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

155
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:
155
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

437
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:
437
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

132
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
132
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

400
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:  
400
Introduction to Epidemiology01:26

Introduction to Epidemiology

826
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
826
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

151
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
151

You might also read

Related Articles

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

Sort by
Same author

Exploring the effective strategies for allocating limited resources to minimize cholera outbreaks.

Journal of mathematical biology·2026
Same author

Adult dengue vaccination in a low transmission setting: A modelling study in Singapore.

PLoS neglected tropical diseases·2026
Same author

Coupling plankton and cholera dynamics: Insights into outbreak prediction and practical disease management.

PLoS computational biology·2025
Same author

Investigating Tipping and Its Predictability in Noisy Environments: Evaluating the Impact of Temporal and Species Response Correlation.

The American naturalist·2025
Same author

Enhancing Disease Control in Resource-Limited Settings Through Bidirectional Behavioral Responses.

Bulletin of mathematical biology·2025
Same author

Cooperation-conflict dynamics and ecological resilience under environmental disturbances.

Mathematical biosciences and engineering : MBE·2025
Same journal

RNA-ligand complexes and the attenuation of neutral confinement in the evolution of RNA secondary structures.

Journal of the Royal Society, Interface·2026
Same journal

Individual detachment-reintegration events in homing pigeon flocks and the dominance of directional adjustment in their kinematic features.

Journal of the Royal Society, Interface·2026
Same journal

Thermal stress disrupts symbiotic fluid dynamics in bobtail squid.

Journal of the Royal Society, Interface·2026
Same journal

Distinct geometrical landscapes distinguish between modes of tristability in gene regulatory networks.

Journal of the Royal Society, Interface·2026
Same journal

Slow modulation of the contraction patterns in Physarum polycephalum.

Journal of the Royal Society, Interface·2026
Same journal

Moo-ving mountains: grazing agents drive terracette formation on steep hillslopes.

Journal of the Royal Society, Interface·2026
See all related articles

Related Experiment Video

Updated: Jul 30, 2025

Remote Laboratory Management: Respiratory Virus Diagnostics
14:56

Remote Laboratory Management: Respiratory Virus Diagnostics

Published on: April 6, 2019

33.2K

Quantifying optimal resource allocation strategies for controlling epidemics.

Biplab Maity1, Swarnendu Banerjee1,2, Abhishek Senapati1,3

  • 1Agricultural and Ecological Research Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata 700108, India.

Journal of the Royal Society, Interface
|May 17, 2023
PubMed
Summary
This summary is machine-generated.

This study optimizes resource allocation for communicable disease control, finding that intervention effectiveness impacts strategies differently for long-term dynamics versus outbreaks. Optimal strategies depend on investment returns and may require resource sharing in low-resource settings.

Keywords:
disease outbreakintervention strategyproduction functionresource allocation

More Related Videos

Estimating Virus Production Rates in Aquatic Systems
10:49

Estimating Virus Production Rates in Aquatic Systems

Published on: September 22, 2010

12.7K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

8.8K

Related Experiment Videos

Last Updated: Jul 30, 2025

Remote Laboratory Management: Respiratory Virus Diagnostics
14:56

Remote Laboratory Management: Respiratory Virus Diagnostics

Published on: April 6, 2019

33.2K
Estimating Virus Production Rates in Aquatic Systems
10:49

Estimating Virus Production Rates in Aquatic Systems

Published on: September 22, 2010

12.7K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

8.8K

Area of Science:

  • Epidemiology
  • Public Health Policy
  • Resource Allocation

Background:

  • Communicable diseases pose a significant global threat, particularly in lower-income countries with limited resources.
  • Effective disease eradication and burden management strategies are crucial for public health and economic stability.

Purpose of the Study:

  • To quantify optimal resource allocation between reducing disease transmission and improving healthcare infrastructure.
  • To analyze the impact of intervention effectiveness on resource allocation for both long-term disease dynamics and outbreak scenarios.

Main Methods:

  • Mathematical modeling to simulate disease dynamics and intervention impacts.
  • Quantification of resource allocation fractions based on intervention effectiveness.
  • Analysis of long-term and outbreak scenarios to compare optimal strategies.

Main Results:

  • Intervention effectiveness significantly influences optimal resource allocation in both long-term and outbreak scenarios.
  • Optimal allocation for long-term dynamics shows non-monotonic behavior, contrasting with outbreak strategies.
  • The relationship between investment and recovery/transmission rates is key; decreasing returns necessitate resource sharing.

Conclusions:

  • Resource allocation strategies must consider intervention effectiveness and economic factors like diminishing returns.
  • This research provides insights for effective epidemic control in resource-constrained environments.
  • Optimizing interventions requires a nuanced approach tailored to specific disease dynamics and available resources.