Search arXivSearch

arXiv · 2503.03310

Optimal virulence strategies in epidemiological models with asymptomatic transmission

Abstract

Asymptomatic infection has gained notoriety as an important feature of infectious disease dynamics. Despite increasing attention, there have been few rigorous examinations of how asymptomatic transmission influences pathogen evolution. In this study, we apply evolutionary invasion analysis to compute optimal strategies for viruses evolving in a system with a distinct asymptomatic transmission stage. We ask how pathogens would evolve under three conditions: with an increase in the mean infectious period in the symptomatic state, with an increase in the mean infectious period in the asymptomatic stage, and an increase in proportion proceeding through the ``mild recovery route" (where the symptomatic state was bypassed entirely). We find that an increased proportion of cases moving through a ``mild recovery route" -- which can occur with different host susceptibility or increased public health intervention -- leads to a model structure in which mutant pathogens are transmitted largely through the asymptomatic route, with slightly increased evolved virulence levels. In addition, we find that an increase in the mean infectious period of the symptomatic state has a small overall influence on the fitness of the pathogen, when effective transmission can occur via the asymptomatic route. Further, we find that virulence levels change very slightly for both the asymptomatic and symptomatic populations. In sum, our results highlight the evolutionary implications of variation in host susceptibility and public health interventions in the context of asymptomatic transmission. More generally, the findings speak to the need for more nuanced interrogations of subtle routes of transmission, as they can have profound implications in disease evolution, ecology, and epidemiology.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

C. Brandon Ogbunugafor, Sudam Surasinghe. 2025-03-05. Optimal virulence strategies in epidemiological models with asymptomatic transmission. https://arxiv.org/abs/2503.03310

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Evolution as fitness landscape navigation: concepts, measures, and emerging questions

Fitness landscapes are mappings between genotypes, phenotypes, and fitness that shape evolution. In recent years, empirical work and theoretical models have greatly advanced our understanding of how populations navigate rugged fitness landscapes. Here, we provide a timely review of the theoretical aspects of this field. Its rapidly growing literature employs a wide range of terms, which are sometimes used ambiguously or inconsistently. We therefore begin by defining the major concepts and the field's vocabulary, highlighting our own terminology choices wherever needed. We then review key results on the relationships between epistasis, ruggedness, accessibility, and navigability for genotype-fitness maps, highlighting several complex and sometimes counterintuitive connections that have emerged. Further, we review how the conserved structural properties of the underlying genotype-phenotype map, which can lead to the formation of large connected neutral networks of genotypes, influence dynamics on fitness landscapes. We then compare the two levels to study landscape navigation: the level of genotype-phenotype maps and the level of genotype-fitness maps. Our review leads us to propose a new measure of navigability, based on evolutionary outcomes, that is broadly applicable and overcomes limitations of existing measures. Finally, we highlight examples from the smaller body of work that relaxes the common assumption of fitness-monotonic paths on static landscapes, and discuss how this can fundamentally change the nature of fitness landscape navigation. Throughout the review, we identify directions for future work to fill existing gaps and to synthesize the disparate strands of research within the field.

q-bio.PE

Best Matches in Phylogenetic Networks

Best match graphs (BMGs) were introduced in mathematical phylogenetics to describe the concept of closest relatives for related genes (leaves of rooted tree) in different organisms (defining leaf colors). We generalize this concept here to leaf-colored rooted networks, where least common ancestors are in general neither unique nor comparable. We characterize BMGs of rooted networks as those vertex-colored digraphs that are properly colored and satisfy an easy-to-check condition that we call the sicor-in-hub property. BMGs can be recognized in linear time and an explaining network can be constructed in quadratic time. Analogous results are obtained for reciprocal best match graphs (RBMGs), where an edge $\{x,y\}$ corresponds to pairs of vertices with different color that are mutually closest relatives.

q-bio.PE

Exact Counts of Binary Phylogenetic Networks with Four Reticulations

Phylogenetic networks provide a flexible framework for representing reticulate evolutionary processes, such as hybridization, introgression, recombination, and horizontal gene transfer. However, their combinatorial complexity makes even basic enumeration problems difficult. Building on our previous work for networks with up to three reticulations, we derive an explicit closed-form formula for the number of unrestricted rooted binary phylogenetic networks with four reticulations on \(n\) labeled taxa. Our approach is based on tree-component graphs. We classify the 79 possible component graphs corresponding to networks with four reticulations into ten groups. We then enumerate the networks associated with each group by combining known counts of one-component networks, forests, and networks with fewer reticulations. Summing these contributions yields the desired formula. This result extends the exact enumeration of unrestricted binary phylogenetic networks to four reticulations and further demonstrates the effectiveness of component graphs for systematically organizing and counting increasingly complex network classes.

q-bio.PE