Parasite spill-back from domestic hosts may induce an Allee effect in wildlife hosts

The exchange of native pathogens between wild and domesticated animals can lead to novel disease dynamics. A simple model reveals that the spill-back of native parasites\ud from domestic to wild hosts may cause a demographic Allee effect. Because parasite spill-over and spill-back decouples the abundance of parasite infectious stages from the abundance of the wild host population, parasitism and mortality of the wild host population increases non-linearly as host abundance decreases. Analogous to the effects of satiation of generalist predators, parasite spill-back can produce an unstable equilibrium in the abundance of the host population above which the host population persists and below which it is at risk of extirpation. These effects are likely to be most pronounced in systems where the parasite has a high efficiency of transmission from domestic to wild host populations due to prolonged sympatry, disease vectors, or proximity of domesticated populations to wildlife migratory corridors

The dynamics of Ascaris lumbricoides infections

The Anderson–May model of human parasite infections and specifically that for the intestinal worm Ascaris lumbricoides is reconsidered, with a view to deriving the observed characteristic negative binomial distribution which is frequently found in human communities. The means to obtaining this result lies in reformulating the continuous Anderson–May model as a stochastic process involving two essential populations, the density of mature worms in the gut, and the density of mature eggs in the environment. The resulting partial differential equation for the generating function of the joint probability distribution of eggs and worms can be partially solved in the appropriate limit where the worm lifetime is much greater than that of the mature eggs in the environment. Allowing for a mean field nonlinearity, and for egg immigration from neighbouring communities, a negative binomial worm distribution can be predicted, whose parameters are determined by those in the continuous Anderson–May model; this result assumes no variability in predisposition to the infection

Modeling, analysis and defense strategies against Internet attacks.

Third, we have analyzed the tradeoff between delay caused by filtering of worms at routers, and the delay due to worms' excessive amount of network traffic. We have used the optimal control problem, to determine the appropriate tradeoffs between these two delays for a given rate of a worm spreading. Using our technique we can minimize the overall network delay by finding the number of routers that should perform filtering and the time at which they should start the filtering process.Many early Internet protocols were designed without a fundamentally secure infrastructure and hence vulnerable to attacks such as denial of service (DoS) attacks and worms. DoS attacks attempt to consume the resources of a remote host or network, thereby denying or degrading service to legitimate users. Network forensics is an emerging area wherein the source or the cause of the attacker is determined using IDS tools. The problem of finding the source(s) of attack(s) is called the "trace back problem". Lately, Internet worms have become a major problem for the security of computer networks, causing considerable amount of resources and time to be spent recovering from the disruption of systems. In addition to breaking down victims, these worms create large amounts of unnecessary network data traffic that results in network congestion, thereby affecting the entire network.In this dissertation, first we solve the trace back problem more efficiently in terms of the number of routers needed to complete the track back. We provide an efficient algorithm to decompose a network into connected components and construct a terminal network. We show that for a terminal network with n routers, the trace back can be completed in O(log n) steps.Second, we apply two classical epidemic SIS and SIR models to study the spread of Internet Worm. The analytical models that we provide are useful in determining the rate of spread and time required to infect a majority of the nodes in the network. Our simulation results on large Internet like topologies show that in a fairly small amount of time, 80% of the network nodes is infected

Deciphering infant mortality. Part 1: empirical evidence

This paper is not (or at least not only) about human infant mortality. In line with reliability theory, "infant" will refer here to the time interval following birth during which the mortality (or failure) rate decreases. This definition provides a systems science perspective in which birth constitutes a sudden transition which falls within the field of application of the "Transient Shock" (TS) conjecture put forward in Richmond et al. (2016c). This conjecture provides predictions about the timing and shape of the death rate peak. (i) It says that there will be a death rate spike whenever external conditions change abruptly and drastically. (ii) It predicts that after a steep rising there will be a much longer hyperbolic relaxation process. These predictions can be tested by considering living organisms for which birth is a multi-step process. Thus, for fish there are three states: egg, yolk-sac phase, young adult. The TS conjecture predicts a mortality spike at the end of the yolk-sac phase, and this timing is indeed confirmed by observation. Secondly, the hyperbolic nature of the relaxation process can be tested using high accuracy Swiss statistics which give postnatal death rates from one hour after birth up to the age of 10 years. It turns out that since the 19th century despite a great overall reduction in infant mortality, the shape of the age-specific death rate has remained basically unchanged. This hyperbolic pattern is not specific to humans. It can also be found in small primates as recorded in the archives of zoological gardens. Our ultimate objective is to set up a chain of cases which starts from simple systems and then moves up step by step to more complex organisms. The cases discussed here can be seen as initial landmarks.Comment: 46 pages, 14 figures, 4 table

Graph-theoretic Approach To Modeling Propagation And Control Of Network Worms

In today\u27s network-dependent society, cyber attacks with network worms have become the predominant threat to confidentiality, integrity, and availability of network computing resources. Despite ongoing research efforts, there is still no comprehensive network-security solution aimed at controling large-scale worm propagation. The aim of this work is fivefold: (1) Developing an accurate combinatorial model of worm propagation that can facilitate the analysis of worm control strategies, (2) Building an accurate epidemiological model for the propagation of a worm employing local strategies, (3) Devising distributed architecture and algorithms for detection of worm scanning activities, (4) Designing effective control strategies against the worm, and (5) Simulation of the developed models and strategies on large, scale-free graphs representing real-world communication networks. The proposed pair-approximation model uses the information about the network structure--order, size, degree distribution, and transitivity. The empirical study of propagation on large scale-free graphs is in agreement with the theoretical analysis of the proposed pair-approximation model. We, then, describe a natural generalization of the classical cops-and-robbers game--a combinatorial model of worm propagation and control. With the help of this game on graphs, we show that the problem of containing the worm is NP-hard. Six novel near-optimal control strategies are devised: combination of static and dynamic immunization, reactive dynamic and invariant dynamic immunization, soft quarantining, predictive traffic-blocking, and contact-tracing. The analysis of the predictive dynamic traffic-blocking, employing only local information, shows that the worm can be contained so that 40\% of the network nodes are not affected. Finally, we develop the Detection via Distributed Blackholes architecture and algorithm which reflect the propagation strategy used by the worm and the salient properties of the network. Our distributed detection algorithm can detect the worm scanning activity when only 1.5% of the network has been affected by the propagation. The proposed models and algorithms are analyzed with an individual-based simulation of worm propagation on realistic scale-free topologies

Parasitic nematodes in humans : exploring the host-parasite dynamic through historical, biological, and public health evaluations of infection.

This thesis investigated infection dynamics of parasitic nematodes at both the population and individual levels by exploring evolutionary and historical aspects of infection as well as how host-parasite interactions influence virulence. In particular, this thesis sought to answer questions of how host populations have influenced the spread of infection and how transmission determines infection virulence, with a final goal of understanding how eradication programs for parasites can be developed or improved with this knowledge. The host-parasite dynamic was explored throughout history, with particular focus on the ways host populations have shaped infection distribution in present, historic, and prehistoric times. Then, data for each nematode was systemically collected and presented for a comprehensive analysis of virulence and transmission mode. It was discovered that microparasitic principles of virulence can be applied limitedly to predict virulence of macroparasitic nematodes, and the relative virulence of each nematode can be explained partially by transmission mode

Complex Ecological Dynamics and Eradicability of the Vector Borne Macroparasitic Disease, Lymphatic Filariasis

The current global efforts to control the morbidity and mortality caused by infectious diseases affecting developing countries--such as HIV/AIDS, polio, tuberculosis, malaria and the Neglected Tropical Diseases (NTDs)-have led to an increasing focus on the biological controllability or eradicability of disease transmission by management action. Here, we use an age-structured dynamical model of lymphatic filariasis transmission to show how a quantitative understanding of the dynamic processes underlying infection persistence and extinction is key to evaluating the eradicability of this macroparasitic disease.We investigated the persistence and extinction dynamics of lymphatic filariasis by undertaking a numerical equilibrium analysis of a deterministic model of parasite transmission, based on varying values of the initial L3 larval density in the system. The results highlighted the likely occurrence of complex dynamics in parasite transmission with three major outcomes for the eradicability of filariasis. First, both vector biting and worm breakpoint thresholds are shown to be complex dynamic entities with values dependent on the nature and magnitude of vector-and host specific density-dependent processes and the degree of host infection aggregation prevailing in endemic communities. Second, these thresholds as well as the potential size of the attractor domains and hence system resilience are strongly dependent on peculiarities of infection dynamics in different vector species. Finally, the existence of multiple stable states indicates the presence of hysteresis nonlinearity in the filariasis system dynamics in which infection thresholds for infection invasion are lower but occur at higher biting rates than do the corresponding thresholds for parasite elimination.The variable dynamic nature of thresholds and parasite system resilience reflecting both initial conditions and vector species-infection specificities, and the existence of hysteresis loop phenomenon, suggests that eradication of filariasis may require taking a more flexible and locally relevant approach to designing elimination programmes compared to the current command and control approach advocated by the global programme

Stochastic propagation modeling and early detection of malicious mobile code

Epidemic models are commonly used to model the propagation of malicious mobile code like a computer virus or a worm. In this dissertation, we introduce stochastic techniques to describe the propagation behavior of malicious mobile code. We propose a stochastic infection-immunization (INIM) model based on the standard Susceptible-Infected-Removed (SIR) epidemic model, and we get an explicit solution of this model using probability generating function (pgf.). Our experiments simulate the propagation of malicious mobile code with immunization. The simulation results match the theoretical results of the model, which indicates that it is reliable to use INIM model to predict the propagation of malicious mobile code at the early infection stage when immunization factor is considered. In this dissertation, we also propose a control system that could automatically detect and mitigate the propagation of malicious mobile programs at the early infection stage. The detection method is based on the observation that a worm always opens as many connections as possible in order to propagate as fast as possible. To develop the detection algorithm, we extend the traditional statistical process control technique by adding a sliding window. We do the experiment to demonstrate the training process and testing process of a control system using both real and simulation data set. The experiment results show that the control system detects the propagation of malicious mobile code with zero false negative rate and less than 6% false positive rate. Moreover, we introduce risk analysis using Sequential Probability Ratio Test (SPRT) to limit the false positive rate. Examples of risk control using SPTR are presented. Furthermore, we analyze the network behavior using the propagation models we developed to evaluate the effect of the control system in a network environment. The theoretical analysis of the model shows that the propagation of malicious program is reduced when hosts in a network applied the control system. To verify the theoretical result, we also develop the experiment to simulate the propagation process in a network. The experiment results match the mathematical results