Eradicating infectious disease using weakly transmissible vaccines.

Viral vaccines have had remarkable positive impacts on human health as well as the health of domestic animal populations. Despite impressive vaccine successes, however, many infectious diseases cannot yet be efficiently controlled or eradicated through vaccination, often because it is impossible to vaccinate a sufficient proportion of the population. Recent advances in molecular biology suggest that the centuries-old method of individual-based vaccine delivery may be on the cusp of a major revolution. Specifically, genetic engineering brings to life the possibility of a live, transmissible vaccine. Unfortunately, releasing a highly transmissible vaccine poses substantial evolutionary risks, including reversion to high virulence as has been documented for the oral polio vaccine. An alternative, and far safer approach, is to rely on genetically engineered and weakly transmissible vaccines that have reduced scope for evolutionary reversion. Here, we use mathematical models to evaluate the potential efficacy of such weakly transmissible vaccines. Our results demonstrate that vaccines with even a modest ability to transmit can significantly lower the incidence of infectious disease and facilitate eradication efforts. Consequently, weakly transmissible vaccines could provide an important tool for controlling infectious disease in wild and domestic animal populations and for reducing the risks of emerging infectious disease in humans.

Optimal vaccination in a stochastic epidemic model of two non-interacting populations.

Developing robust, quantitative methods to optimize resource allocations in response to epidemics has the potential to save lives and minimize health care costs. In this paper, we develop and apply a computationally efficient algorithm that enables us to calculate the complete probability distribution for the final epidemic size in a stochastic Susceptible-Infected-Recovered (SIR) model. Based on these results, we determine the optimal allocations of a limited quantity of vaccine between two non-interacting populations. We compare the stochastic solution to results obtained for the traditional, deterministic SIR model. For intermediate quantities of vaccine, the deterministic model is a poor estimate of the optimal strategy for the more realistic, stochastic case.

Coevolutionary immune system dynamics driving pathogen speciation.

We introduce and analyze a within-host dynamical model of the coevolution between rapidly mutating pathogens and the adaptive immune response. Pathogen mutation and a homeostatic constraint on lymphocytes both play a role in allowing the development of chronic infection, rather than quick pathogen clearance. The dynamics of these chronic infections display emergent structure, including branching patterns corresponding to asexual pathogen speciation, which is fundamentally driven by the coevolutionary interaction. Over time, continued branching creates an increasingly fragile immune system, and leads to the eventual catastrophic loss of immune control.

Measuring and modeling behavioral decision dynamics in collective evacuation.

Identifying and quantifying factors influencing human decision making remains an outstanding challenge, impacting the performance and predictability of social and technological systems. In many cases, system failures are traced to human factors including congestion, overload, miscommunication, and delays. Here we report results of a behavioral network science experiment, targeting decision making in a natural disaster. In a controlled laboratory setting, our results quantify several key factors influencing individual evacuation decision making in a controlled laboratory setting. The experiment includes tensions between broadcast and peer-to-peer information, and contrasts the effects of temporal urgency associated with the imminence of the disaster and the effects of limited shelter capacity for evacuees. Based on empirical measurements of the cumulative rate of evacuations as a function of the instantaneous disaster likelihood, we develop a quantitative model for decision making that captures remarkably well the main features of observed collective behavior across many different scenarios. Moreover, this model captures the sensitivity of individual- and population-level decision behaviors to external pressures, and systematic deviations from the model provide meaningful estimates of variability in the collective response. Identification of robust methods for quantifying human decisions in the face of risk has implications for policy in disasters and other threat scenarios, specifically the development and testing of robust strategies for training and control of evacuations that account for human behavior and network topologies.

Population-expression models of immune response.

The immune response to a pathogen has two basic features. The first is the expansion of a few pathogen-specific cells to form a population large enough to control the pathogen. The second is the process of differentiation of cells from an initial naive phenotype to an effector phenotype which controls the pathogen, and subsequently to a memory phenotype that is maintained and responsible for long-term protection. The expansion and the differentiation have been considered largely independently. Changes in cell populations are typically described using ecologically based ordinary differential equation models. In contrast, differentiation of single cells is studied within systems biology and is frequently modeled by considering changes in gene and protein expression in individual cells. Recent advances in experimental systems biology make available for the first time data to allow the coupling of population and high dimensional expression data of immune cells during infections. Here we describe and develop population-expression models which integrate these two processes into systems biology on the multicellular level. When translated into mathematical equations, these models result in non-conservative, non-local advection-diffusion equations. We describe situations where the population-expression approach can make correct inference from data while previous modeling approaches based on common simplifying assumptions would fail. We also explore how model reduction techniques can be used to build population-expression models, minimizing the complexity of the model while keeping the essential features of the system. While we consider problems in immunology in this paper, we expect population-expression models to be more broadly applicable.

Diversity of T-cell responses.

Using a dynamic model we study the adaptive immune response to a sequence of two infections. We incorporate lymphocyte diversity by modeling populations as continuous distributions in a multi-dimensional space. As expected, memory cells generated by the primary infection invoke a rapid response when the secondary infection is identical (homologous). When the secondary infection is different (heterologous), the memory cells have a positive effect or no effect at all depending on the similarity of the infections. This model displays 'original antigenic sin' where the average effector affinity for the heterologous infection is lower than it would be for a naive response, but in cases with original antigenic sin we see a reduction in pathogen density. We model pathology resulting from the immune system itself (immunopathology) but find that in cases of original antigenic sin, immunopathology is still reduced. Average effector affinity is not an accurate measure of the quality of an immune response. The effectivity, which is the total pathogen killing rate, provides a direct measure of quality. This quantity takes both affinity and magnitude into account.

On the role of CD8 T cells in the control of persistent infections.

The control of pathogen density during infections is typically assumed to be the result of a combination of resource limitation (loss of target cells that the pathogen can infect), innate immunity, and specific immunity. The contributions of these factors have been considered in acute infections, which are characterized by having a short duration. What controls the pathogen during persistent infections is less clear, and is complicated by two factors. First, specific immune responses become exhausted if they are subject to chronic stimulation. Exhaustion has been best characterized for CD8 T cell responses, and occurs through a combination of cell death and loss of functionality of surviving cells. Second, new nonexhausted T cells can immigrate from the thymus during the infection, and may play a role in the control of the infection. In this article, we formulate a partial-differential-equation model to describe the interaction between these processes, and use this model to explore how thymic influx and exhaustion might affect the ability of CD8 T cell responses to control persistent infections. We find that although thymic influx can play a critical role in the maintenance of a limited CD8 T cell response during persistent infections, this response is not sufficiently large to play a significant role in controlling the infection. In doing so, our results highlight the importance of resource limitation and innate immunity in the control of persistent infections.

Vaccination by delayed treatment of infection.

Two medical interventions allow us to combat infectious diseases: vaccination which can be administered well in advance of exposure, and antimicrobials which are most often administered contemporaneously with exposure. In this paper we show how they can, in principle, be combined - with infection followed by treatment being used as a form of vaccination. We use mathematical models to examine how appropriately administered antimicrobial treatment following natural infection can be used to reduce the pathology caused by the infection, and also generate long-lasting immunological memory to the pathogen. The models explore the tradeoff between reduction in pathology and strength of immunization. This tradeoff suggests a limited treatment window during which antimicrobial treatment can be started and provide both amelioration of disease symptoms and long-term immunity. This approach may be particularly well suited to combat the emergence of novel pandemic influenza infections particularly for individuals such as medical healthcare professionals at greatest risk for exposure during the initial stages of a pandemic.

The suppression of immune system disorders by passive attrition.

Exposure to infectious diseases has an unexpected benefit of inhibiting autoimmune diseases and allergies. This is one of many fundamental fitness tradeoffs associated with immune system architecture. The immune system attacks pathogens, but also may (inappropriately) attack the host. Exposure to pathogens can suppress the deleterious response, at the price of illness and the decay of immunity to previous diseases. This "hygiene hypothesis" has been associated with several possible underlying biological mechanisms. This study focuses on physiological constraints that lead to competition for survival between immune system cell types. Competition maintains a relatively constant total number of cells within each niche. The constraint implies that adding cells conferring new immunity requires loss (passive attrition) of some cells conferring previous immunities. We consider passive attrition as a mechanism to prevent the initial proliferation of autoreactive cells, thus preventing autoimmune disease. We see that this protection is a general property of homeostatic regulation and we look specifically at both the IL-15 and IL-7 regulated niches to make quantitative predictions using a mathematical model. This mathematical model yields insight into the dynamics of the "Hygiene Hypothesis," and makes quantitative predictions for experiments testing the ability of passive attrition to suppress immune system disorders. The model also makes a prediction of an anti-correlation between prevalence of immune system disorders and passive attrition rates.

Robustness and fragility in immunosenescence.

We construct a model to study tradeoffs associated with aging in the adaptive immune system, focusing on cumulative effects of replacing naive cells with memory cells. Binding affinities are characterized by a stochastic shape space model. System loss arising from an individual infection is associated with disease severity, as measured by the total antigen population over the course of an infection. We monitor evolution of cell populations on the shape space over a string of infections, and find that the distribution of losses becomes increasingly heavy-tailed with time. Initially this lowers the average loss: the memory cell population becomes tuned to the history of past exposures, reducing the loss of the system when subjected to a second, similar infection. This is accompanied by a corresponding increase in vulnerability to novel infections, which ultimately causes the expected loss to increase due to overspecialization, leading to increasing fragility with age (i.e., immunosenescence). In our model, immunosenescence is not the result of a performance degradation of some specific lymphocyte, but rather a natural consequence of the built-in mechanisms for system adaptation. This "robust, yet fragile" behavior is a key signature of Highly Optimized Tolerance.