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:

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.