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Related Concept Videos

Data Collection by Experiments01:13

Data Collection by Experiments

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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
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Data Collection by Observations01:08

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Data Collection I01:30

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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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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...
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Seven challenges for model-driven data collection in experimental and observational studies.

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|April 7, 2015
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Summary

Infectious disease models can guide data collection for more efficient hypothesis testing and robust study designs. Integrating dynamic modeling with empirical data collection is crucial for advancing infectious disease research and preparedness.

Keywords:
Data collectionExperimental studiesModelingObservational studies

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Public Health

Background:

  • Infectious disease models serve as hypotheses and tools for understanding disease dynamics.
  • Models have the potential to guide data collection in experimental and observational studies.
  • Synergies between modeling and data collection are not yet the norm in infectious disease research.

Purpose of the Study:

  • To highlight the potential of infectious disease models in guiding data collection.
  • To emphasize the need for closer integration of dynamic modeling and empirical data.
  • To underscore the benefits of overcoming challenges in model-informed data collection.

Main Methods:

  • Review of existing literature and case studies (e.g., Garki project, H1N1 response, T-cell immunodynamics).
  • Conceptual framework emphasizing the role of models in study design.
  • Discussion of challenges and opportunities for integrating modeling with data collection.

Main Results:

  • Infectious disease models can lead to more efficient hypothesis testing and robust study designs.
  • Historical examples demonstrate the successful application of models in informing data collection.
  • A significant gap exists between the potential of models and their current integration into research practices.

Conclusions:

  • Integrating dynamic modeling with empirical data collection is essential for accelerating innovation in infectious disease research.
  • Overcoming current challenges can significantly improve the response to infectious disease threats.
  • Closer collaboration between modelers and empirical researchers is needed to realize the full potential of infectious disease modeling.