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

You might also read

Related Articles

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

Sort by
Same author

Positional variability of a small intestinal stromal tumor: a case report.

Journal of surgical case reports·2025
Same author

Comparisons of in vitro Fick's first law, lipolysis, and in vivo rat models for oral absorption on BCS II drugs in SNEDDS.

International journal of nanomedicine·2019
Same author

Regulation of fibroblast growth factor 8 (FGF8) in chicken embryonic stem cells differentiation into spermatogonial stem cells.

Journal of cellular biochemistry·2017
Same author

CREPT and p15RS regulate cell proliferation and cycling in chicken DF-1 cells through the Wnt/β-catenin pathway.

Journal of cellular biochemistry·2017
Same author

Partner change, birth interval and risk of pre-eclampsia: a paradoxical triangle.

Paediatric and perinatal epidemiology·2007
Same author

Partner change and perinatal outcomes: a systematic review.

Paediatric and perinatal epidemiology·2007

Related Experiment Video

Updated: Nov 2, 2025

Efficient SARS-CoV-2 Quantitative Reverse Transcriptase PCR Saliva Diagnostic Strategy utilizing Open-Source Pipetting Robots
11:11

Efficient SARS-CoV-2 Quantitative Reverse Transcriptase PCR Saliva Diagnostic Strategy utilizing Open-Source Pipetting Robots

Published on: February 11, 2022

4.7K

Data-driven test strategy for COVID-19 using machine learning: A study in Lahore, Pakistan.

Chuanli Huang1,2, Min Wang3, Warda Rafaqat1

  • 1State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei, 230027, PR China.

Socio-Economic Planning Sciences
|June 14, 2021
PubMed
Summary

A new data-driven strategy significantly improved COVID-19 test detection rates and resource utilization. This adaptive approach enhances pandemic response by optimizing testing based on real-time data and local conditions.

Keywords:
COVID-19Logistic regressionMachine learningPolicy makingSpatial analysisTest strategyTime series analysis

More Related Videos

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
08:26

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

1.9K
Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
08:48

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

Published on: February 16, 2022

3.1K

Related Experiment Videos

Last Updated: Nov 2, 2025

Efficient SARS-CoV-2 Quantitative Reverse Transcriptase PCR Saliva Diagnostic Strategy utilizing Open-Source Pipetting Robots
11:11

Efficient SARS-CoV-2 Quantitative Reverse Transcriptase PCR Saliva Diagnostic Strategy utilizing Open-Source Pipetting Robots

Published on: February 11, 2022

4.7K
Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
08:26

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

1.9K
Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
08:48

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

Published on: February 16, 2022

3.1K

Area of Science:

  • Epidemiology
  • Public Health
  • Data Science

Background:

  • Current COVID-19 testing strategies exhibit weaknesses due to dynamic pandemic changes.
  • Understanding the temporal and spatial effects of data selection is crucial for effective testing.

Purpose of the Study:

  • To analyze the limitations of existing COVID-19 test strategies.
  • To propose a self-adaptive, data-driven testing strategy for dynamic pandemic conditions.
  • To evaluate the impact of data-driven selection on test performance over time and space.

Main Methods:

  • A mathematical framework defined the test strategy.
  • Logistic regression and priority ranking machine learning models were employed.
  • Analysis utilized real COVID-19 test data from Lahore (March-July).
  • Performance was assessed using Area Under the Curve (AUC), time series, and spatial cross-tests.

Main Results:

  • The proposed data-driven strategy increased the positive detection rate from 2.54% to 28.18% and recall from 8.05% to 89.35%.
  • Optimal test resource utilization achieved 89.35% positive case detection with 48.17% of the original test volume.
  • The strategy demonstrated self-adaptability to pandemic evolution, with local data proving most effective.

Conclusions:

  • A generalized data-driven testing strategy is recommended for improved global pandemic response.
  • Refining COVID-19 data systems for spatial granularity is essential for local application effectiveness.