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Microsoft Excel is a powerful tool for statistical analysis, including calculating Pearson's correlation coefficient, which measures the strength and direction of a linear relationship between two continuous variables. Pearson's correlation coefficient, often denoted as "r," ranges from -1 to 1. A value close to 1 indicates a strong positive correlation, meaning as one variable increases, the other does too. A value close to -1 indicates a strong negative correlation, implying...
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Predicting COVID-19 future trends for different European countries using Pearson correlation.

Jihan Muhaidat1, Aiman Albatayneh2, Ramez Abdallah3

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Forecasting COVID-19 trends requires understanding country correlations. This study used Pearson correlation to analyze European COVID-19 case data, revealing unexpected relationships beyond geographical proximity.

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Area of Science:

  • Epidemiology
  • Public Health
  • Biostatistics

Background:

  • Accurate forecasting of COVID-19 cases is crucial for public health strategies.
  • Past prediction models often failed due to insufficient reliable data.
  • Monitoring other countries' trends has yielded inaccurate results due to unestablished correlations.

Purpose of the Study:

  • To explore correlations in daily COVID-19 case numbers among European countries.
  • To determine if geographical proximity influences COVID-19 case trend correlations.
  • To provide data-driven insights for better pandemic preparedness and response planning.

Main Methods:

  • Analysis of official daily COVID-19 case data from selected European countries over 76 weeks.
  • Application of the Pearson correlation technique to quantify relationships between country case trends.
  • Utilized correlation coefficient (r) thresholds to define correlation strength (very strong, strong, moderate, weak/none).

Main Results:

  • Identified strong correlations between some neighboring and non-geographically close European countries.
  • Observed correlations between countries on opposite sides of Europe, such as Belgium and Armenia.
  • Found weak or no correlation for several countries including France, Iceland, Israel, Kosovo, San Marino, Spain, Sweden, and Turkey.

Conclusions:

  • COVID-19 case trends exhibit complex correlations not solely dictated by geographical proximity.
  • The findings challenge the assumption that only neighboring countries need close monitoring for pandemic response.
  • Pearson correlation analysis offers a valuable tool for identifying inter-country relationships to inform public health policy.