While T cell-based vaccines have the potential to provide protection against chronic virus infections, they also have the potential to generate immunopathology following subsequent virus infection. We develop a mathematical model to investigate the conditions under which T cells lead to protection versus adverse pathology. The model illustrates how the balance between virus clearance and immune exhaustion may be disrupted when vaccination generates intermediate numbers of specific CD8 T cells. Surprisingly, our model suggests that this adverse effect of vaccination is largely unaffected by the generation of mutant viruses that evade T cell recognition and cannot be avoided by simply increasing the quality (affinity) or diversity of the T cell response. These findings should be taken into account when developing vaccines against persistent infections.

Since May, 2022, clusters of monkeypox infections have caused global concern. At present, this concern has been tempered by the fact that, even when uncontrolled, the number of infections is growing slowly, indicating a reproductive number (R) not much larger than unity. However, the effect of R on the probability of evolution might not be obvious. We suggest that, compared with zoonotic pathogens with large R values, those pathogens with R values just above 1, such as monkeypox virus, have a higher probability of evolution during the timeframe in which the number of cases remains low. Waiting until the number of cases is high would give monkeypox virus—or any emerging pathogen—the opportunity to adapt substantially to humans.

Currently, vaccines for SARS-CoV-2 and influenza viruses are updated if the new vaccine induces higher antibody-titers to circulating variants than current vaccines. This approach does not account for complex dynamics of how prior immunity skews recall responses to the updated vaccine. We: (i) use computational models to mechanistically dissect how prior immunity influences recall responses; (ii) explore how this affects the rules for evaluating and deploying updated vaccines; and (iii) apply this to SARS-CoV-2. Our analysis of existing data suggests that there is a strong benefit to updating the current SARS-CoV-2 vaccines to match the currently circulating variants. We propose a general two-dose strategy for determining if vaccines need updating as well as for vaccinating high-risk individuals. Finally, we directly validate our model by reanalysis of earlier human H5N1 influenza vaccine studies.

When should vaccines to evolving pathogens such as SARS-CoV-2 be updated? Our computational models address this focusing on updating SARS-CoV-2 vaccines to the currently circulating Omicron variant. Current studies typically compare the antibody titers to the new variant following a single dose of the original-vaccine versus the updated-vaccine in previously immunized individuals. These studies find that the updated-vaccine does not induce higher titers to the vaccine-variant compared with the original-vaccine, suggesting that updating may not be needed. Our models recapitulate this observation but suggest that vaccination with the updated-vaccine generates qualitatively different humoral immunity, a small fraction of which is specific for unique epitopes to the new variant. Our simulations suggest that these new variant-specific responses could dominate following subsequent vaccination or infection with either the currently circulating or future variants. We suggest a two-dose strategy for determining if the vaccine needs updating and for vaccinating high-risk individuals.

We examined how baseline CD4+ T cell repertoire and precursor states impact responses to pathogen infection in humans using primary immunization with yellow fever virus (YFV) vaccine. YFV-specific T cells in unexposed individuals were identified by peptide-MHC tetramer staining and tracked pre- and post-vaccination by tetramers and TCR sequencing. A substantial number of YFV-reactive T cells expressed memory phenotype markers and contained expanded clones in the absence of exposure to YFV. After vaccination, pre-existing YFV-specific T cell populations with low clonal diversity underwent limited expansion, but rare populations with a reservoir of unexpanded TCRs generated robust responses. These altered dynamics reorganized the immunodominance hierarchy and resulted in an overall increase in higher avidity T cells. Thus, instead of further increasing the representation of dominant clones, YFV vaccination recruits rare and more responsive T cells. Our findings illustrate the impact of vaccines in prioritizing T cell responses and reveal repertoire reorganization as a key component of effective vaccination.

Background and objectives: Theory suggests that some types of vaccines against infectious pathogens may lead to the evolution of variants that cause increased harm, particularly when they infect unvaccinated individuals. This theory was supported by the observation that the use of an imperfect vaccine to control Marek's disease virus in chickens resulted in the virus evolving to be more lethal to unvaccinated birds. This raises the concern that the use of some other vaccines may lead to similar pernicious outcomes. We examine that theory with a focus on considering the regimes in which such outcomes are expected. Methodology: We evaluate the plausibility of assumptions in the original theory. The previous theory rested heavily on a particular form of transmission-mortality-recovery trade-off and invoked other assumptions about the pathways of evolution. We review alternatives to mortality in limiting transmission and consider evolutionary pathways that were omitted in the original theory. Results: The regime where the pernicious evolutionary outcome occurs is narrowed by our analysis but remains possible in various scenarios. We propose a more nuanced consideration of alternative models for the within-host dynamics of infections and for factors that limit virulence. Our analysis suggests imperfect vaccines against many pathogens will not lead to the evolution of pathogens with increased virulence in unvaccinated individuals. Conclusions and implications: Evolution of greater pathogen mortality driven by vaccination remains difficult to predict, but the scope for such outcomes appears limited. Incorporation of mechanistic details into the framework, especially regarding immunity, may be requisite for prediction accuracy. Lay Summary: A virus of chickens appears to have evolved high mortality in response to a vaccine that merely prevented disease symptoms. Theory has predicted this type of evolution in response to a variety of vaccines and other interventions such as drug treatment. Under what circumstances is this pernicious result likely to occur? Analysis of the theory in light of recent changes in our understanding of viral biology raises doubts that medicine-driven, pernicious evolution is likely to be common. But we are far from a mechanistic understanding of the interaction between pathogen and host that can predict when vaccines and other medical interventions will lead to the unwanted evolution of more virulent pathogens. So, while the regime where a pernicious result obtains may be limited, caution remains warranted in designing many types of interventions.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated disease, coronavirus disease 2019 (COVID-19), has caused a devastating pandemic worldwide. Here, we explain basic concepts underlying the transition from an epidemic to an endemic state, where a pathogen is stably maintained in a population. We discuss how the number of infections and the severity of disease change in the transition from the epidemic to the endemic phase and consider the implications of this transition in the context of COVID-19.

We have come a long way since the start of the COVID-19 pandemic—from hoarding toilet paper and wiping down groceries to sending our children back to school and vaccinating billions. Over this period, the global community of epidemiologists and evolutionary biologists has also come a long way in understanding the complex and changing dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19. In this Review, we retrace our steps through the questions that this community faced as the pandemic unfolded. We focus on the key roles that mathematical modeling and quantitative analyses of empirical data have played in allowing us to address these questions and ultimately to better understand and control the pandemic.

Effective control of infectious disease outbreaks is an important public health goal. In a number of recent studies, it has been shown how different intervention measures like travel restrictions, school closures, treatment and prophylaxis might allow us to control outbreaks of diseases, such as SARS, pandemic influenza and others. In these studies, control of a single outbreak is considered. It is, however, not clear how one should handle a situation where multiple outbreaks are likely to occur. Here, we identify the best control strategy for such a situation. We further discuss ways in which such a strategy can be implemented to achieve additional public health objectives.

Understanding how immunological memory lasts a lifetime requires quantifying changes in the number of memory cells as well as how their division and death rates change over time. We address these questions by using a statistically powerful mixed-effects differential equations framework to analyze data from two human studies that follow CD8 T cell responses to the yellow fever vaccine (YFV-17D). Models were first fit to the frequency of YFV-specific memory CD8 T cells and deuterium enrichment in those cells 42 days to 1 year post-vaccination. A different dataset, on the loss of YFV-specific CD8 T cells over three decades, was used to assess out of sample predictions of our models. The commonly used exponential and bi-exponential decline models performed relatively poorly. Models with the cell loss following a power law (exactly or approximately) were most predictive. Notably, using only the first year of data, these models accurately predicted T cell frequencies up to 30 years post-vaccination. Our analyses suggest that division rates of these cells drop and plateau at a low level (0.1% per day, ∼ double the estimated values for naive T cells) within one year following vaccination, whereas death rates continue to decline for much longer. Our results show that power laws can be predictive for T cell memory, a finding that may be useful for vaccine evaluation and epidemiological modeling. Moreover, since power laws asymptotically decline more slowly than any exponential decline, our results help explain the longevity of immune memory phenomenologically.