Showing posts with label viral fitness. Show all posts

Sunday, February 9, 2025

Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data

Article
Open access
Published: 07 January 2025

Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data

Nature Communications volume 16, Article number: 441 (2025) Cite this article

2967 Accesses
106 Altmetric

Need to compare this with DPGR, CNN-fitness regression and SHAP analysis

Thursday, July 18, 2024

Summary of COVID-19 Impacts and the DPGR Method

COVID-19 Impact Factors

COVID-19 has shown varying impacts on transmission and hospitalization rates across different countries due to a complex interplay of factors:

Population Demographics: Countries with a higher proportion of elderly individuals experience more severe outcomes and increased hospitalization rates.
Socioeconomic Conditions: Limited healthcare infrastructure and resources make managing the pandemic more challenging.
Environmental Factors: Temperature and population density contribute to transmission differences.
Government Interventions: The timing and effectiveness of interventions, including lockdowns, testing strategies, and vaccination campaigns, significantly affect the virus's spread and impact on healthcare systems.
Cultural Practices and Social Norms: Adherence to preventive measures varies across nations.
Global Connectivity: The interconnectedness through global travel and trade networks has influenced early spread patterns, with highly connected nations often experiencing earlier outbreaks.

These multifaceted factors create unique epidemiological profiles for each country, resulting in diverse outcomes in terms of COVID-19 transmission and hospitalization rates.

DPGR (Differential Population Growth Rate) Method

The DPGR method offers a novel approach to estimating the transmission fitness of SARS-CoV-2 variants by using viral strains as internal controls to mitigate sampling biases and handle variability over time.

Key Features and Strengths:

Sliding Window Approach: Selects appropriate periods where the log-linear assumption holds, adapting to changing dynamics between variants.
Pairwise Comparisons: Uses one variant as an internal control against another, providing relative measurements that reduce the impact of temporal and geographic biases.
Flexibility in Time Windows: Allows for analysis during specific phases of the pandemic and can be applied at different geographical scales.
Applicability to Sublineages: Can analyze major variants and sublineages.
Indirect Comparisons: Uses intermediate variants for comparisons when variants don't coexist in the same period.
Continuous Monitoring: Can be applied continuously as new data becomes available.

Comparison with Other Methods:

R0 and Rt Methods: DPGR can differentiate between specific variants using genomic data, while R0 and Rt rely on incidence data lacking variant-specific information.
Phylogenetic Approaches: Avoids limitations like low genetic variability and geographic biases by focusing on population-level growth rates.
Multinomial Logistic Regression: Offers a simpler, more interpretable log-linear regression method.

Advantages:

Internal Control: Mitigates sampling biases.
Flexibility: Adaptable to different scales and time periods.
Visual Representation: Constructs fitness landscapes for intuitive visualization of variant trajectories.

Limitations:

Time Window Dependency: Relies on suitable time windows for the log-linear assumption.
Pairwise Nature: May become cumbersome with many variants.
Exponential Growth Assumption: May not always hold, especially during later stages of variant waves.
Data Quality Sensitivity: Depends on the quality and consistency of genomic surveillance data.
Limited Causal Inference: Identifies fitness differences without explaining genetic or biological bases.

Future Directions:

Integration with Genetic Data: Combine with specific mutations to identify genetic determinants.
Incorporation of Environmental Factors: Include variables like vaccination rates and climate factors.
Real-time Application: Develop tools for rapid application to emerging data.
Comparative Studies: Systematic comparisons with other methods.
Extension to Other Pathogens: Apply DPGR to other rapidly evolving pathogens.

U.S. COVID-19 Vaccination Data (March-May 2022)

Primary Vaccination Series Completion: Approximately 70% of the U.S. population.
Adults with at Least One Dose: About 75%.
State Variations: Over 80% in states like Rhode Island and Vermont; less than 60% in states like Wyoming and Alabama.
Higher Vaccination Rates Among:
- Democrats (91%)
- Older adults aged 65+ (91%)
- College graduates (88%)
Lower Vaccination Rates Among:
- Those under 65 without health insurance (61%)
- Republicans (66%)

These percentages represent the primary vaccination series and do not include booster shots or updated vaccines available later.

Citations:

Thursday, July 11, 2024

Eric Volz, sars-cov-2 logistic growth

Volz 2023 show logistic growth does not fit well with Omicro data

https://www.nature.com/articles/s41576-023-00610-z

GPT:

The key findings of Erik Volz's paper, "Fitness, Growth and Transmissibility of SARS-CoV-2 Genetic Variants," published in Nature Reviews Genetics, 2023, include:

Genomic Surveillance and Variant Detection:
- The massive global sequencing effort for SARS-CoV-2 has enabled rapid detection and assessment of new variants. This has led to the development of new strategies for characterizing the fitness and transmissibility of emerging variants, utilizing both classic population genetics models and contemporary epidemiological and phylodynamic analyses.
Evolution and Adaptation of SARS-CoV-2:
- The COVID-19 pandemic saw multiple waves driven by distinct SARS-CoV-2 lineages, each with significant changes in transmissibility, antigenicity, and pathogenicity. These variants, such as Alpha, Beta, and Gamma, were identified through genomic surveillance before increases in infections and hospitalizations were evident in epidemiological data.
Fitness and Selection Coefficient:
- The concept of fitness in population genetics, particularly the selection coefficient, is crucial for understanding how quickly advantageous variants become dominant. The logistic growth model, despite its simplicity, has been applied to estimate the selection coefficient for various SARS-CoV-2 variants, providing a fast means to measure growth across many variants.
Complexity in Variant Growth:
- Variant growth is influenced by factors beyond differential fitness, such as genetic drift, founder effects, and epidemiological or behavioral factors. The paper highlights the challenges in disentangling these effects to accurately measure variant fitness.
Mechanisms of Fitness Advantage:
- The paper discusses the importance of virological evidence in evaluating the mechanisms behind a variant's fitness advantage, such as non-synonymous changes in the spike protein that influence virus fitness.
Consistent Growth Across Regions:
- Confidence in a variant's fitness advantage increases with consistent growth observed across different regions and demographics. This was exemplified by the growth patterns of the Delta variant, which showed consistent high growth rates across multiple regions and demographic groups.
Phylodynamic Analysis:
- Phylodynamic modeling provides independent validation of epidemiological trends by analyzing genetic diversity and epidemic history. This approach has been used to estimate the growth rates and reproduction numbers of various SARS-CoV-2 lineages.
Epidemiological Models and Data Integration:
- The integration of epidemiological models with genomic data has been essential in estimating variant-specific reproduction numbers and understanding the dynamics of competing variants. These models consider the impact of transmissibility, immune evasion, and generation times on variant growth.
Future Perspectives:
- The paper concludes that as COVID-19 transitions into a manageable infectious disease, genomic surveillance will remain crucial for monitoring and responding to new variants. Methods developed during the SARS-CoV-2 pandemic will be applicable to other pathogens, enhancing future public health responses.

These findings emphasize the importance of genomic surveillance and advanced modeling techniques in understanding and managing the evolution and spread of SARS-CoV-2 and other emerging pathogens.

Phylodynamic analysis is a powerful method used to understand the growth and spread of pathogen variants by integrating phylogenetic (genetic) and epidemiological data. Here's how phylodynamic analysis can estimate variant growth trends:

1. Genetic Data Collection and Phylogenetic Tree Construction

Genetic Sequencing: First, viral genomes from infected individuals are sequenced.
Phylogenetic Trees: These sequences are then used to construct a phylogenetic tree, which shows the evolutionary relationships between different viral strains. Each branch point (node) represents a common ancestor.

2. Modeling Population Dynamics

Coalescent Models: These models trace the genealogy of sampled genomes backward in time to estimate the rate at which new infections occur. The shape and branching patterns of the tree reflect the population dynamics of the virus.
Birth-Death Models: These models estimate the rate of new infections (births) and recoveries or deaths (deaths) over time, providing insights into how quickly the virus spreads and declines.

3. Estimating Growth Rates

Exponential Growth Detection: During an epidemic's exponential growth phase, the tree will show many closely related sequences branching out rapidly. This abundance of recent, closely related lineages indicates a high growth rate.
Effective Reproduction Number (Rt): Phylodynamic models can estimate Rt, which is the average number of secondary infections caused by a single infected individual. Changes in Rt over time can indicate how control measures or changes in virus transmissibility impact growth.

4. Assessing Selection Pressure and Variant Fitness

Mutation Analysis: By examining specific mutations within the phylogenetic tree, researchers can identify which variants are spreading more rapidly. Mutations that frequently appear in branches leading to many descendant sequences suggest a fitness advantage.
Selective Sweeps: If a new variant with advantageous mutations spreads rapidly, it will dominate the phylogenetic tree, indicating a selective sweep. The speed and extent of this sweep can be quantified to estimate the variant's fitness.

5. Temporal and Geographic Trends

Temporal Signal: Phylodynamic methods can incorporate the sampling dates of sequences to provide a temporal framework. This allows estimation of when a variant emerged and how its prevalence changed over time.
Geographic Spread: By combining phylogenetic data with geographic information, researchers can track how variants spread between regions. Clades (branches) of the tree can be associated with different locations, showing the movement and expansion of variants.

6. Integration with Epidemiological Data

Corroboration with Case Data: Phylodynamic estimates can be validated and refined using epidemiological data, such as case counts, hospitalizations, and deaths. This combined approach provides a comprehensive view of variant dynamics.
Real-Time Analysis: Continuous genomic surveillance and real-time phylodynamic analysis enable early detection of emerging variants with significant growth advantages, guiding public health responses.

Example: Delta Variant Growth in the UK

In the paper by Volz, phylodynamic analysis was applied to understand the growth of the Delta variant in the UK. The study constructed phylogenetic trees from sampled genomes and used birth-death models to estimate the effective reproduction number of Delta. The consistent high growth rates across different regions and demographics provided robust evidence of Delta's fitness advantage.

Phylodynamic analysis thus offers a robust framework for estimating the growth trends of viral variants by leveraging genetic data to infer epidemiological dynamics, helping to predict and mitigate the impact of emerging pathogens.