The Experts below are selected from a list of 33924 Experts worldwide ranked by ideXlab platform
Sylvia Cremer - One of the best experts on this subject based on the ideXlab platform.
-
social transfer of pathogenic fungus promotes active immunisation in ant colonies
PLOS Biology, 2012Co-Authors: Matthias Konrad, Meghan L Vyleta, Miriam Stock, Simon Tragust, Martina Klatt, Verena Drescher, Carsten Marr, Line V Ugelvig, Fabian J Theis, Sylvia CremerAbstract:Due to the omnipresent risk of epidemics, insect societies have evolved sophisticated disease defences at the Individual and colony level. An intriguing yet little understood phenomenon is that social contact to pathogen-exposed Individuals reduces susceptibility of previously naive nestmates to this pathogen. We tested whether such social immunisation in Lasius ants against the entomopathogenic fungus Metarhizium anisopliae is based on active upregulation of the immune system of nestmates following contact to an Infectious Individual or passive protection via transfer of immune effectors among group members—that is, active versus passive immunisation. We found no evidence for involvement of passive immunisation via transfer of antimicrobials among colony members. Instead, intensive allogrooming behaviour between naive and pathogen-exposed ants before fungal conidia firmly attached to their cuticle suggested passage of the pathogen from the exposed Individuals to their nestmates. By tracing fluorescence-labelled conidia we indeed detected frequent pathogen transfer to the nestmates, where they caused low-level infections as revealed by growth of small numbers of fungal colony forming units from their dissected body content. These infections rarely led to death, but instead promoted an enhanced ability to inhibit fungal growth and an active upregulation of immune genes involved in antifungal defences (defensin and prophenoloxidase, PPO). Contrarily, there was no upregulation of the gene cathepsin L, which is associated with antibacterial and antiviral defences, and we found no increased antibacterial activity of nestmates of fungus-exposed ants. This indicates that social immunisation after fungal exposure is specific, similar to recent findings for Individual-level immune priming in invertebrates. Epidemiological modeling further suggests that active social immunisation is adaptive, as it leads to faster elimination of the disease and lower death rates than passive immunisation. Interestingly, humans have also utilised the protective effect of low-level infections to fight smallpox by intentional transfer of low pathogen doses (“variolation” or “inoculation”).
Frank Ball - One of the best experts on this subject based on the ideXlab platform.
-
heterogeneous network epidemics real time growth variance and extinction of infection
Journal of Mathematical Biology, 2017Co-Authors: Frank Ball, Thomas HouseAbstract:Recent years have seen a large amount of interest in epidemics on networks as a way of representing the complex structure of contacts capable of spreading infections through the modern human population. The configuration model is a popular choice in theoretical studies since it combines the ability to specify the distribution of the number of contacts (degree) with analytical tractability. Here we consider the early real-time behaviour of the Markovian SIR epidemic model on a configuration model network using a multitype branching process. We find closed-form analytic expressions for the mean and variance of the number of Infectious Individuals as a function of time and the degree of the initially infected Individual(s), and write down a system of differential equations for the probability of extinction by time t that are numerically fast compared to Monte Carlo simulation. We show that these quantities are all sensitive to the degree distribution—in particular we confirm that the mean prevalence of infection depends on the first two moments of the degree distribution and the variance in prevalence depends on the first three moments of the degree distribution. In contrast to most existing analytic approaches, the accuracy of these results does not depend on having a large number of Infectious Individuals, meaning that in the large population limit they would be asymptotically exact even for one initial Infectious Individual.
-
heterogeneous network epidemics real time growth variance and extinction of infection
arXiv: Populations and Evolution, 2016Co-Authors: Frank Ball, Thomas HouseAbstract:Recent years have seen a large amount of interest in epidemics on networks as a way of representing the complex structure of contacts capable of spreading infections through the modern human population. The configuration model is a popular choice in theoretical studies since it combines the ability to specify the distribution of the number of contacts (degree) with analytical tractability. Here we consider the early real-time behaviour of the Markovian SIR epidemic model on a configuration model network using a multi-type branching process. We find closed-form analytic expressions for the mean and variance of the number of Infectious Individuals as a function of time and the degree of the initially infected Individual(s), and write down a system of differential equations for the probability of extinction that are numerically fast compared to Monte Carlo simulation. We show that these quantities are all sensitive to the degree distribution - in particular we confirm that the mean prevalence of infection depends on the first two moments of the degree distribution and the variance in prevalence depends on the first three moments of the degree distribution. In contrast to most existing analytic approaches, the accuracy of these results does not depend on having a large number of Infectious Individuals, meaning that in the large population limit they would be asymptotically exact even for one initial Infectious Individual.
-
epidemics with two levels of mixing
Annals of Applied Probability, 1997Co-Authors: Frank Ball, Denis Mollison, Gianpaolo ScaliatombaAbstract:We consider epidemics with removal (SIR epidemics) in populations that mix at two levels: global and local. We develop a general modelling framework for such processes, which allows us to analyze the conditions under which a large outbreak is possible, the size of such outbreaks when they can occur and the implications for vaccination strategies, in each case comparing our results with the simpler homogeneous mixing case.More precisely, we consider models in which each Infectious Individual i has a global probability $p_G$ for infecting each other Individual in the population and a local probability $p_L$, typically much larger, of infecting each other Individual among a set of neighbors $\mathscr{N}(i)$. Our main concern is the case where the population is partitioned into local groups or households, but our approach also applies to cases where neighborhoods do not form a partition, for instance, to spatial models with a mixture of local (e.g., nearest-neighbor) and global contacts.We use a variety of theoretical approaches: a random graph framework for the initial exposition of the simple case where an Individual's contacts are independent; branching process approximations for the general threshold result; and an embedding representation for rigorous results on the final size of outbreaks.From the applied viewpoint the key result is that, compared with the homogeneous mixing model in which Individuals make contacts simply with probability $p_G$, the local Infectious contacts have an "amplification" effect. The basic reproductive ratio of the epidemic is increased from its Individual-to-Individual value $R_G$ in the absence of local infections to a group-to-group value $R_* = \mu R_G$, where $\mu$ is the mean size of an outbreak, started by a randomly chosen Individual, in which only local infections count. Where the groups are large and the within-group epidemics are above threshold, this amplification can permit an outbreak in the whole population at very low levels of $p_G$, for instance, for $p_G = O(1/Nn)$ in a population of N divided into groups of size n.The implication of these results for control strategies is that vaccination should be directed preferentially toward reducing $\mu$; we discuss the conditions under which the equalizing strategy, aimed at leaving unvaccinated sets of neighbors of equal sizes, is optimal. We also discuss the estimation of our threshold parameter $R_*$ from data on epidemics among households.
Luis M A Bettencourt - One of the best experts on this subject based on the ideXlab platform.
-
real time bayesian estimation of the epidemic potential of emerging Infectious diseases
PLOS ONE, 2008Co-Authors: Luis M A Bettencourt, Ruy M RibeiroAbstract:Background Fast changes in human demographics worldwide, coupled with increased mobility, and modified land uses make the threat of emerging Infectious diseases increasingly important. Currently there is worldwide alert for H5N1 avian influenza becoming as transmissible in humans as seasonal influenza, and potentially causing a pandemic of unprecedented proportions. Here we show how epidemiological surveillance data for emerging Infectious diseases can be interpreted in real time to assess changes in transmissibility with quantified uncertainty, and to perform running time predictions of new cases and guide logistics allocations. Methodology/Principal Findings We develop an extension of standard epidemiological models, appropriate for emerging Infectious diseases, that describes the probabilistic progression of case numbers due to the concurrent effects of (incipient) human transmission and multiple introductions from a reservoir. The model is cast in terms of surveillance observables and immediately suggests a simple graphical estimation procedure for the effective reproductive number R (mean number of cases generated by an Infectious Individual) of standard epidemics. For emerging Infectious diseases, which typically show large relative case number fluctuations over time, we develop a Bayesian scheme for real time estimation of the probability distribution of the effective reproduction number and show how to use such inferences to formulate significance tests on future epidemiological observations. Conclusions/Significance Violations of these significance tests define statistical anomalies that may signal changes in the epidemiology of emerging diseases and should trigger further field investigation. We apply the methodology to case data from World Health Organization reports to place bounds on the current transmissibility of H5N1 influenza in humans and establish a statistical basis for monitoring its evolution in real time.
Ruy M Ribeiro - One of the best experts on this subject based on the ideXlab platform.
-
real time bayesian estimation of the epidemic potential of emerging Infectious diseases
PLOS ONE, 2008Co-Authors: Luis M A Bettencourt, Ruy M RibeiroAbstract:Background Fast changes in human demographics worldwide, coupled with increased mobility, and modified land uses make the threat of emerging Infectious diseases increasingly important. Currently there is worldwide alert for H5N1 avian influenza becoming as transmissible in humans as seasonal influenza, and potentially causing a pandemic of unprecedented proportions. Here we show how epidemiological surveillance data for emerging Infectious diseases can be interpreted in real time to assess changes in transmissibility with quantified uncertainty, and to perform running time predictions of new cases and guide logistics allocations. Methodology/Principal Findings We develop an extension of standard epidemiological models, appropriate for emerging Infectious diseases, that describes the probabilistic progression of case numbers due to the concurrent effects of (incipient) human transmission and multiple introductions from a reservoir. The model is cast in terms of surveillance observables and immediately suggests a simple graphical estimation procedure for the effective reproductive number R (mean number of cases generated by an Infectious Individual) of standard epidemics. For emerging Infectious diseases, which typically show large relative case number fluctuations over time, we develop a Bayesian scheme for real time estimation of the probability distribution of the effective reproduction number and show how to use such inferences to formulate significance tests on future epidemiological observations. Conclusions/Significance Violations of these significance tests define statistical anomalies that may signal changes in the epidemiology of emerging diseases and should trigger further field investigation. We apply the methodology to case data from World Health Organization reports to place bounds on the current transmissibility of H5N1 influenza in humans and establish a statistical basis for monitoring its evolution in real time.
Matthias Konrad - One of the best experts on this subject based on the ideXlab platform.
-
social transfer of pathogenic fungus promotes active immunisation in ant colonies
PLOS Biology, 2012Co-Authors: Matthias Konrad, Meghan L Vyleta, Miriam Stock, Simon Tragust, Martina Klatt, Verena Drescher, Carsten Marr, Line V Ugelvig, Fabian J Theis, Sylvia CremerAbstract:Due to the omnipresent risk of epidemics, insect societies have evolved sophisticated disease defences at the Individual and colony level. An intriguing yet little understood phenomenon is that social contact to pathogen-exposed Individuals reduces susceptibility of previously naive nestmates to this pathogen. We tested whether such social immunisation in Lasius ants against the entomopathogenic fungus Metarhizium anisopliae is based on active upregulation of the immune system of nestmates following contact to an Infectious Individual or passive protection via transfer of immune effectors among group members—that is, active versus passive immunisation. We found no evidence for involvement of passive immunisation via transfer of antimicrobials among colony members. Instead, intensive allogrooming behaviour between naive and pathogen-exposed ants before fungal conidia firmly attached to their cuticle suggested passage of the pathogen from the exposed Individuals to their nestmates. By tracing fluorescence-labelled conidia we indeed detected frequent pathogen transfer to the nestmates, where they caused low-level infections as revealed by growth of small numbers of fungal colony forming units from their dissected body content. These infections rarely led to death, but instead promoted an enhanced ability to inhibit fungal growth and an active upregulation of immune genes involved in antifungal defences (defensin and prophenoloxidase, PPO). Contrarily, there was no upregulation of the gene cathepsin L, which is associated with antibacterial and antiviral defences, and we found no increased antibacterial activity of nestmates of fungus-exposed ants. This indicates that social immunisation after fungal exposure is specific, similar to recent findings for Individual-level immune priming in invertebrates. Epidemiological modeling further suggests that active social immunisation is adaptive, as it leads to faster elimination of the disease and lower death rates than passive immunisation. Interestingly, humans have also utilised the protective effect of low-level infections to fight smallpox by intentional transfer of low pathogen doses (“variolation” or “inoculation”).