2,244 research outputs found
Canalization of the evolutionary trajectory of the human influenza virus
Since its emergence in 1968, influenza A (H3N2) has evolved extensively in
genotype and antigenic phenotype. Antigenic evolution occurs in the context of
a two-dimensional 'antigenic map', while genetic evolution shows a
characteristic ladder-like genealogical tree. Here, we use a large-scale
individual-based model to show that evolution in a Euclidean antigenic space
provides a remarkable correspondence between model behavior and the
epidemiological, antigenic, genealogical and geographic patterns observed in
influenza virus. We find that evolution away from existing human immunity
results in rapid population turnover in the influenza virus and that this
population turnover occurs primarily along a single antigenic axis. Thus,
selective dynamics induce a canalized evolutionary trajectory, in which the
evolutionary fate of the influenza population is surprisingly repeatable and
hence, in theory, predictable.Comment: 29 pages, 5 figures, 10 supporting figure
Innovative in silico approaches to address avian flu using grid technology
The recent years have seen the emergence of diseases which have spread very
quickly all around the world either through human travels like SARS or animal
migration like avian flu. Among the biggest challenges raised by infectious
emerging diseases, one is related to the constant mutation of the viruses which
turns them into continuously moving targets for drug and vaccine discovery.
Another challenge is related to the early detection and surveillance of the
diseases as new cases can appear just anywhere due to the globalization of
exchanges and the circulation of people and animals around the earth, as
recently demonstrated by the avian flu epidemics. For 3 years now, a
collaboration of teams in Europe and Asia has been exploring some innovative in
silico approaches to better tackle avian flu taking advantage of the very large
computing resources available on international grid infrastructures. Grids were
used to study the impact of mutations on the effectiveness of existing drugs
against H5N1 and to find potentially new leads active on mutated strains. Grids
allow also the integration of distributed data in a completely secured way. The
paper presents how we are currently exploring how to integrate the existing
data sources towards a global surveillance network for molecular epidemiology.Comment: 7 pages, submitted to Infectious Disorders - Drug Target
A Simple Cellular Automaton Model for Influenza A Viral Infections
Viral kinetics have been extensively studied in the past through the use of
spatially homogeneous ordinary differential equations describing the time
evolution of the diseased state. However, spatial characteristics such as
localized populations of dead cells might adversely affect the spread of
infection, similar to the manner in which a counter-fire can stop a forest fire
from spreading. In order to investigate the influence of spatial
heterogeneities on viral spread, a simple 2-D cellular automaton (CA) model of
a viral infection has been developed. In this initial phase of the
investigation, the CA model is validated against clinical immunological data
for uncomplicated influenza A infections. Our results will be shown and
discussed.Comment: LaTeX, 12 pages, 18 EPS figures, uses document class ReTeX4, and
packages amsmath and SIunit
Simulations of Antigenic Variability in Influenza A
Computational models of the immune system (IS) and pathogenic agents have several applications, such as theory testing and validation, or as a complement to first stages of drug trials. One possible application is the prediction of the lethality of new Influenza A strains, which are constantly created due to antigenic drift and shift. Here, we present several simulations of antigenic variability in Influenza A using an agent-based approach, where low level molecular antigen-antibody interactions are explicitly described. Antigenic drift and shift events are analyzed regarding the virulence of emergent strains against the IS. Results are discussed from a qualitative point of view taking into account recent and generally recognized immunology and influenza literature
Some Remarks about the Complexity of Epidemics Management
Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that
the assumptions underlying the established theory of epidemics management are
too idealistic. For an improvement of procedures and organizations involved in
fighting epidemics, extended models of epidemics management are required. The
necessary extensions consist in a representation of the management loop and the
potential frictions influencing the loop. The effects of the non-deterministic
frictions can be taken into account by including the measures of robustness and
risk in the assessment of management options. Thus, besides of the increased
structural complexity resulting from the model extensions, the computational
complexity of the task of epidemics management - interpreted as an optimization
problem - is increased as well. This is a serious obstacle for analyzing the
model and may require an additional pre-processing enabling a simplification of
the analysis process. The paper closes with an outlook discussing some
forthcoming problems
Numerical investigation of Differential Biological-Models via GA-Kansa Method Inclusive Genetic Strategy
In this paper, we use Kansa method for solving the system of differential
equations in the area of biology. One of the challenges in Kansa method is
picking out an optimum value for Shape parameter in Radial Basis Function to
achieve the best result of the method because there are not any available
analytical approaches for obtaining optimum Shape parameter. For this reason,
we design a genetic algorithm to detect a close optimum Shape parameter. The
experimental results show that this strategy is efficient in the systems of
differential models in biology such as HIV and Influenza. Furthermore, we prove
that using Pseudo-Combination formula for crossover in genetic strategy leads
to convergence in the nearly best selection of Shape parameter.Comment: 42 figures, 23 page
The Aerosphere as a Network Connector of Organisms and Their Diseases
Aeroecological processes, especially powered flight of animals, can rapidly connect biological communities across the globe. This can have profound consequences for evolutionary diversification, energy and nutrient transfers, and the spread of infectious diseases. The latter is of particular consequence for human populations, since migratory birds are known to host diseases which have a history of transmission into domestic poultry or even jumping to human hosts. In this chapter, we present a scenario under which a highly pathogenic avian influenza (HPAI) strain enters North America from East Asia via postmolting waterfowl migration. We use an agent-based model (ABM) to simulate the movement and disease transmission among 106 generalized waterfowl agents originating from ten molting locations in eastern Siberia, with the HPAI seeded in only ~102 agents at one of these locations. Our ABM tracked the disease dynamics across a very large grid of sites as well as individual agents, allowing us to examine the spatiotemporal patterns of change in virulence of the HPAI infection as well as waterfowl host susceptibility to the disease. We concurrently simulated a 12-station disease monitoring network in the northwest USA and Canada in order to assess the potential efficacy of these sites to detect and confirm the arrival of HPAI. Our findings indicated that HPAI spread was initially facilitated but eventually subdued by the migration of host agents. Yet, during the 90-day simulation, selective pressures appeared to have distilled the HPAI strain to its most virulent form (i.e., through natural selection), which was counterbalanced by the host susceptibility being conversely reduced (i.e., through genetic predisposition and acquired immunity). The monitoring network demonstrated wide variation in the utility of sites; some were clearly better at providing early warnings of HPAI arrival, while sites further from the disease origin exposed the selective dynamics which slowed the spread of the disease albeit with the result of passing highly virulent strains into southern wintering locales (where human impacts are more likely). Though the ABM presented had generalized waterfowl migration and HPAI disease dynamics, this exercise demonstrates the power of such simulations to examine the extremely large and complex processes which comprise aeroecology. We offer insights into how such models could be further parameterized to represent HPAI transmission risks as well as how ABMs could be applied to other aeroecological questions pertaining to individual-based connectivity
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