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Related Concept Videos

Sampling Plans01:23

Sampling Plans

169
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Convenience Sampling Method00:55

Convenience Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
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Systematic Sampling Method01:17

Systematic Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Censoring Survival Data01:09

Censoring Survival Data

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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Watershed Planning within a Quantitative Scenario Analysis Framework
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Equity-centered adaptive sampling in sub-sewershed wastewater surveillance using census data.

Amita Muralidharan1, Rachel Olson1, C Winston Bess1

  • 1Department of Civil and Environmental Engineering, University of California Davis Davis California 95616 USA hbischel@ucdavis.edu.

Environmental Science : Water Research & Technology
|October 28, 2024
PubMed
Summary

Sub-city wastewater monitoring enhances infectious disease surveillance by using a geospatial tool to equitably represent diverse populations. This method ensures accurate public health data, even with reduced sampling, prioritizing vulnerable groups.

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Area of Science:

  • Environmental science
  • Public health
  • Epidemiology

Background:

  • Sub-city wastewater monitoring offers localized infectious disease surveillance, complementing city-wide data.
  • Equitable population representation in sampling frameworks is challenged by demographic data and zone misalignment.

Purpose of the Study:

  • Develop a geospatial tool for probabilistic demographic assignment to sub-city sampling zones.
  • Evaluate population subgroup representativeness for COVID-19 wastewater surveillance.
  • Demonstrate scenario planning to prioritize vulnerable populations.

Main Methods:

  • Utilized a geospatial analysis tool to assign census block demographic data to sub-city sampling zones.
  • Monitored SARS-CoV-2 in wastewater (November 2021-September 2022) in Davis, California.
  • Evaluated four scenarios reducing sampling zones by 25% and 50%, randomly or prioritizing older adults.

Main Results:

  • Sub-city wastewater data correlated strongly with centralized treatment plant data (Spearman's correlation 0.909).
  • Prioritizing representation increased coverage of individuals over 65 and Black or African American populations.
  • Reduced sampling showed minimal impact on data correlation, especially when prioritizing older adults.

Conclusions:

  • Probabilistic demographic assignment aids in adapting sampling locations to prioritize vulnerable groups.
  • Sub-city wastewater surveillance can maintain data integrity while optimizing sampling strategies.
  • This approach enhances equitable public health surveillance for infectious diseases.