Populations and Evolution (q-bio.PE)
Tue, 12 Sep 2023
1.Social \textit{vs.} individual age-dependent costs of imperfect vaccination
Authors:Fabio A. C. C. Chalub, Paulo Doutor, Paula Patrício, Maria do Céu Soares
Abstract: In diseases with long-term immunity, vaccination is known to increase the average age at infection as a result of the decrease in the pathogen circulation. This implies that a vaccination campaign can have negative effects when a disease is more costly (financial or health-related costs) for higher ages. This work considers an age-structured population transmission model with imperfect vaccination. Our aim is to compare the social and individual costs of vaccination, assuming that disease costs are age-dependent. A model coupling pathogen deterministic dynamics for a population consisting of juveniles and adults, both assumed to be rational agents, is introduced. The parameter region for which vaccination has a positive social impact is fully characterized and the Nash equilibrium of the vaccination game is obtained. Finally, collective strategies designed to promote voluntary vaccination, without compromising social welfare, are discussed.
2.Single-cell mutational burden distributions in birth-death processes
Authors:Christo Morison, Dudley Stark, Weini Huang
Abstract: Genetic mutations are footprints of cancer evolution and reveal critical dynamic parameters of tumour growth, which otherwise are hard to measure in vivo. The mutation accumulation in tumour cell populations has been described by various statistics, such as site frequency spectra (SFS) from bulk or single-cell data, as well as single-cell division distributions (DD) and mutational burden distributions (MBD). An integrated understanding of these distributions obtained from different sequencing information is important to illuminate the ecological and evolutionary dynamics of tumours, and requires novel mathematical and computational tools. We introduce dynamical matrices to analyse and unite the SFS, DD and MBD based on a birth-death process. Using the Markov nature of the model, we derive recurrence relations for the expectations of these three distributions. While recovering classic exact results in pure-birth cases for the SFS and the DD through our new framework, we also derive a new expression for the MBD as well as approximations for all three distributions when death is introduced, confirming our results with stochastic simulations. Moreover, we demonstrate a natural link between the SFS and the single-cell MBD, and show that the MBD can be regenerated through the DD. Surprisingly, the single-cell MBD is mainly driven by the stochasticity arising in the DD, rather than the extra stochasticity in the number of mutations at each cell division.