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

Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

1.7K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.7K
Errors occurring during blood pressure monitoring01:25

Errors occurring during blood pressure monitoring

818
Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...
818
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

478
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
478
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

163
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:
163
Contaminants and Errors01:16

Contaminants and Errors

125
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
125
Random and Systematic Errors01:20

Random and Systematic Errors

11.5K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
11.5K

You might also read

Related Articles

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

Sort by
Same author

Transmission Dynamics of COVID-19 in Ghana and the Impact of Public Health Interventions.

The American journal of tropical medicine and hygiene·2022
Same author

Estimating excess septicaemia mortality and hospitalisation burden associated with influenza in Hong Kong, 1998 to 2019.

Epidemiology and infection·2022
Same author

COVID-19 Vaccination Preferences of University Students and Staff in Hong Kong.

JAMA network open·2022
Same author

Editorial: liver and kidney injury from remdesivir-an issue not as much as its purpose. Authors' reply.

Alimentary pharmacology & therapeutics·2022
Same author

How repeated influenza vaccination effects might apply to COVID-19 vaccines.

The Lancet. Respiratory medicine·2022
Same author

Reproduction Number of the Omicron Variant Triples That of the Delta Variant.

Viruses·2022

Related Experiment Video

Updated: Aug 12, 2025

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

1.3K

Managing sources of error during pandemics.

Simon Cauchemez1, Paolo Bosetti1, Benjamin J Cowling2,3

  • 1Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR2000, Paris, France.

Science (New York, N.Y.)
|February 2, 2023
PubMed
Summary

Modeling future pandemics requires careful consideration of factors highlighted by the COVID-19 pandemic. Understanding these elements is crucial for effective pandemic preparedness and response strategies.

More Related Videos

Remote Laboratory Management: Respiratory Virus Diagnostics
14:56

Remote Laboratory Management: Respiratory Virus Diagnostics

Published on: April 6, 2019

33.2K
Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs
07:13

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Published on: April 9, 2021

4.3K

Related Experiment Videos

Last Updated: Aug 12, 2025

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

1.3K
Remote Laboratory Management: Respiratory Virus Diagnostics
14:56

Remote Laboratory Management: Respiratory Virus Diagnostics

Published on: April 6, 2019

33.2K
Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs
07:13

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Published on: April 9, 2021

4.3K

Area of Science:

  • Epidemiology
  • Public Health
  • Mathematical Modeling

Background:

  • The COVID-19 pandemic revealed critical gaps in current pandemic preparedness and response frameworks.
  • Effective modeling is essential for anticipating and mitigating the impact of future infectious disease outbreaks.

Purpose of the Study:

  • To identify and analyze key considerations for improving future pandemic modeling based on lessons learned from COVID-19.
  • To provide a framework for developing more robust and accurate predictive models for emerging infectious diseases.

Main Methods:

  • Review of epidemiological data and public health responses during the COVID-19 pandemic.
  • Analysis of existing pandemic modeling techniques and their limitations.
  • Synthesis of expert recommendations and scientific literature on pandemic preparedness.

Main Results:

  • Identification of critical factors such as rapid transmission dynamics, asymptomatic spread, and healthcare system strain.
  • Assessment of the need for real-time data integration and adaptive model parameters.
  • Highlighting the importance of socioeconomic factors and behavioral responses in disease propagation.

Conclusions:

  • Future pandemic models must incorporate a wider range of variables, including social and behavioral determinants.
  • Enhanced data infrastructure and interdisciplinary collaboration are vital for accurate and timely pandemic forecasting.
  • The findings underscore the necessity of continuous refinement of modeling approaches to strengthen global health security.