the...
465
Pulmonary Tuberculosis I01:29

Pulmonary Tuberculosis I

741
Tuberculosis, often called TB, is a contagious illness primarily caused by Mycobacterium tuberculosis. It mainly affects the lung parenchyma but can also impact other body parts.
Causative Organism
The primary infectious agent causing tuberculosis is Mycobacterium tuberculosis, a slow-growing, acid-fast, aerobic rod that exhibits sensitivity to heat and ultraviolet light. Instances of Mycobacterium bovis and Mycobacterium avium contributing to the development of TB infection are rare.
Mode of...
741
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

416
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:
416
Pulmonary Tuberculosis II01:28

Pulmonary Tuberculosis II

1.2K
Tuberculosis, or TB, is a bacterial infectious disease caused by Mycobacterium tuberculosis. While its primary impact is on the lungs, leading to pulmonary tuberculosis, it can also affect various other organs, a condition referred to as extrapulmonary tuberculosis.
Here is a detailed explanation of its pathophysiology:
Transmission: The process begins when a person inhales droplet nuclei containing M. tuberculosis. These are typically released into the air when an individual with pulmonary or...
1.2K
Pulmonary Tuberculosis III01:31

Pulmonary Tuberculosis III

795
Tuberculosis (TB) is a contagious infection primarily affecting the lung parenchyma but which can also affect other body parts. TB can be classified based on disease development, presentation, and the affected anatomical site.
The first classification is based on the development of the disease, and it includes the following categories:
795

You might also read

Related Articles

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

Sort by
Same author

Comparative performance of EQ-5D-5L bolt-ons in China and the Netherlands: results from the EQ-DAPHNIE project.

Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation·2026
Same author

Adult-Onset Multisystem Langerhans Cell Histiocytosis: Atypical Skeletal and Endocrine Manifestations.

Cureus·2026
Same author

Epstein-Barr virus orchestrates spatial reorganization and immunomodulation in the classic Hodgkin lymphoma tumor microenvironment.

Cell reports. Medicine·2026
Same author

Same-Slide Spatial Multi-Omics Integration with IN-DEPTH Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment.

Cancer discovery·2026
Same author

Authors' reply: Re: Yang et al. The road to therapy for myeloid sarcoma: navigating the complexities of subclonal MAPK/ERK mutations and clonal evolution.

The journal of pathology. Clinical research·2025
Same author

Understanding the pathology workforce: motivations, job satisfaction, and training perspectives in Germany.

Virchows Archiv : an international journal of pathology·2025
Same journal

Host immunity and recurrent <i>Clostridioides difficile</i> infection: a comprehensive review.

Pathogens and global health·2026
Same journal

Genetic variability of SARS-CoV-2 XFG lineage and its parental lineages.

Pathogens and global health·2026
Same journal

Study of <i>Leptospira</i> spp. in rodents and water sources: implications for public health in rural environments from Buenos Aires province, Argentina.

Pathogens and global health·2026
Same journal

Designing out rabies: a conceptual urban planning framework for dog-mediated rabies control in informal settlements.

Pathogens and global health·2026
Same journal

Trends in hospitalizations in adults with HIV infection in Spain over 25 years.

Pathogens and global health·2026
Same journal

What can be learnt from India's success in controlling <i>Anopheles stephensi</i> in urban systems.

Pathogens and global health·2026
See all related articles

Related Experiment Video

Updated: Dec 20, 2025

Analysis of 18FDG PET/CT Imaging as a Tool for Studying Mycobacterium tuberculosis Infection and Treatment in Non-human Primates
10:04

Analysis of 18FDG PET/CT Imaging as a Tool for Studying Mycobacterium tuberculosis Infection and Treatment in Non-human Primates

Published on: September 5, 2017

19.1K

Forecasting tuberculosis using diabetes-related google trends data.

Leonie Frauenfeld1, Dominik Nann1, Zita Sulyok2

  • 1Institute for Pathology and Neuropathology, Eberhard Karls University, University Hospital of Tübingen , Tübingen 72076, Germany.

Pathogens and Global Health
|May 27, 2020
PubMed
Summary
This summary is machine-generated.

Integrating diabetes-related Google searches into tuberculosis (TB) forecasting models significantly improved prediction accuracy. This approach enhances infectious disease surveillance, especially in data-limited settings.

Keywords:
DiabetesForecastingGoogle TrendsSurveillanceTuberculosis

More Related Videos

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
06:46

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19

Published on: July 5, 2022

3.1K

Related Experiment Videos

Last Updated: Dec 20, 2025

Analysis of 18FDG PET/CT Imaging as a Tool for Studying Mycobacterium tuberculosis Infection and Treatment in Non-human Primates
10:04

Analysis of 18FDG PET/CT Imaging as a Tool for Studying Mycobacterium tuberculosis Infection and Treatment in Non-human Primates

Published on: September 5, 2017

19.1K
A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
06:46

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19

Published on: July 5, 2022

3.1K

Area of Science:

  • Epidemiology
  • Public Health Surveillance
  • Computational Biology

Background:

  • Tuberculosis (TB) and diabetes are significant global health challenges.
  • Forecasting infectious disease outbreaks relies on timely and accurate data.
  • Traditional surveillance methods may face limitations in data availability and timeliness.

Purpose of the Study:

  • To forecast tuberculosis (TB) case numbers by integrating online search data.
  • To evaluate the effectiveness of using diabetes-related Google Trends data to enhance TB prediction models.
  • To assess model performance in simulated data-poor surveillance scenarios.

Main Methods:

  • Collected weekly TB case data and Google Trends data for 'diabetes' in Germany (2014-2019).
  • Developed and cross-validated Seasonal Autoregressive Moving Average (SARIMA) and Neural Network Autoregressive (NNAR) models.
  • Compared traditional models with models extended by incorporating Google Trends data, including in a data-poor simulation.

Main Results:

  • Google Trends-extended models showed lower Root Mean Squared Errors (RMSE) compared to traditional models during cross-validation.
  • In data-poor simulations, GTD-extended models significantly outperformed traditional models (p < 0.001).
  • The integration of online search data consistently improved prediction accuracy across all evaluated parameters.

Conclusions:

  • Online activity-based data, specifically diabetes-related searches, can substantially improve TB forecasting.
  • This methodology offers a valuable tool for enhancing infectious disease surveillance, particularly in resource-limited settings.
  • Further validation studies are recommended to confirm the generalizability and robustness of these findings.