https://github.com/nikbarb810/covid_growth_rate_390.51

Exploring Covid Growth Rate of European Population using genetic data analysis
https://github.com/nikbarb810/covid_growth_rate_390.51

bioinformatics data-analysis r rcpp

Last synced: 8 days ago
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Exploring Covid Growth Rate of European Population using genetic data analysis

• Host: GitHub
• URL: https://github.com/nikbarb810/covid_growth_rate_390.51
• Owner: nikbarb810
• Created: 2023-06-20T20:38:37.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-16T12:34:26.000Z (about 1 year ago)
• Last Synced: 2024-11-08T15:07:18.893Z (8 days ago)
• Topics: bioinformatics, data-analysis, r, rcpp
• Homepage:
• Size: 3.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Estimating Rate of Growth of SARS-CoV-2 in the European Population

## Overview

This project focuses on estimating the growth rate of the SARS-CoV-2 virus in the European population using genetic data analysis. The goal is to identify the parameter responsible for observed genetic changes.

## Methodology

### Data Preparation

- Read and format observation and simulation files.

- Simulations consist of 10,000 datasets with 50 sequences each.

### Pairwise Difference Calculation

- Calculate the average number of pairwise differences between sequences.

- Optimize performance using Rcpp-based C++ implementation.

### Parameter Estimation

#### Average Number of Pairwise Differences (k Estimate)

- `k_estimate` function calculates average pairwise differences for simulations.

#### Rcpp Implementation for Performance

- Utilize C++ with Rcpp to enhance performance.

#### Calculation of Other Parameters

- Calculate "w" and "Tajima's D" based on average pairwise differences.

### Normalization of Parameters

- Normalize parameters for simulations and observations.

### Parameter Comparison and Approximation

- Compare parameters between simulations and observations.

#### Parameter Association

- Associate parameters with distances.

- Select the 500 closest parameters from simulations.

#### Parameter Estimation

- Approximate the parameter that generated the observed dataset based on the 500 closest parameters.

## Results

- The estimated growth rate parameter for SARS-CoV-2 in the European population is approximately 98.4.

- Compared to the mean parameter value of 185.5 from simulations, this suggests a decrease in the virus's growth rate over time.

## Conclusion

This project provides insights into the growth rate of SARS-CoV-2 in the European population using genetic data analysis. The estimation indicates a decrease in the growth rate, which may have implications for understanding the virus's evolution and spread.

## Author

- [Nikolaos Barmparousis](https://github.com/nikbarb810)

## License

This project is licensed under the [MIT License](LICENSE).