Effects of population mixing on the spread of SIR epidemics

We study dynamics of spread of epidemics of SIR type in a realistic spatially-explicit geographical region, Southern and Central Ontario, using census data obtained from Statistics Canada, and examine the role of population mixing in epidemic processes. Our model incorporates the random nature of disease transmission, the discreteness and heterogeneity of distribution of host population.We find that introduction of a long-range interaction destroys spatial correlations very easily if neighbourhood sizes are homogeneous. For inhomogeneous neighbourhoods, very strong long-range coupling is required to achieve a similar effect. Our work applies to the spread of influenza during a single season.     

Epidemic spreading in scale-free networks

The Internet has a very complex connectivity recently modeled by the class of scale-free networks. This feature, which appears to be very efficient for a communications network, favors at the same time the spreading of computer viruses. We analyze real data from computer virus infections and find the average lifetime and persistence of viral strains on the Internet. We define a dynamical model for the spreading of infections on scale-free networks, finding the absence of an epidemic threshold and its associated critical behavior. This new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.     

Inferring epidemiological parameters from phylogenetic information for the HIV-1 epidemic among MSM

The HIV-1 epidemic in Europe is primarily sustained by a dynamic topology of sexual interactions among MSM who have individual immune systems and behavior. This epidemiological process shapes the phylogeny of the virus population. Both fields of epidemic modeling and phylogenetics have a long history, however it remains difficult to use phylogenetic data to infer epidemiological parameters such as the structure of the sexual network and the per-act infectiousness. This is because phylogenetic data is necessarily incomplete and ambiguous. Here we show that the cluster-size distribution indeed contains information about epidemiological parameters using detailed numberical experiments. We simulate the HIV epidemic among MSM many times using the Monte Carlo method with all parameter values and their ranges taken from literature. For each simulation and the corresponding set of parameter values we calculate the likelihood of reproducing an observed cluster-size distribution. The result is an estimated likelihood distribution of all parameters from the phylogenetic data, in particular the structure of the sexual network, the per-act infectiousness, and the risk behavior reduction upon diagnosis. These likelihood distributions encode the knowledge provided by the observed cluster-size distrbution, which we quantify using information theory. Our work suggests that the growing body of genetic data of patients can be exploited to understand the underlying epidemiological process.     

Population turnover and adaptation in heterogeneous environments

We study adaptive dynamics in a structured population model of asexual individuals which takes into account environmental heterogeneity among the subpopulations. The key purpose of the present work is to address how population turnovers, i.e. extinction events followed by recolonization, affect the rate of fixation of advantageous mutations. This model is a generalization of our previous model to address the interplay between environmental correlation and evolutionary forces on the adaptive process. The incorporation of population turnovers into the model enables us to make a direct correspondence between the model and host-parasite dynamics (epidemiological models). Strikingly, contrary to the intuitive and usual deleterious effect associated to extinction events, it is observed that population turnovers can in fact speed up adaptation as heterogeneity rises. On the other side, in nearly homogeneous population turnovers have a neutral effect on fixation rates, but a detrimental outcome is also achieved when extinction events become very common. In resume, population turnover outcomes on fixation rates of advantageous mutations are strongly influenced by the selective correlation among the subpopulations (demes).     

Consensus QSAR model for identifying novel H5N1 inhibitors

Due to the importance of neuraminidase in the pathogenesis of influenza virus infection, it has been regarded as the most important drug target for the treatment of influenza. Resistance to currently available drugs and new findings related to structure of the protein requires novel neuraminidase 1 (N1) inhibitors. In this study, a consensus QSAR model with defined applicability domain (AD) was developed using published N1 inhibitors. The consensus model was validated using an external validation set. The model achieved high sensitivity, specificity, and overall accuracy along with low false positive rate (FPR) and false discovery rate (FDR). The performance of model on the external validation set and training set were comparable, thus it was unlikely to be overfitted. The low FPR and low FDR will increase its accuracy in screening large chemical libraries. Screening of ZINC library resulted in 64,772 compounds as probable N1 inhibitors, while 173,674 compounds were defined to be outside the AD of the consensus model. The advantage of the current model is that it was developed using a large and diverse dataset and has a defined AD which prevents its use on compounds that it is not capable of predicting. The consensus model developed in this study is made available via the free software, PaDEL-DDPredictor.     

Public debates driven by incomplete scientific data: The cases of evolution theory, global warming and H1N1 pandemic influenza

Public debates driven by incomplete scientific data where nobody can claim absolute certainty, due to the current state of scientific knowledge, are studied. The cases of evolution theory, global warming and H1N1 pandemic influenza are investigated. The first two are of controversial impact while the third is more neutral and resolved. To adopt a cautious balanced attitude based on clear but inconclusive data appears to be a lose-out strategy. In contrast overstating arguments with incorrect claims which cannot be scientifically refuted appears to be necessary but not sufficient to eventually win a public debate. The underlying key mechanisms of these puzzling and unfortunate conclusions are identified using the Galam sequential probabilistic model of opinion dynamics (Galam, 2002 [4], Galam, 2005 [18], Galam and Jacobs, 2007 [19]). It reveals that the existence of inflexible agents and their respective proportions are the instrumental parameters to determine the faith of incomplete scientific data in public debates. Acting on one’s own inflexible proportion modifies the topology of the flow diagram, which in turn can make irrelevant initial supports. On the contrary focusing on open-minded agents may be useless given some topologies. When the evidence is not as strong as claimed, the inflexibles rather than the data are found to drive the opinion of the population. The results shed a new but disturbing light on designing adequate strategies to win a public debate.     

Time evolution of non-lethal infectious diseases: a semi-continuous approach

A model describing the dynamics related to the spreading of non-lethal infectious diseases in a fixed-size population is proposed. The model consists of a non-linear delay-differential equation describing the time evolution of the increment in the number of infectious individuals and depends upon a limited number of parameters. Predictions are in good qualitative agreement with data on influenza, which is taken to be a representative type of non-lethal infectious disease.     

Magnetic shape anisotropy calculations of Fe nanostructures at the Si/Fe(1 1 1) interface

The influence of Si capping layers on the magnetic properties of thin Fe films grown on Si(1 1 1) has been studied by means of shape anisotropy calculations. Fe surface morphology simulations are realized using experimental STM data. Surface modifications induced by the interaction between the Si overlayer and the Fe surface are performed in agreement with the model proposed in a previous work by Stephan et al. [J. Magn. Mater. 293 (2005) 746]. Calculations of the uniaxial anisotropy energy constant K_u are then performed on the modified Fe surface morphology for different Si deposition geometries as proposed in the model. The relevant data deduced by this method such as anisotropy constants and their related easy axis direction, are directly compared to the experimental ones obtained by ex situ magneto-optical Kerr effect (MOKE) measurements at room temperature using the transverse bias initial inverse susceptibility and torque (TBIIST) method. We show that a very good agreement between those results leads to a confirmation of the proposed model.     

Mathematical models for dengue fever epidemiology: A 10-year systematic review

Mathematical models have a long history in epidemiological research, and as the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading.Mathematical models describing dengue fever epidemiological dynamics are found back from 1970. Dengue fever is a viral mosquito-borne infection caused by four antigenically related but distinct serotypes (DENV-1 to DENV-4). With 2.5 billion people at risk of acquiring the infection, it is a major international public health concern. Although most of the cases are asymptomatic or mild, the disease immunological response is complex, with severe disease linked to the antibody-dependent enhancement (ADE) - a disease augmentation phenomenon where pre-existing antibodies to previous dengue infection do not neutralize but rather enhance the new infection. Here, we present a 10-year systematic review on mathematical models for dengue fever epidemiology. Specifically, we review multi-strain frameworks describing host-to-host and vector-host transmission models and within-host models describing viral replication and the respective immune response.Following a detailed literature search in standard scientific databases, different mathematical models in terms of their scope, analytical approach and structural form, including model validation and parameter estimation using empirical data, are described and analyzed.Aiming to identify a consensus on infectious diseases modeling aspects that can contribute to public health authorities for disease control, we revise the current understanding of epidemiological and immunological factors influencing the transmission dynamics of dengue. This review provide insights on general features to be considered to model aspects of real-world public health problems, such as the current epidemiological scenario we are living in.     

A simple statistical physics model for the epidemic with incubation period

Based on the classical SIR model, we derive a simple modification for the dynamics of epidemics with a known incubation period of infection. The model is described by a system of integro-differential equations. Parameters of our model are directly related to epidemiological data. We derive some analytical results, as well as perform numerical simulations. We use the proposed model to analyze COVID-19 epidemic data in Armenia.     

NMR 1D-imaging of water infiltration into mesoporous matrices

It is shown that coupling nuclear magnetic resonance (NMR) 1D-imaging with the measure of NMR relaxation times and self-diffusion coefficients can be a very powerful approach to investigate fluid infiltration into porous media. Such an experimental design was used to study the very slow seeping of pure water into hydrophobic materials. We consider here three model samples of nuclear waste conditioning matrices which consist in a dispersion of NaNO₃ (highly soluble) and/or BaSO₄ (poorly soluble) salt grains embedded in a bitumen matrix. Beyond studying the moisture progression according to the sample depth, we analyze the water NMR relaxation times and self-diffusion coefficients along its 1D-concentration profile to obtain spatially resolved information on the solution properties and on the porous structure at different scales. It is also shown that, when the relaxation or self-diffusion properties are multimodal, the 1D-profile of each water population is recovered. Three main levels of information were disclosed along the depth-profiles. They concern (i) the water uptake kinetics, (ii) the salinity and the molecular dynamics of the infiltrated solutions and (iii) the microstructure of the water-filled porosities: open networks coexisting with closed pores. All these findings were fully validated and enriched by NMR cryoporometry experiments and by performing environmental scanning electronic microscopy observations. Surprisingly, results clearly show that insoluble salts enhance the water progression and thereby increase the capability of the material to uptake water.     

A generation-attraction model for renewable energy flows in Italy: A complex network approach

In recent years, in Italy, the trend of the electricity demand and the need to connect a large number of renewable energy power generators to the power-grid, developed a novel type of energy transmission/distribution infrastructure. The Italian Transmission System Operator (TSO) and the Distribution System Operator (DSO), worked on a new infrastructural model, based on electronic meters and information technology. In pursuing this objective it is crucial importance to understand how even more larger shares of renewable energy can be fully integrated, providing a constant and reliable energy background over space and time. This is particularly true for intermittent sources as photovoltaic installations due to the fine-grained distribution of them across the Country. In this work we use an over-simplified model to characterize the Italian power grid as a graph whose nodes are Italian municipalities and the edges cross the administrative boundaries between a selected municipality and its first neighbours, following a Delaunay triangulation. Our aim is to describe the power flow as a diffusion process over a network, and using open data on the solar irradiation at the ground level, we estimate the production of photovoltaic energy in each node. An attraction index was also defined using demographic data, in accordance with average per capita energy consumption data. The available energy on each node was calculated by finding the stationary state of a generation-attraction model.     

A semi-Markov model for price returns

We study the high frequency price dynamics of traded stocks by a model of returns using a semi-Markov approach. More precisely we assume that the intraday returns are described by a discrete time homogeneous semi-Markov process and the overnight returns are modeled by a Markov chain. Based on this assumptions we derived the equations for the first passage time distribution and the volatility autocorrelation function. Theoretical results have been compared with empirical findings from real data. In particular we analyzed high frequency data from the Italian stock market from 1 January 2007 until the end of December 2010. The semi-Markov hypothesis is also tested through a nonparametric test of hypothesis.     

Instabilities in population dynamics

Biologists have long known that the smaller the population, the more susceptible it is to extinction from various causes. Biologists define minimum viable population size (MVP), which is the critical population size, below which the population has a very small chance to survive. There are several theoretical models for predicting the probability that a small population will become extinct. But these models either embody unrealistic assumptions or lead to currently unresolved mathematical problems. In other popular models of population dynamics, like the logistic model, MVP does not exist. In this paper we find the existence of such a critical concentration in a simple model of evolution. We solve this model by a mean field theory and show, in one and two dimensions, the existence of the critical adaptation and concentration below which a population dies out. We also show that, like in the logistic model, above the critical value a population reaches its carrying capacity. Moreover, in the two-dimensional case we find - the so common in biological models - periodic solutions and their biffurcations. Received 15 February 2000     

Experimental determination of the helium 2(3)P(1)-1(1)S0 transition rate

We present the first experimental determination of the 2(3)P(1)-1(1)S0 transition rate in helium and compare this measurement with theoretical quantum-electrodynamic predictions. The experiment exploits the very long (approximately 1 minute) confinement times obtained for atoms magneto-optically trapped in an apparatus used to create a Bose-Einstein condensate of metastable (2(3)S1) helium. The 2(3)P(1)-1(1)S0 transition rate is measured directly from the decay rate of the cold atomic cloud following 1083 nm laser excitation from the 2(3)S1 to the 2(3)P1 state, and from accurate knowledge of the 2(3)P1 population. The value obtained is 177+/-8 s(-1), which agrees very well with theoretical predictions, and has an accuracy that compares favorably with measurements for the same transition in heliumlike ions higher in the isoelectronic sequence.     

X-ray photoelectron diffraction study of Cu(1 1 1): Multiple scattering investigation

Multiple scattering theory based on a cluster model is used to simulate full-hemispherical X-ray photoelectron diffraction measurements in order to verify how state of the art multiple scattering simulations are able to reproduce the experiment. This approach is applied to the Cu(1 1 1) surface for two different photoelectron kinetic energies. Differences and similarities between single and multiple scattering are discussed in comparison with experimental results. We find that the present approach gives very good results despite some limitations.     

Trapping, molecular adsorption, and precursors for nonactivated chemisorption

Many fundamental questions are still unanswered regarding the very existence of precursor states and the microscopic mechanism of its population in the case of dissociative adsorption of light molecules on metal surfaces. We report results of classical trajectory calculations for H(2)/Pd(110) based on a six-dimensional potential energy surface obtained from ab initio calculations and a generalized Langevin oscillator model to account for energy exchange with the surface and dissipation. A quantitative characterization of the dynamic process is obtained which elucidates the respective roles played by direct dissociation, dynamic trapping, and precursor mediated adsorption. We predict the existence of H2 molecular chemisorption and provide the precise adsorption conditions for its experimental observation.     

Scale-free distribution of avian influenza outbreaks

Using global case data for the period from 25 November 2003 to 10 March 2007, we construct a network of plausible transmission pathways for the spread of avian influenza among domestic and wild birds. The network structure we obtain is complex and exhibits scale-free (although not necessarily small-world) properties. Communities within this network are connected with a distribution of links with infinite variance. Hence, the disease transmission model does not exhibit a threshold and so the infection will continue to propagate even with very low transmissibility. Consequentially, eradication with methods applicable to locally homogeneous populations is not possible. Any control measure needs to focus explicitly on the hubs within this network structure.     

Sexually transmitted infections and the marriage problem

We study an SIS epidemiological model for a sexually transmitted infection in a monogamous population where the formation and breaking of couples is governed by individual preferences. The mechanism of couple recombination is based on the so-called bar dynamics for the marriage problem. We compare the results with those of random recombination – where no individual preferences exist – for which we calculate analytically the infection incidence and the endemic threshold. We find that individual preferences give rise to a large dispersion in the average duration of different couples, causing substantial changes in the incidence of the infection and in the endemic threshold. Our analysis yields also new results on the bar dynamics, that may be of interest beyond the field of epidemiological models.     

甲型流感病毒DNA序列的长记忆ARFIMA模型

流感病毒分为三类:甲型(A型),乙型(B型),丙型(C型).在这三种类型中甲型(A型)流感病毒是最致命的流感病毒,对人类引起了严重疾病.本文对甲型流感病毒DNA序列建立了一种新的时间序列模型,即CGR(Chaos Game Representation)弧度序列.利用CGR坐标将甲流病毒DNA序列转换成CGR弧度序列,且引入长记忆ARFIMA模型去拟合此类序列,发现随机找来的10条H1N1序列,10条H3N2序列都具有长相关性且拟合很好,并且还发现这两种序列可以尝试用不同的ARFIMA模型去识别,其中H1 关键词： 甲型流感 时间序列模型 CGR (p')" href="#">ARFIMA(p d 模型')" href="#">q)模型