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The literature on the importance of plant pathogens sometimes emphasizes their possible role in historical food shortages and even in famines. Aside from such major crises, plant pathogens should also be seen as important reducers...
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The literature on the importance of plant pathogens sometimes emphasizes their possible role in historical food shortages and even in famines. Aside from such major crises, plant pathogens should also be seen as important reducers of crop performances, with impacts on system sustainability, from the ecological, agronomical, social, and economic standpoints - all contributing ultimately to affecting food security. These views need reconciliation in order to produce a clearer picture of the multidimensional effects of plant disease epidemics. Such a picture is needed for disease management today, but would also be useful for future policies. This article attempts to develop a framework that would enable assessment of the impacts of plant diseases, referred collectively to as crop health, on food security via its components. We have combined three different existing definitions of food security in order to develop a framework consisting of the following six components: (1) Availability. Primary production; (2) Availability. Import - Stockpiles; (3) Access. Physical and supply chain; (4) Access. Economic; (5) Stability of food availability; (6) Utility-Safety-Quality-Nutritive value. In this framework, components of food security are combined with three attributes of production situations: the nature of the considered crop (i.e. food- or non-food), the structure of farms (i.e. subsistence or commercial), and the structure of markets (i.e. weakly organized and local, to strongly organized and globalized). The resulting matrix: [Food security components] x [Attributes of production situations] provides a framework where the impacts of chronic, acute, and emerging plant disease epidemics on food security can be examined. We propose that, given the number of components and interactions at play, a systems modelling approach is required to address the functioning of food systems exposed to plant disease risks. This approach would have application in both the management of the current attrition of crop performances by plant diseases, and also of possible disease-induced shocks. Such an approach would also enable quantifying shifts in disease vulnerability of production situations, and therefore, of food systems, as a result of climate change, globalization, and evolving crop health.
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Understanding the dynamics of computer virus (malware, worm) in cyberspace is an important problem that has attracted a fair amount of attention. Early investigations for this purpose adapted biological epidemic models, and thus i...
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Understanding the dynamics of computer virus (malware, worm) in cyberspace is an important problem that has attracted a fair amount of attention. Early investigations for this purpose adapted biological epidemic models, and thus inherited the so-called homogeneity assumption that each node is equally connected to others. Later studies relaxed this often unrealistic homogeneity assumption, but still focused on certain power-law networks. Recently, researchers investigated epidemic models in arbitrary networks (i.e., no restrictions on network topology). However, all these models only capture push-based infection, namely that an infectious node always actively attempts to infect its neighboring nodes. Very recently, the concept of pull-based infection was introduced but was not treated rigorously. Along this line of research, the present article investigates push- and pull-based epidemic spreading dynamics in arbitrary networks, using a nonlinear dynamical systems approach. The article advances the state-of-the-art as follows: (1) It presents a more general and powerful sufficient condition (also known as epidemic threshold in the literature) under which the spreading will become stable. (2) It gives both upper and lower bounds on the global mean infection rate, regardless of the stability of the spreading. (3) It offers insights into, among other things, the estimation of the global mean infection rate through localized monitoring of a small constant number of nodes, without knowing the values of the parameters.
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In real-world networks of disease transmission, the incidence of infection among individuals conforms to a certain fixed probability of effective contact between them, which must meet some necessary conditions for the disease to c...
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In real-world networks of disease transmission, the incidence of infection among individuals conforms to a certain fixed probability of effective contact between them, which must meet some necessary conditions for the disease to continue to spread. Based on susceptible/infective/removed (SIR) models in homogeneous or heterogeneous networks, we find that these models evolve dynamically just like in networks without connectivity fluctuations if all the susceptible individuals are supposed to have the same effective contact. This means that effectively heterogeneous contacts play a striking role in epidemic dynamics. To go a step further, we introduce the effective contact function (ECF) into models and present an analytical and numerical study for the threshold and dynamical behaviors of epidemic incidence. The power-law and proportional ECFs are considered, and, we demonstrate analytically that the epidemic incidence is generally a monotone decreasing function of the epidemic threshold and increasing function of the number of effective contacts. Certain exceptional cases are also discussed. This tells us that we cannot always focus on the threshold to evaluate the extent of epidemic outbreaks.
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Epidemics and pandemics are a field of scientific research since ancient times. The intensity of the repeated phenomena demonstrates their cyclicality in time. The ongoing COVID-19 pandemic, also known as the coronavirus pandemic,...
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Epidemics and pandemics are a field of scientific research since ancient times. The intensity of the repeated phenomena demonstrates their cyclicality in time. The ongoing COVID-19 pandemic, also known as the coronavirus pandemic, confirmed observations made in previous disease outbreaks. Epidemics are mainly characterized by two factors: (a) the population dynamics and (b) the nature of the disease. This article uses continuous mathematical models, on the basis of a scalable compartmental approach, characterized by systems of ordinary differential equations under the condition that individuals can freely move from one compartment to another. Numerous experiments were carried out to examine the impact of quarantine and vaccination policies, separately or in combination, on cumulative viral load, a measure adopted to reflect the cumulative viral burden of an infected population for a given time period. Current findings demonstrate that quarantine may play a crucial role in controlling an epidemic at its early stages, as well as the importance of early and widespread implementation of a vaccination program. The suggested approach may be utilized to study specific quarantine and vaccination scenarios, by manipulating various parameters such as the duration and extent of social distancing measures or the effectiveness and compliance to vaccination policies, and thus assist in decision making.
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Background Defining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute to the pandemic preparedness. The present study aimed to determine the...
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Background Defining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute to the pandemic preparedness. The present study aimed to determine the epidemic and intensity thresholds of influenza season in Tunisia using the moving epidemic method. Methods We applied the moving epidemic method (MEM) using the R Language implementation (package “mem”). We have calculated the epidemic and the different intensity thresholds from historical data of the past nine influenza seasons (2009‐2010 to 2017‐2018) and assessed the impact of the 2009‐2010 pandemic year. Data used were the weekly influenza‐like illness (ILI) proportions compared with all outpatient acute consultations. The goodness of the model was assessed using a cross validation procedure. Results The average duration of influenza epidemic during a typical season was 20?weeks and ranged from 11?weeks (2009‐2010 season) to 23?weeks (2015‐2016 season). The epidemic threshold with the exclusion of the pandemic season was 6.25%. It had a very high sensitivity of 85% and a high specificity of 69%. The different levels of intensity were established as follows: low, if ILI proportion is below 9.74%, medium below 12.05%; high below 13.27%; and very high above this last rate. Conclusions This is the first mathematically based study of seasonal threshold of influenza in Tunisia. As in other studies in different countries, the model has shown both good specificity and sensitivity, which allows timely and accurate detection of the start of influenza seasons. The findings will contribute to the development of more efficient measures for influenza prevention and control.
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The evolution of epidemics based on the Susceptible-Infected-Susceptible (SIS) model re-lies on the density of infected individuals rho. Recent results show that the mean density (rho) and its variance Sigma 2 can be regarded as c...
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The evolution of epidemics based on the Susceptible-Infected-Susceptible (SIS) model re-lies on the density of infected individuals rho. Recent results show that the mean density (rho) and its variance Sigma 2 can be regarded as canonical variables and obey Hamilton's equations. Using the Hamiltonian formulation, we study the SIS system coupled to a Nose thermal bath. We reinterpret classical parameters like temperature in an epidemiological context. In contrast to classical epidemiological models, the thermal bath modifies the dynamical behavior of the system by introducing fluctuations, such as those seen in some infectious waves. We study the stability and show that (rho) tends to be half of the value predicted by the original SIS model.(c) 2022 Elsevier Inc. All rights reserved.
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This paper is concerned with estimation of the within-household infection rate for a susceptible infective recovered epidemic among a population of households, from observation of the early, exponentially growing phase of an epide...
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This paper is concerned with estimation of the within-household infection rate for a susceptible infective recovered epidemic among a population of households, from observation of the early, exponentially growing phase of an epidemic. Specifically, it is assumed that an estimate of the exponential growth rate is available from general data on an emerging epidemic and more-detailed, household-level data are available in a sample of households. Estimates of obtained using the final size distribution of single-household epidemics are usually biased owing to the emerging nature of the epidemic. A new method, which accounts correctly for the emerging nature of the epidemic, is developed by exploiting the asymptotic theory of supercritical branching processes and proved to yield a strongly consistent estimator of as the population and sampled households both tend to infinity in an appropriate fashion. The theory is illustrated by simulations which demonstrate that the new method is feasible for finite populations and numerical studies are used to explore how changes to the parameters governing the spread of an epidemic affect the bias of estimates based on single-household final size distributions.
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Theoretical modeling of computer virus/worm epidemic dynamics is an important problem that has attracted many studies. However, most existing models are adapted from biological epidemic ones. Although biological epidemic models ca...
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Theoretical modeling of computer virus/worm epidemic dynamics is an important problem that has attracted many studies. However, most existing models are adapted from biological epidemic ones. Although biological epidemic models can certainly be adapted to capture some computer virus spreading scenarios (especially when the so-called homogeneity assumption holds), the problem of computer virus spreading is not well understood because it has many important perspectives that are not necessarily accommodated in the biological epidemic models. In this article, we initiate the study of such a perspective, namely that of adaptive defense against epidemic spreading in arbitrary networks. More specifically, we investigate a nonhomoge-neous Susceptible-Infectious-Susceptible (SIS) model where the model parameters may vary with respect to time. In particular, we focus on two scenarios we call semi-adaptive defense and fully adaptive defense, which accommodate implicit and explicit dependency relationships between the model parameters, respectively. In the semi-adaptive defense scenario, the model's input parameters are given; the defense is semi-adaptive because the adjustment is implicitly dependent upon the outcome of virus spreading. For this scenario, we present a set of sufficient conditions (some are more general or succinct than others) under which the virus spreading will die out; such sufficient conditions are also known as epidemic thresholds in the literature. In the fully adaptive defense scenario, some input parameters are not known (i.e., the aforementioned sufficient conditions are not applicable) but the defender can observe the outcome of virus spreading. For this scenario, we present adaptive control strategies under which the virus spreading will die out or will be contained to a desired level.
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Starting with the power law for the total number of detected infections, we propose differential equations describing the effect of momentum epidemic management. Our 2-phase formula matches very well the curves of the total number...
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Starting with the power law for the total number of detected infections, we propose differential equations describing the effect of momentum epidemic management. Our 2-phase formula matches very well the curves of the total numbers of the Covid-19 infection in many countries; the first phase is described by Bessel functions. It provides projections for the saturation, assuming that the management is steady. We discuss Austria, Brazil, Germany, Japan, India, Israel, Italy, the Netherlands, Sweden, Switzerland, UK, and the USA, including some analysis of the second waves. (C) 2020 Elsevier Ltd. All rights reserved.
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This paper is concerned with the definition and calculation of containment probabilities for emerging disease epidemics. A general multitype branching process is used to model an emerging infectious disease in a population of hous...
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This paper is concerned with the definition and calculation of containment probabilities for emerging disease epidemics. A general multitype branching process is used to model an emerging infectious disease in a population of households. It is shown that the containment probability satisfies a certain fixed point equation which has a unique solution under certain conditions; the case of multiple solutions is also described. The extinction probability of the branching process is shown to be a special case of the containment probability. It is shown that Laplace transform ordering of the severity distributions of households in different epidemics yields an ordering on the containment probabilities. The results are illustrated with both standard epidemic models and a specific model for an emerging strain of influenza.
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