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Related Concept Videos

Correlation01:09

Correlation

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Related Experiment Video

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The correlation between Google trends and salmonellosis.

Ming-Yang Wang1,2,3, Nai-Jun Tang4,5

  • 1Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin, 300070, China.

BMC Public Health
|August 21, 2021
PubMed
Summary

Google Trends data can predict Salmonella outbreaks. Search terms like "foods," "hotel," "poor sanitation," "blueberries," and "hypovolemic shock" are key indicators for early salmonellosis detection.

Keywords:
CorrelationGoogle trendsPredictorSalmonellosissalmonella

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Area of Science:

  • Epidemiology
  • Public Health
  • Data Science

Background:

  • Salmonella infection (salmonellosis) is a prevalent infectious disease causing gastroenteritis and other complications.
  • Internet search data offers an economical and logistically advantageous method for disease outbreak surveillance.
  • This study explores the utility of Google Trends for monitoring salmonellosis in the USA.

Purpose of the Study:

  • To determine the relationship between salmonellosis incidence and Google search trends in the USA.
  • To identify specific Google search terms that can serve as early indicators or correlates of Salmonella outbreaks.
  • To evaluate the predictive value of internet search data for public health surveillance.

Main Methods:

  • Utilized National Outbreak Reporting System (NORS) data for reported salmonellosis cases in the USA (2004-2017).
  • Collected Google search term data related to salmonellosis from Google Trends for the same period.
  • Employed cross-correlation and multiple regression analyses to assess relationships between search trends and reported cases.

Main Results:

  • Multiple Google search terms showed temporal correlations with salmonellosis outbreaks.
  • Search terms like "foods" predicted outbreaks when appearing earlier.
  • "Hotel," "poor sanitation," "blueberries," and "hypovolemic shock" were significant predictors when search terms coincided with outbreaks.
  • "Ice cream" was a predictor when search terms appeared after outbreaks.

Conclusions:

  • Google Trends data is a valuable tool for salmonellosis surveillance.
  • Specific search terms, including "hotel," "poor sanitation," "blueberries," and "hypovolemic shock," are identified as important predictors.
  • Increased search activity for these terms may signal potential salmonellosis outbreaks, enabling timely public health interventions.