Predicting COVID-19 future trends for different European countries using Pearson correlation

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.