Related Experiment Video
Updated: May 4, 2026
12:18
Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST
Published on: April 23, 2015
11.8K
Systems approaches to animal disease surveillance and resource allocation: methodological frameworks for behavioral
Karl M Rich1, Matthew J Denwood2, Alistair W Stott3
1Department of International Economics, Norwegian Institute of International Affairs (NUPI), Oslo, Norway.
Plos One
|December 19, 2013
Summary
Understanding animal disease surveillance requires examining resource allocation, cost-effectiveness, and actor behavior. This study analyzes these interactions to improve disease control and surveillance strategies.
Area of Science:
- Veterinary Epidemiology
- Animal Health Economics
- Behavioral Science
Background:
- Growing demand for effective animal disease surveillance systems.
- Limited research on the interplay of resource allocation, cost-effectiveness, and actor behavior in surveillance.
- Surveillance decisions and disease dynamics are influenced by contextual factors impacting system efficiency.
Purpose of the Study:
- To investigate the interactions between resource allocation, cost-effectiveness, and behavioral factors in livestock disease surveillance.
- To identify key behavioral aspects within animal health surveillance systems.
- To present a framework for analyzing these interactions to enhance decision-making.
Main Methods:
- Review of novel methodologies for analyzing behavioral aspects in surveillance.
- Development of a generic analytical framework.
- Application of the framework with exemplar results.
Main Results:
- Identification of critical behavioral considerations in animal health surveillance.
- Demonstration of a framework's utility in analyzing surveillance system dynamics.
- Exemplar results illustrating the impact of behavioral factors on cost-effectiveness.
Conclusions:
- Integrating resource allocation, cost-effectiveness, and behavioral analysis is crucial for optimizing surveillance systems.
- The proposed framework aids in understanding complex interactions within surveillance.
- This approach can guide improved disease control and surveillance decision-making in livestock.
Related Concept Videos
01:26Principles of Disease Surveillance
853
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...
853
01:18Steps in Outbreak Investigation
779
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:
779
01:23Investigation of Disease Outbreaks
74
Multistate foodborne outbreaks pose significant public health risks and require meticulous investigation to identify sources and implement control measures. The Centers for Disease Control and Prevention (CDC) utilizes a dynamic seven-step process for these investigations, integrating data from laboratories, interviews, and environmental assessments to protect public health.Outbreak Detection: The detection of multistate outbreaks typically begins with PulseNet, the CDC's national laboratory...
74
01:25Statistical Methods for Analyzing Epidemiological Data
1.3K
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:
1.3K
01:21Behavior Modification
1.0K
Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
A real-world application of operant conditioning principles is applied...
A real-world application of operant conditioning principles is applied...
1.0K
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
359
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
359