17,372 research outputs found
Evolutionary bi-stability in pathogen transmission mode
Many pathogens transmit to new hosts by both infection (horizontal transmission) and transfer to the
infected host's offspring (vertical transmission). These two transmission modes require speci®c adap-
tations of the pathogen that can be mutually exclusive, resulting in a trade-off between horizontal and
vertical transmission. We show that in mathematical models such trade-offs can lead to the simultaneous
existence of two evolutionary stable states (evolutionary bi-stability) of allocation of resources to the two
modes of transmission. We also show that jumping between evolutionary stable states can be induced by
gradual environmental changes. Using quantitative PCR-based estimates of abundance in seed and vege-
tative parts, we show that the pathogen of wheat, Phaeosphaeria nodorum, has jumped between two
distinct states of transmission mode twice in the past 160 years, which, based on published evidence,
we interpret as adaptation to environmental change. The ®nding of evolutionary bi-stability has impli-
cations for human, animal and other plant diseases. An ill-judged change in a disease control
programme could cause the pathogen to evolve a new, and possibly more damaging, combination of
transmission modes. Similarly, environmental changes can shift the balance between transmission
modes, with adverse effects on human, animal and plant health
Horizontal DNA transfer mechanisms of bacteria as weapons of intragenomic conflict
Horizontal DNA transfer (HDT) is a pervasive mechanism of diversification in many microbial species, but its primary evolutionary role remains controversial. Much recent research has emphasised the adaptive benefit of acquiring novel DNA, but here we argue instead that intragenomic conflict provides a coherent framework for understanding the evolutionary origins of HDT. To test this hypothesis, we developed a mathematical model of a clonally descended bacterial population undergoing HDT through transmission of mobile genetic elements (MGEs) and genetic transformation. Including the known bias of transformation toward the acquisition of shorter alleles into the model suggested it could be an effective means of counteracting the spread of MGEs. Both constitutive and transient competence for transformation were found to provide an effective defence against parasitic MGEs; transient competence could also be effective at permitting the selective spread of MGEs conferring a benefit on their host bacterium. The coordination of transient competence with cell-cell killing, observed in multiple species, was found to result in synergistic blocking of MGE transmission through releasing genomic DNA for homologous recombination while simultaneously reducing horizontal MGE spread by lowering the local cell density. To evaluate the feasibility of the functions suggested by the modelling analysis, we analysed genomic data from longitudinal sampling of individuals carrying Streptococcus pneumoniae. This revealed the frequent within-host coexistence of clonally descended cells that differed in their MGE infection status, a necessary condition for the proposed mechanism to operate. Additionally, we found multiple examples of MGEs inhibiting transformation through integrative disruption of genes encoding the competence machinery across many species, providing evidence of an ongoing "arms race." Reduced rates of transformation have also been observed in cells infected by MGEs that reduce the concentration of extracellular DNA through secretion of DNases. Simulations predicted that either mechanism of limiting transformation would benefit individual MGEs, but also that this tactic's effectiveness was limited by competition with other MGEs coinfecting the same cell. A further observed behaviour we hypothesised to reduce elimination by transformation was MGE activation when cells become competent. Our model predicted that this response was effective at counteracting transformation independently of competing MGEs. Therefore, this framework is able to explain both common properties of MGEs, and the seemingly paradoxical bacterial behaviours of transformation and cell-cell killing within clonally related populations, as the consequences of intragenomic conflict between self-replicating chromosomes and parasitic MGEs. The antagonistic nature of the different mechanisms of HDT over short timescales means their contribution to bacterial evolution is likely to be substantially greater than previously appreciated
Stochastic modelling of the spatial spread of influenza in Germany
In geographical epidemiology, disease counts are typically available in discrete spatial units and at discrete time-points. For example, surveillance data on infectious diseases usually consists of weekly counts of new infections in pre-defined geographical areas. Similarly, but on a different time-scale, cancer registries typically report yearly incidence or mortality counts in administrative regions. A major methodological challenge lies in building realistic models for space-time interactions on discrete irregular spatial graphs. In this paper, we will discuss an observation-driven approach, where past observed counts in neighbouring areas enter directly as explanatory variables, in contrast to the parameter-driven approach through latent Gaussian Markov random fields (Rue and Held, 2005) with spatio-temporal structure. The main focus will lie on the demonstration of the spread of influenza in Germany, obtained through the design and simulation of a spatial extension of the classical SIR model (Hufnagel et al., 2004)
Modeling the long term dynamics of pre-vaccination pertussis
The dynamics of strongly immunizing childhood infections is still not well
understood. Although reports of successful modeling of several incidence data
records can be found in the literature, the key determinants of the observed
temporal patterns have not been clearly identified. In particular, different
models of immunity waning and degree of protection applied to disease and
vaccine induced immunity have been debated in the literature on pertussis. Here
we study the effect of disease acquired immunity on the long term patterns of
pertussis prevalence. We compare five minimal models, all of which are
stochastic, seasonally forced, well-mixed models of infection based on
susceptible-infective-recovered dynamics in a closed population. These models
reflect different assumptions about the immune response of naive hosts, namely
total permanent immunity, immunity waning, immunity waning together with
immunity boosting, reinfection of recovered, and repeat infection after partial
immunity waning. The power spectra of the output prevalence time series
characterize the long term dynamics of the models. For epidemiological
parameters consistent with published data for pertussis, the power spectra show
quantitative and even qualitative differences that can be used to test their
assumptions by comparison with ensembles of several decades long
pre-vaccination data records. We illustrate this strategy on two publicly
available historical data sets.Comment: paper (31 pages, 11 figures, 1 table) and supplementary material (19
pages, 5 figures, 2 tables
Suppressing disease spreading by using information diffusion on multiplex networks
Although there is always an interplay between the dynamics of information
diffusion and disease spreading, the empirical research on the systemic
coevolution mechanisms connecting these two spreading dynamics is still
lacking. Here we investigate the coevolution mechanisms and dynamics between
information and disease spreading by utilizing real data and a proposed
spreading model on multiplex network. Our empirical analysis finds asymmetrical
interactions between the information and disease spreading dynamics. Our
results obtained from both the theoretical framework and extensive stochastic
numerical simulations suggest that an information outbreak can be triggered in
a communication network by its own spreading dynamics or by a disease outbreak
on a contact network, but that the disease threshold is not affected by
information spreading. Our key finding is that there is an optimal information
transmission rate that markedly suppresses the disease spreading. We find that
the time evolution of the dynamics in the proposed model qualitatively agrees
with the real-world spreading processes at the optimal information transmission
rate.Comment: 11 pages, 8 figure
Understanding disease control: influence of epidemiological and economic factors
We present a local spread model of disease transmission on a regular network
and compare different control options ranging from treating the whole
population to local control in a well-defined neighborhood of an infectious
individual. Comparison is based on a total cost of epidemic, including cost of
palliative treatment of ill individuals and preventive cost aimed at
vaccination or culling of susceptible individuals. Disease is characterized by
pre- symptomatic phase which makes detection and control difficult. Three
general strategies emerge, global preventive treatment, local treatment within
a neighborhood of certain size and only palliative treatment with no
prevention. The choice between the strategies depends on relative costs of
palliative and preventive treatment. The details of the local strategy and in
particular the size of the optimal treatment neighborhood weakly depends on
disease infectivity but strongly depends on other epidemiological factors. The
required extend of prevention is proportional to the size of the infection
neighborhood, but this relationship depends on time till detection and time
till treatment in a non-nonlinear (power) law. In addition, we show that the
optimal size of control neighborhood is highly sensitive to the relative cost,
particularly for inefficient detection and control application. These results
have important consequences for design of prevention strategies aiming at
emerging diseases for which parameters are not known in advance
The effects of heterogeneity on stochastic cycles in epidemics
Models of biological processes are often subject to different sources of
noise. Developing an understanding of the combined effects of different types
of uncertainty is an open challenge. In this paper, we study a variant of the
susceptible-infective-recovered model of epidemic spread, which combines both
agent-to-agent heterogeneity and intrinsic noise. We focus on epidemic cycles,
driven by the stochasticity of infection and recovery events, and study in
detail how heterogeneity in susceptibilities and propensities to pass on the
disease affects these quasi-cycles. While the system can only be described by a
large hierarchical set of equations in the transient regime, we derive a
reduced closed set of equations for population-level quantities in the
stationary regime. We analytically obtain the spectra of quasi-cycles in the
linear-noise approximation. We find that the characteristic frequency of these
cycles is typically determined by population averages of susceptibilities and
infectivities, but that their amplitude depends on higher-order moments of the
heterogeneity. We also investigate the synchronisation properties and phase lag
between different groups of susceptible and infected individuals.Comment: Main text 16 pages, 9 figures. Supplement 5 page
- …