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Biomedical subjects

Simon A Levin

Publications and source records attributed to Simon A Levin.

31 records · Page 2Linked to original sources
PubMed2004

Dynamical resonance can account for seasonality of influenza epidemics.

Influenza incidence exhibits strong seasonal fluctuations in temperate regions throughout the world, concentrating the mortality and morbidity burden of the disease into a few months each year. The cause of influenza's seasonality has remained elusive. Here we show that the large oscillations in incidence may be caused by undetectably small seasonal changes in the influenza transmission rate that are amplified by dynamical resonance.

Disease Outbreaks↗
PubMed2004

Spread of two linked social norms on complex interaction networks.

In this paper, we study the spread of social norms, such as rules and customs that are components of human cultures. We consider the spread of two social norms, which are linked through individual behaviors. Spreading social norms depend not only on the social network structure, but also on the learning system. We consider four social network structures: (1) complete mixing, in which each individual interacts with the others at random, (2) lattice, in which each individual interacts with its neighbors with some probability and with the others at random, (3) power-law network, in which a few influential people have more social contacts than the others, and (4) random graph network, in which the number of contacts follows a Poisson distribution. Using the lattice model, we also investigate the effect of the small-world phenomenon on the dynamics of social norms. In our models, each individual learns a social norm by trial and error (individual learning) and also imitates the other's social norm (social learning). We investigate how social network structure and learning systems affect the spread of two linked social norms. Our main results are: (1) Social learning does not lead to coexistence of social norms. Individual learning produces coexistence, and the dynamics of coexistence depend on which social norms are learned individually. (2) Social norms spread fastest in the power-law network model, followed by the random graph model, the complete mixing model, the two-dimensional lattice model and the one-dimensional lattice. (3) We see a "small world effect" in the one-dimensional model, but not in two dimensions.

Computer Simulation↗
PubMed2004

Optimal nitrogen-to-phosphorus stoichiometry of phytoplankton.

Redfield noted the similarity between the average nitrogen-to-phosphorus ratio in plankton (N:P = 16 by atoms) and in deep oceanic waters (N:P = 15; refs 1, 2). He argued that this was neither a coincidence, nor the result of the plankton adapting to the oceanic stoichiometry, but rather that phytoplankton adjust the N:P stoichiometry of the ocean to meet their requirements through nitrogen fixation, an idea supported by recent modelling studies. But what determines the N:P requirements of phytoplankton? Here we use a stoichiometrically explicit model of phytoplankton physiology and resource competition to derive from first principles the optimal phytoplankton stoichiometry under diverse ecological scenarios. Competitive equilibrium favours greater allocation to P-poor resource-acquisition machinery and therefore a higher N:P ratio; exponential growth favours greater allocation to P-rich assembly machinery and therefore a lower N:P ratio. P-limited environments favour slightly less allocation to assembly than N-limited or light-limited environments. The model predicts that optimal N:P ratios will vary from 8.2 to 45.0, depending on the ecological conditions. Our results show that the canonical Redfield N:P ratio of 16 is not a universal biochemical optimum, but instead represents an average of species-specific N:P ratios.

Ecosystem↗
PubMed2004

Persistent colonization and the spread of antibiotic resistance in nosocomial pathogens: resistance is a regional problem.

Infections with antibiotic-resistant bacteria (ARB) in hospitalized patients are becoming increasingly frequent despite extensive infection-control efforts. Infections with ARB are most common in the intensive care units of tertiary-care hospitals, but the underlying cause of the increases may be a steady increase in the number of asymptomatic carriers entering hospitals. Carriers may shed ARB for years but remain undetected, transmitting ARB to others as they move among hospitals, long-term care facilities, and the community. We apply structured population models to explore the dynamics of ARB, addressing the following questions: (i) What is the relationship between the proportion of carriers admitted to a hospital, transmission, and the risk of infection with ARB? (ii) How do frequently hospitalized patients contribute to epidemics of ARB? (iii) How do transmission in the community, long-term care facilities, and hospitals interact to determine the proportion of the population that is carrying ARB? We offer an explanation for why ARB epidemics have fast and slow phases and why resistance may continue to increase despite infection-control efforts. To successfully manage ARB at tertiary-care hospitals, regional coordination of infection control may be necessary, including tracking asymptomatic carriers through health-care systems.

Bacterial Infections↗
PubMed2004

Coupling ecology and evolution: malaria and the S-gene across time scales.

Malaria has long been a scourge to humans. The exceptionally high mortality in some regions has led to strong selection for resistance, even at the cost of increased risk of potentially fatal red blood cell deformities in some offspring. In particular, genes that confers resistance to malaria when they appear in heterozygous individuals are known to lead to sickle-cell anemia, or other blood diseases, when they appear in homozygous form. Thus, there is balancing selection against the evolution of resistance, with the strength of that selection dependent upon malaria prevalence. Over longer time scales, the increased frequency of resistance in a population might be expected to decrease the frequency of malaria and reduce selection for resistance. However, possession of the sickle-cell gene leads to longer-lasting parasitaemia in heterozygote individuals, and therefore the presence of resistance may actually increase infection prevalence. In this paper, we explore the interplay among these processes, operating over very different time scales. In particular, we show that on the fast time scale of malarial dynamics, the disease level reaches an equilibrium; on the slower, evolutionary time scale, this equilibrium tracks gene frequency. We analyze the slow time scale dynamics to investigate the impact of malaria on the evolution of resistance.

Algorithms↗
PubMed2004

Competitive coexistence in a dynamic landscape.

This paper investigates the effect of a dynamic landscape on the persistence of many interacting species. We develop a multi-species community model with an evolving landscape in which the creation and destruction of habitat are dynamic and local in space. Species interactions are also local involving hierarchical competitive trade-offs. We show that dynamic landscapes can reverse the trend of increasing species richness with higher fragmentation observed in static landscapes. The increase in the species-area exponent from a homogeneous to a fragmented landscape does not occur when dynamics are turned on. Thus, temporal aspects of the processes that generate and destroy habitat appear dominant relative to spatial characteristics. We also demonstrate, however, that temporal and spatial aspects interact to influence the persistence time of individual species, and therefore, rank-abundance curves. Specifically, persistence in the model increases in habitats with faster local turnover because of the presence of dynamic corridors.

Animals↗
PubMed2003

Traveling waves in a model of influenza A drift.

Between major pandemics, the influenza A virus changes its antigenic properties by accumulating point mutations (drift) mainly in the RNA genes that code for the surface proteins hemagglutinin (HA) and neuraminidase (NA). The successful strain (variant) that will cause the next epidemic is selected from a reduced number of progenies that possess relatively high transmissibility and the ability to escape from the immune surveillance of the host. In this paper, we analyse a one-dimensional model of influenza A drift (Z. Angew. Math. Mech. 76 (2) (1996) 421) that generalizes the classical SIR model by including mutation as a diffusion process in a phenotype space of variants. The model exhibits traveling wave solutions with an asymptotic wave speed that matches well those obtained from numerical simulations. As exact solutions for these waves are not available, asymptotic estimates for the amplitudes of infected and recovered classes are provided through an exponential approximation based on the smallness of the diffusion constant. Through this approximation, we find simple scaling properties to several parameters of relevance to the epidemiology of the disease.

Disease Outbreaks↗
PubMed2002

Mechanisms of long-distance dispersal of seeds by wind.

Long-distance dispersal (LDD) is central to species expansion following climate change, re-colonization of disturbed areas and control of pests. The current paradigm is that the frequency and spatial extent of LDD events are extremely difficult to predict. Here we show that mechanistic models coupling seed release and aerodynamics with turbulent transport processes provide accurate probabilistic descriptions of LDD of seeds by wind. The proposed model reliably predicts the vertical distribution of dispersed seeds of five tree species observed along a 45-m high tower in an eastern US deciduous forest. Simulations show that uplifting above the forest canopy is necessary and sufficient for LDD, hence, they provide the means to define LDD quantitatively rather than arbitrarily. Seed uplifting probability thus sets an upper bound on the probability of long-distance colonization. Uplifted yellow poplar seeds are on average lighter than seeds at the forest floor, but also include the heaviest seeds. Because uplifting probabilities are appreciable (as much as 1 5%), and tree seed crops are commonly massive, some LDD events will establish individuals that can critically affect plant dynamics on large scales.

Computer Simulation↗
PubMed2002

Hemagglutinin sequence clusters and the antigenic evolution of influenza A virus.

Continual mutations to the hemagglutinin (HA) gene of influenza A virus generate novel antigenic strains that cause annual epidemics. Using a database of 560 viral RNA sequences, we study the structure and tempo of HA evolution over the past two decades. We detect a critical length scale, in amino acid space, at which HA sequences aggregate into clusters, or swarms. We investigate the spatio-temporal distribution of viral swarms and compare it to the time series of the influenza vaccines recommended by the World Health Organization. We introduce a method for predicting future dominant HA amino acid sequences and discuss its potential relevance to vaccine choice. We also investigate the relationship between cluster structure and the primary antibody-combining regions of the HA protein.

Antibodies↗
PubMed2002

Competition and species packing in patchy environments.

In models of competition in which space is treated as a continuum, and population size as continuous, there are no limits to the number of species that can coexist. For a finite number of sites, N, the results are different. The answer will, of course, depend on the model used to ask the question. In the Tilman-May-Nowak ordinary differential equation model, the number of species is asymptotically C log N with most species packed in at the upper end of the competitive hierarchy. In contrast, for metapopulation models with discrete individuals and stochastic spatial systems with various competition neighborhoods, we find a traditional species area relationship CN(a), with no species clumping along the phenotypic gradient. The exponent a is larger by a factor of 2 for spatially explicit models. In words, a spatial distribution of competitors allows for greater diversity than a metapopulation model due to the effects of recruitment limitation in their competition.

Competitive Behavior↗
PubMed2000

Pathogen-Driven Outbreaks in Forest Defoliators Revisited: Building Models from Experimental Data.

Models of outbreaks in forest-defoliating insects are typically built from a priori considerations and tested only with long time series of abundances. We instead present a model built from experimental data on the gypsy moth and its nuclear polyhedrosis virus, which has been extensively tested with epidemic data. These data have identified key details of the gypsy moth-virus interaction that are missing from earlier models, including seasonality in host reproduction, delays between host infection and death, and heterogeneity among hosts in their susceptibility to the virus. Allowing for these details produces models in which annual epidemics are followed by bouts of reproduction among surviving hosts and leads to quite different conclusions than earlier models. First, these models suggest that pathogen-driven outbreaks in forest defoliators occur partly because newly hatched insect larvae have higher average susceptibility than do older larvae. Second, the models show that a combination of seasonality and delays between infection and death can lead to unstable cycles in the absence of a stabilizing mechanism; these cycles, however, are stabilized by the levels of heterogeneity in susceptibility that we have observed in our experimental data. Moreover, our experimental estimates of virus transmission rates and levels of heterogeneity in susceptibility in gypsy moth populations give model dynamics that closely approximate the dynamics of real gypsy moth populations. Although we built our models from data for gypsy moth, our models are, nevertheless, quite general. Our conclusions are therefore likely to be true, not just for other defoliator-pathogen interactions, but for many host-pathogen interactions in which seasonality plays an important role. Our models thus give qualitative insight into the dynamics of host-pathogen interactions, while providing a quantitative interpretation of our gypsy moth-virus data.

Lymantria dispar↗
PubMedDate not supplied

Evolution and persistence of influenza A and other diseases.

The evolution of the etiological agents of disease presents one of the greatest challenges for their control, and makes essential complementing standard epidemiological investigations with broader approaches that allow for evolutionary change. Given the stunning genetic diversity that is possible for many such agents, such as the influenza virus, it is impossible to represent all of the diversity manifest at the level of amino acid sequences. We show that drift-variant influenza strains naturally cluster into groups which are associated with functionally important epitopic regions. Dominant clusters typically replace each other every 2-5 years, and this feature is fundamental to the development of vaccination strategies. We furthermore show that stochastic fluctuations can greatly magnify small interference effects among strains, or even among subtypes, leading for example to competitive exclusion in situations where such effects would be unexpected based on the usual deterministic models. We suggest that this effect might be involved in the explanations of some persistent empirical anomalies.

Animals↗