Shreyas S. Joshi (1), Narendra M. Dixit (1)
(1) Indian Institute of Science, India
Objectives:
The outcomes of SARS-CoV-2 infection exhibit considerable heterogeneity among individuals, with the diversity of these outcomes (asymptomatic, mild, severe, chronic etc.) varying across different regions globally, thereby contributing to distinct evolutionary trajectories in the emergence of variants[1]. These patterns are characterized by variations in the number of mutations observed between the emergent strains and the original seed strain [2]. Conventionally, strains incrementally accumulate mutations through transmission chains, eventually resulting in the emergence of a variant. However, the presence of individuals experiencing chronic infection can lead to the emergence of a mutant exhibiting significant mutational divergence [3]. Hence, comprehending the pathway accountable for these varied mutational jumps can facilitate the design of targeted strategies. This emergence is intricately tied to factors associated with interactions and region-specific immunity, which can be modulated through interventions, including physical measures such as social distancing, as well as therapeutic approaches like drugs and vaccines, to achieve desired outcomes.
- Understand the emergence of SARS-CoV-2 variants within the context of regional heterogeneity, considering factors such as interaction dynamics and background immunity.
- Assess the efficacy of interventions such as vaccination and social interaction in preventing the emergence of variants.
Methods:
We employ a multiscale framework based on ordinary differential equations (ODEs) to simulate the influence of region-specific factors on the emergence of variants. To quantify within host diversity, we simulate a set of differential equations that track cells infected with various strains evolved from the initial infecting strain. We incorporate immune heterogeneity by considering the interferon response for innate immunity and the CD8 response for adaptive immunity. Utilizing inputs derived from this model, such as transmission probability and recovery period, we conduct simulations within an epidemiological model. Varying proportions of individuals with certain within host immune heterogeneity accounts for background immunity in the region. The model incorporates vaccination escape through the inclusion of an immune evasive parameter specific to each strain within the host. We account for the nature of mutations arising in the population by considering fitness landscapes related to infectivity, transmission, and immune evasion. These considerations are essential for comprehending the diverse evolutionary mutations observed in variants across the globe.
Results:
Our model accurately captures the mutational jumps observed in variants across the world, aligning with their country-specific emergence patterns. Through exploration of a broad parameter regime, we discover that both interaction dynamics and disease severity exert a critical influence on the magnitude of the mutational jump observed. The magnitude of the mutational jumps becomes more pronounced with these parameter increase, highlighting the significance of targeting chronic individuals in regions where such jumps are prominent, as they are characteristic of this pathway. Nonetheless, strains displaying heightened intermediate transmission fitness redirect attention towards the chains pathway. On the other hand, vaccination restricts the accumulation of mutations by diminishing the pool of susceptible individuals available for transmission. When interactions are limited, transmission rates decrease, thereby rendering vaccination particularly effective, especially in regions with low background immunity (high severity). This effect diminishes as interaction rates increase. Population density significantly contributes to determining the frequency of interactions, exerting a profound effect that varies among regions. As background immunity increases, there is a greater need for accelerated vaccination efforts, as both mild and severe cases contribute to the mutation build-up. This time, the magnitude of the jumps varies significantly and depends on region-specific parameters. Thus, the decision to target either mild or severe cases becomes crucial.
Conclusions:
Our study paves a way for utilizing simple ODE based multiscale models to predict evolution of SARS-COV-2 and similar viruses under various within host and population conditions. We used the framework to study the effect of interaction, severity and vaccination on the extent of the mutational jump in variant. We further suggest control strategies pertaining to these factors to keep the emergence of variants in check.
References:
[1] Chatterjee, B., H. Singh Sandhu, and N.M. Dixit, Modeling recapitulates the heterogeneous outcomes of SARS-CoV-2 infection and quantifies the differences in the innate immune and CD8 T-cell responses between patients experiencing mild and severe symptoms. PLoS Pathog, 2022. 18(6): p. e1010630.
[2] Markov, P.V., et al., The evolution of SARS-CoV-2. Nat Rev Microbiol, 2023. 21(6): p. 361-379.
[3] Harari, S., et al., Drivers of adaptive evolution during chronic SARS-CoV-2 infections. Nat Med, 2022. 28(7): p. 1501-1508.
Reference: PAGE 32 (2024) Abstr 11164 [www.page-meeting.org/?abstract=11164]
Poster: Drug/Disease Modelling - Other Topics