A Data-Driven Sliding-window Pairwise Comparative Approach for the Estimation of Transmission Fitness of SARS-CoV-2 Variants and Construction of the Evolution Fitness Landscape

Preprint Link to the Manuscript Data Source

Table of Contents

• Overview
• Installation
• Key Insights
• Project Structure

Overview

This repository contains the Differential Population Growth Rate (DPGR) model, a novel sliding-window, data-driven approach to estimate and compare the transmission fitness of SARS-CoV-2 variants. DPGR utilizes pairwise comparisons across specific time windows to quantify relative fitness advantages between viral strains, creating a dynamic fitness landscape that reflects the evolutionary trends of Variants of Concern (VOCs) such as Omicron and Delta. This generalized approach is appilcable to any viral surveillance dataset of exponentially growing viral populations.

By addressing key issues with sampling biases and allowing for real-time epidemiological insights, DPGR provides a robust framework that complements transmission fitness estimation.

Installation

Clone this repository to run locally:

git clone https://github.com/QinLab/DPGR2024.git
cd DPGR

Use Conda to create an environment named myenv and activate it:

conda create --name myenv python=3.8
conda activate myenv

Now, install dependencies from requirements.txt:

pip install -r requirements.txt

Key Insights

Here, we highlight some key insights from applying the DPGR model:

Transmission Fitness Variability by Region

Omicron consistently exhibited higher transmission fitness over Delta across various geographic levels (countries and continents).

Transmission Fitness Estimation in the Country Level

Transmission Fitness Estimation in the Continent Level

Transmission Fitness Estimation for Omicron Sublineages in the Continents

Pairwise Distance Matrix

As the method is pairwise, we can directly estimate the transmission fitness of the adjacent variants like DPGRAlpha,Beta, DPGRBeta, Delta and DPGRDelta.Omicron. To infer the transmission fitness of the non-adjacent variants, DPGRAlpha,Delta, DPGRAlpha, Omicron, the property of logarithms (log(a/b) = log(a/c) + log(c/b) is used. Thus, we can infer the growth advantages of non-adjacent variants. The initial variant Alpha is considered as the root variant. Using the pairwise distance matrix, the relative transmission fitness landscape and transmission fitness stair are constructed.
Here, is the Pairwise Distance matrix of SARS-CoV-2 Variants of Concern for the WHO labels:

Tranmission Fitness Evolution Landscape

The fitness landscape generated through pairwise DPGR analyses shows how each successive variant of concerns of SARS-CoV-2 dominates over the other. This model provides a clear visualization of the evolutionary trajectory in fitness between major VOCs.

Tranmission Fitness Stair of the Co-Circulating Variants

Fitness Stair of the WHO Varinats of Concern(VOCs)

Fitness Stair of the Omicron Sublineages(BA.1* -BA.5*)

Neighbor Join Tree

The neighbor Join tree helps to understand the dynamic evolution of variants in terms of relative transmission fitness.
Neighbor Join Tree for the WHO Varinats for United States:

Project Structure

├── Code             # Jupyter notebooks for analysis and visualization
├── Figures          # Visualizations
├── Files            # Estimated Fitness values
├── requirements.txt # Dependency list
└── README.md         # Project documentation