132 research outputs found
R-DjidjouDemasse/Age-structured-model-COVID19-Julia-code: 1st release of Age-structured-model-COVID19-Julia-code
No full textAge-structured non-pharmaceutical interventions for optimal control of COVID-19 epidemic by Q. Richard, S. Alizon, M. Choisy, M.T. Sofonea, R. Djidjou-Demass
Simulating the effect of public health interventions using dated virus sequences and geographical data
No full textRecommendationInternational audienceA recommendation of the preprint: Simon Dellicour, Guy Baele, Gytis Dudas, Nuno R. Faria, Oliver G. Pybus, Marc A. Suchard, Andrew Rambaut, Philippe Lemey. Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak. (2018), bioRxiv, 163691, ver. 3 peer-reviewed by Peer Community in Evolutionary Biology. 10.1101/16369
Bibliographie
No full textAgnew, P., Berticat, C., Bedhomme, S., Sidobre, C. & Michalakis, Y. (2004). Parasitism increases and decreases the costs of insecticide resistance in mosquitoes. Evolution, 58, 579-586. Alford, R.A., Brown, G.P., Schwarzkopf, L., Phillips, B.L. & Shine, R. (2009). Comparisons through time and space suggest rapid evolution of dispersal behaviour in an invasive species. Wildlife Research, 36, 23-28. Alizon, S., Luciani, F. & Regoes, R.R. (2011). Epidemiological and clinical consequences of with..
An efficient moments-based inference method for within-host bacterial infection dynamics.
No full textOver the last ten years, isogenic tagging (IT) has revolutionised the study of bacterial infection dynamics in laboratory animal models. However, quantitative analysis of IT data has been hindered by the piecemeal development of relevant statistical models. The most promising approach relies on stochastic Markovian models of bacterial population dynamics within and among organs. Here we present an efficient numerical method to fit such stochastic dynamic models to in vivo experimental IT data. A common approach to statistical inference with stochastic dynamic models relies on producing large numbers of simulations, but this remains a slow and inefficient method for all but simple problems, especially when tracking bacteria in multiple locations simultaneously. Instead, we derive and solve the systems of ordinary differential equations for the two lower-order moments of the stochastic variables (mean, variance and covariance). For any given model structure, and assuming linear dynamic rates, we demonstrate how the model parameters can be efficiently and accurately estimated by divergence minimisation. We then apply our method to an experimental dataset and compare the estimates and goodness-of-fit to those obtained by maximum likelihood estimation. While both sets of parameter estimates had overlapping confidence regions, the new method produced lower values for the division and death rates of bacteria: these improved the goodness-of-fit at the second time point at the expense of that of the first time point. This flexible framework can easily be applied to a range of experimental systems. Its computational efficiency paves the way for model comparison and optimal experimental design
data_for_Dryad
No full textThe zip file contains an R script to run the statistical analyses and plot the Figure shown in the manuscript. It also contains the data from the original article, presented here in a format appropriate for use with the script. The data from the original article are archived as: Harrison F (2013) Data from: Dynamic social behaviour in a bacterium: Pseudomonas aeruginosa partially compensates for siderophore loss to cheats. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.t77n
Introducing the outbreak threshold in epidemiology.
No full textWhen a pathogen is rare in a host population, there is a chance that it will die out because of stochastic effects instead of causing a major epidemic. Yet no criteria exist to determine when the pathogen increases to a risky level, from which it has a large chance of dying out, to when a major outbreak is almost certain. We introduce such an outbreak threshold (T₀), and find that for large and homogeneous host populations, in which the pathogen has a reproductive ratio R₀, on the order of 1/Log(R₀) infected individuals are needed to prevent stochastic fade-out during the early stages of an epidemic. We also show how this threshold scales with higher heterogeneity and R0 in the host population. These results have implications for controlling emerging and re-emerging pathogens
Within-Host Dynamics of Multi-Species Infections:Facilitation, Competition and Virulence
No full textHost individuals are often infected with more than one parasite species (parasites defined broadly, to include viruses and bacteria). Yet, research in infection biology is dominated by studies on single-parasite infections. A focus on single-parasite infections is justified if the interactions among parasites are additive, however increasing evidence points to non-additive interactions being the norm. Here we review this evidence and theoretically explore the implications of non-additive interactions between co-infecting parasites. We use classic Lotka-Volterra two-species competition equations to investigate the within-host dynamical consequences of various mixes of competition and facilitation between a pair of co-infecting species. We then consider the implications of these dynamics for the virulence (damage to host) of co-infections and consequent evolution of parasite strategies of exploitation. We find that whereas one-way facilitation poses some increased virulence risk, reciprocal facilitation presents a qualitatively distinct destabilization of within-host dynamics and the greatest risk of severe disease.</p
data_for_Dryad
No full textThe zip file contains an R script to run the statistical analyses and plot the Figure shown in the manuscript. It also contains the data from the original article, presented here in a format appropriate for use with the script. The data from the original article are archived as: Harrison F (2013) Data from: Dynamic social behaviour in a bacterium: Pseudomonas aeruginosa partially compensates for siderophore loss to cheats. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.t77n
Example of simulation runs.
No full text<p>Profiles of example simulation runs over time. In (A), the first strain goes extinct before a mutated pathogen arises, while in (B) emergence occurs. Blue dots represent the initial pathogen, red dots in (B) represent the mutated strain, while green dots show immune cell proliferation. The constant stream of red dots in (B) indicate the mutated strain at zero copies. Parameters are <i>K</i> = 100, <i>R</i><sub>1</sub> = 60, <i>R</i><sub>2</sub> = 200, <i>ρ</i> = 0.5, and <i>μ</i> = 0.01.</p
Comparison of simulations with analytical solutions.
No full text<p>Comparisons of the full analytical solution (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004149#pcbi.1004149.e008" target="_blank">Equation 7</a>, with Π given by <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004149#pcbi.1004149.e013" target="_blank">Equation 10</a>) with simulation results. Solid lines represent analytical solutions; points are simulation calculations. Graphs are plotted as a function of the reproductive ratio of the second strain, <i>R</i><sub>2</sub>. Note that the <i>y</i> axis is plotted on a log scale. Different colours denote different mutation rates, as shown in the accompanying legend. Other parameters are (A and B) <i>K</i> = 100, <i>R</i><sub>1</sub> = 60; (C and D) <i>K</i> = 1,000, <i>R</i><sub>1</sub> = 100; or (E and F) <i>K</i> = 10,000, <i>R</i><sub>1</sub> = 2000. In all panels, <i>x</i><sub><i>init</i></sub> = 1 and <i>y</i><sub><i>init</i></sub> = 20. <i>ρ</i> equals either 0.5 (A, C, and E) or 9 (B, D, and F). All error bars, as calculated using binomial confidence intervals, lie within the points. Further results are shown in Section 2 of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004149#pcbi.1004149.s002" target="_blank">S1 Text</a>.</p
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