Maria A. Riolo

Can vaccine legacy explain the British pertussis resurgence

Pertussis incidence has been rising in some countries, including the UK, despite sustained high vaccine coverage. We questioned whether it is possible to explain the resurgence without recourse to complex hypotheses about pathogen evolution, subclinical infections, or trends in surveillance efficiency. In particular, we investigated the possibility that the resurgence is a consequence of the legacy of incomplete pediatric immunization, in the context of cohort structure and age-dependent transmission. We constructed a model of pertussis transmission in England and Wales based on data on age-specific contact rates and historical vaccine coverage estimates. We evaluated the agreement between model-predicted and observed patterns of age-specific pertussis incidence under a variety of assumptions regarding the duration of immunity. Under the assumption that infection-derived immunity is complete and lifelong, and regardless of the duration of vaccine-induced immunity, the model consistently predicts a resurgence of pertussis incidence comparable to that which has been observed. Interestingly, no resurgence is predicted when infection- and vaccine-derived immunities wane at the same rate. These results were qualitatively insensitive to rates of primary vaccine failure. We conclude that the alarming resurgence of pertussis among adults and adolescents in Britain and elsewhere may simply be a legacy of historically inadequate coverage employing imperfect vaccines. Indeed, we argue that the absence of resurgence at this late date would be more surprising. Our analysis shows that careful accounting for age dependence in contact rates and susceptibility is prerequisite to the identification of which features of pertussis epidemiology want additional explanation.

Epidemiological evidence for herd immunity induced by acellular pertussis vaccines

In a series of elegant experiments on baboons, Warfel et al. conclude that acellular pertussis vaccines (aP) prevent disease but fail to protect against transmissible infection (1). The authors speculate that this fact may explain the resurgence of pertussis in some countries (2). Although the animal model of Warfel et al. is a true breakthrough, we question the soundness of their extrapolation to transmission in human populations. Indeed, much available epidemiological evidence argues against it.

First-principles multiway spectral partitioning of graphs

We consider the minimum-cut partitioning of a graph into more than two parts using spectral methods. While there exist well-established spectral algorithms for this problem that give good results, they have traditionally not been well motivated. Rather than being derived from first principles by minimizing graph cuts, they are typically presented without direct derivation and then proved after the fact to work. In this paper, we take a contrasting approach in which we start with a matrix formulation of the minimum cut problem and then show, via a relaxed optimization, how it can be mapped onto a spectral embedding defined by the leading eigenvectors of the graph Laplacian. The end result is an algorithm that is similar in spirit to, but different in detail from, previous spectral partitioning approaches. In tests of the algorithm we find that it outperforms previous approaches on certain particularly difficult partitioning problems.

Combating pertussis resurgence: One booster vaccination schedule does not fit all

Significance Pertussis has reemerged as a major public health concern in many countries where vaccine uptake remains high and pertussis has been considered well controlled until recently. In our paper, we address the important scientific and practical problem of developing optimal booster vaccination schedules by using a genetic algorithm. Our results argue that booster vaccination schedules developed based on misdiagnosis of the problem are likely to be epidemiologically ineffective and economically costly. Pertussis has reemerged as a major public health concern in many countries where it was once considered well controlled. Although the mechanisms responsible for continued pertussis circulation and resurgence remain elusive and contentious, many countries have nevertheless recommended booster vaccinations, the timing and number of which vary widely. Here, using a stochastic, age-stratified transmission model, we searched for cost-effective booster vaccination strategies using a genetic algorithm. We did so assuming four hypothesized mechanisms underpinning contemporary pertussis epidemiology: (I) insufficient coverage, (II) frequent primary vaccine failure, (III) waning of vaccine-derived protection, and (IV) vaccine “leakiness.” For scenarios I–IV, successful booster strategies were identified and varied considerably by mechanism. Especially notable is the inability of booster schedules to alleviate resurgence when vaccines are leaky. Critically, our findings argue that the ultimate effectiveness of vaccine booster schedules will likely depend on correctly pinpointing the causes of resurgence, with misdiagnosis of the problem epidemiologically ineffective and economically costly.

Co-Adaptation and the Emergence of Structure

Co-adaptation (or co-evolution), the parallel feedback process by which agents continuously adapt to the changes induced by the adaptive actions of other agents, is a ubiquitous feature of complex adaptive systems, from eco-systems to economies. We wish to understand which general features of complex systems necessarily follow from the (meta)-dynamics of co-adaptation, and which features depend on the details of particular systems. To begin this project, we present a model of co-adaptation (“The Stigmergy Game”) which is designed to be as a priori featureless as possible, in order to help isolate and understand the naked consequences of co-adaptation. In the model, heterogeneous, co-adapting agents, observe, interact with and change the state of an environment. Agents do not, ab initio, directly interact with each other. Agents adapt by choosing among a set of random “strategies,” particular to each agent. Each strategy is a complete specification of an agents actions and payoffs. A priori, all environmental states are equally likely and all strategies have payoffs that sum to zero, so without co-adaptation agents would on average have zero “wealth”. Nevertheless, the dynamics of co-adaptation generates a structured environment in which only a subset of environmental states appear with high probability (niches) and in which agents accrue positive wealth. Furthermore, although there are no direct agent-agent interactions, there are induced non-trivial inter-agent interactions mediated by the environment. As a function of the population size and the number of possible environmental states, the system can be in one of three dynamical regions. Implications for a basic understanding of complex adaptive systems are discussed.

Competition and immigration lead to clusters of similar species, not trait separation

Patterns of trait distribution among competing species can potentially reveal the processes that allow them to coexist. It has been recently proposed that competition may drive the spontaneous emergence of niches comprising clusters of similar species, in contrast with the dominant paradigm of greater-than-chance species differences. However, current clustering theory relies largely on heuristic rather than mechanistic models. Furthermore, studies of models incorporating demographic stochasticity and immigration, two key players in community assembly, did not observe clusters. Here we demonstrate clustering under partitioning of resources, partitioning of environmental gradients, and a competition-colonization tradeoff. We show that clusters are robust to demographic stochasticity, and can persist under immigration. While immigration may sustain clusters that are otherwise transient, too much dilutes the pattern. In order to detect and quantify clusters in nature, we introduce and validate metrics which have no free parameters nor require arbitrary trait binning, and weigh species by their abundances rather than relying on a presence-absence count. By generalizing beyond the circumstances where clusters have been observed, our study contributes to establishing them as an update to classical trait patterning theory. Author Summary Species traits determine how they compete with each other. As such, patterns in the distributions of traits in a community of competing species may reveal the processes responsible for coexistence. One central idea in theoretical ecology is that the strength of competition relates to similarity in species needs and strategies, and therefore if competition plays out at short timescales, coexisting species should be more different than expected by chance. However, recent theory suggests that competition may lead species to temporarily self-organize into groups with similar traits. Here we show that this clustering is a generic feature of competitive dynamics, which is robust to demographic stochasticity and can be indefinitely maintained by immigration. We show that clustering arises whether species coexist by partitioning resources, environmental preferences, or through tradeoffs in life-history strategies. We introduce and validate metrics that, given species traits and abundances, determine whether they are clustered, and if so, how many clusters occur. By showing the generality of self-organized species clusters and providing a tools for their detection, our study contributes to updating classical ideas about how competition shapes communities, and motivates searches for them in nature.Patterns of trait distribution among coexisting species can potentially reveal the processes by which species assemble into communities. The dominant paradigm, that competition causes species to differ more than expected by chance, has limited empirical support. Here we show that when competition acts in concert with stochasticity and dispersal, communities spontaneously organize into clusters of similar species. Further, we show clusters feature generally across different niche mechanisms, and even under the confounding influence of environmental filters. While clusters have been previously reported as transient or else persisting under restricted circumstances, our results show they persist broadly under immigration. Previous stochastic niche studies missed this effect because they only explored extremely low immigration. We provide parameter-free metrics for detecting clusters in field data, which we validate using simulations. We conclude that clusters are a more general pattern than overdispersion, and trait-based searches for niche differentiation may be more successful once they account for this fact.

Core pertussis transmission groups in England and Wales: A tale of two eras

The recent resurgence of pertussis in England and Wales has been marked by infant deaths and rising cases in teens and adults. To understand which age cohorts are most responsible for these trends, we employed three separate statistical methods to analyze high-resolution pertussis reports from 1982 to 2012. The fine-grained nature of the time-series allowed us to describe the changes in age-specific incidence and contrast the transmission dynamics in the 1980s and during the resurgence era. Our results identified infants and school children younger than 10 years of age as a core group, prior to 2002: pertussis incidence in these populations was predictive of incidence in other age groups. After 2002, no core groups were identifiable. This conclusion is independent of methodology used. Because it is unlikely that the underlying contact patterns substantially changed over the study period, changes in predictability likely result from the introduction of more stringent diagnostics tests that may have inadvertently played a role in masking the relative contributions of core transmission groups.

Role Of Competition In The Strain Structure Of Rotavirus Under Invasion And Reassortment

The role of competitive interactions in the formation and coexistence of viral strains remains unresolved. Neglected aspects of existing strain theory are that viral pathogens are repeatedly introduced from animal sources and readily exchange their genes. The combined effect of introduction and reassortment opposes strain structure, in particular the predicted stable coexistence of antigenically differentiated strains under strong frequency-dependent selection mediated by cross-immunity. Here we use a stochastic model motivated by rotavirus, the most common cause of childhood diarrheal mortality, to investigate serotype structure under these conditions. We describe a regime in which the transient coexistence of distinct strains emerges despite only weak cross-immunity, but is disturbed by invasions of new antigenic segments that reassort into existing backgrounds. We find support for this behavior in global rotavirus sequence data and present evidence for the displacement of new strains towards open antigenic niches. Our work extends previous work to bacterial and viral pathogens that share these rotavirus-like characteristics, with important implications for the effects of interventions such as vaccination on strain composition, and for the understanding of the factors promoting emergence of new subtypes.

Efficient method for estimating the number of communities in a network

While there exist a wide range of effective methods for community detection in networks, most of them require one to know in advance how many communities one is looking for. Here we present a method for estimating the number of communities in a network using a combination of Bayesian inference with a novel prior and an efficient Monte Carlo sampling scheme. We test the method extensively on both real and computer-generated networks, showing that it performs accurately and consistently, even in cases where groups are widely varying in size or structure.