Article

Are SARS superspreaders cloud adults?

Emerging infectious diseases (Impact Factor: 7.33). 05/2005; 11(4):637-8. DOI: 10.3201/eid1104.040639
Source: PubMed

Full-text

Available from: Werner E Bischoff, Jun 03, 2015
0 Followers
 · 
104 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: 1. Introduced species disrupt native communities and biodiversity worldwide. Parasitic infections (and at times, their absence) are thought to be a key component in the success and impact of biological invasions by plants and animals. They can facilitate or limit invasions, and positively or negatively impact native species. 2. Parasites have not only direct effects on their hosts, but also indirect effects on the species with which their hosts interact. Indirect effects include density-mediated effects (resulting from parasite-induced reduction in host reproduction and survival) as well as trait-mediated indirect effects (resulting from parasite-induced changes in host phenotype, behaviour or life history). These effects are not mutually exclusive but often interact. 3. The importance of these indirect interactions for invasion success, and the extent to which these effects ramify throughout communities and influence ecosystems undergoing biological invasion provide the focus of our review. Examples from the animal and plant literature illustrate the importance of parasites in mediating both competitive and consumer—resource interactions between native and invasive species. 4. Parasites are involved in indirect interactions at all trophic levels. Furthermore, the indirect effects of parasitic infection are important at a range of biological scales from within a host to the whole ecosystem in determining invasion success and impact. 5. To understand the importance of parasitic infection in invasion success and in the outcomes for invaded communities requires an interdisciplinary approach by ecologists and parasitologists, across animal and plant systems. Future research should develop a framework integrating community ecology, evolution and immunology to better understand and manage the spread of invasive species and their diseases.
    Functional Ecology 10/2012; 26(6). DOI:10.2307/23326822 · 4.86 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Co-infection by multiple parasites is common within individuals. Interactions between co-infecting parasites include resource competition, direct competition and immune-mediated interactions and each are likely to alter the dynamics of single parasites. We posit that co-infection is a driver of variation in parasite establishment and growth, ultimately altering the production of parasite transmission stages. To test this hypothesis, three different treatment groups of laboratory mice were infected with the gastrointestinal helminth Heligmosomoides polygyrus, the respiratory bacterial pathogen Bordetella bronchiseptica lux(+) or co-infected with both parasites. To follow co-infection simultaneously, self-bioluminescent bacteria were used to quantify infection in vivo and in real-time, while helminth egg production was monitored in real-time using faecal samples. Co-infection resulted in high bacterial loads early in the infection (within the first 5 days) that could cause host mortality. Co-infection also produced helminth 'super-shedders'; individuals that chronically shed the helminth eggs in larger than average numbers. Our study shows that co-infection may be one of the underlying mechanisms for the often-observed high variance in parasite load and shedding rates, and should thus be taken into consideration for disease management and control. Further, using self-bioluminescent bacterial reporters allowed quantification of the progression of infection within the whole animal of the same individuals at a fine temporal scale (daily) and significantly reduced the number of animals used (by 85%) compared with experiments that do not use in vivo techniques. Thus, we present bioluminescent imaging as a novel, non-invasive tool offering great potential to be taken forward into other applications of infectious disease ecology.
    Journal of The Royal Society Interface 03/2013; 10(80):20120588. DOI:10.1098/rsif.2012.0588 · 3.86 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Hierarchical geographical traffic networks are critical for our understanding of scaling laws in human trajectories. Here, we investigate the susceptible-infected epidemic process evolving on hierarchical networks in which agents randomly walk along the edges and establish contacts in network nodes. We employ a metapopulation modeling framework that allows us to explore the contagion spread patterns in relation to multi-scale mobility behaviors. A series of computer simulations revealed that a shifted power-law-like negative relationship between the peak timing of epidemics τ0τ0 and population density, and a logarithmic positive relationship between τ0τ0 and the network size, can both be explained by the gradual enlargement of fluctuations in the spreading process. We employ a semi-analytical method to better understand the nature of these relationships and the role of pertinent demographic factors. Additionally, we provide a quantitative discussion of the efficiency of a border screening procedure in delaying epidemic outbreaks on hierarchical networks, yielding a rather limited feasibility of this mitigation strategy but also its non-trivial dependence on population density, infector detectability, and the diversity of the susceptible region. Our results suggest that the interplay between the human spatial dynamics, network topology, and demographic factors can have important consequences for the global spreading and control of infectious diseases. These findings provide novel insights into the combined effects of human mobility and the organization of geographical networks on spreading processes, with important implications for both epidemiological research and health policy.
    Communications in Nonlinear Science and Numerical Simulation 05/2014; 19(5):1301–1312. DOI:10.1016/j.cnsns.2013.09.002 · 2.57 Impact Factor