Number Of Infected Hosts Research Articles

Contribution of host species and pathogen clade to snake fungal disease hotspots in Europe

Infectious diseases are influenced by interactions between host and pathogen, and the number of infected hosts is rarely homogenous across the landscape. Areas with elevated pathogen prevalence can maintain a high force of infection and may indicate areas with disease impacts on host populations. However, isolating the ecological processes that result in increases in infection prevalence and intensity remains a challenge. Here we elucidate the contribution of pathogen clade and host species in disease hotspots caused by Ophidiomyces ophidiicola, the pathogen responsible for snake fungal disease, in 21 species of snakes infected with multiple pathogen strains across 10 countries in Europe. We found isolated areas of disease hotspots in a landscape where infections were otherwise low. O. ophidiicola clade had important effects on transmission, and areas with multiple pathogen clades had higher host infection prevalence. Snake species further influenced infection, with most positive detections coming from species within the Natrix genus. Our results suggest that both host and pathogen identity are essential components contributing to increased pathogen prevalence.

Balancing economic and epidemiological interventions in the early stages of pathogen emergence.

The global pandemic of COVID-19 has underlined the need for more coordinated responses to emergent pathogens. These responses need to balance epidemic control in ways that concomitantly minimize hospitalizations and economic damages. We develop a hybrid economic-epidemiological modeling framework that allows us to examine the interaction between economic and health impacts over the first period of pathogen emergence when lockdown, testing, and isolation are the only means of containing the epidemic. This operational mathematical setting allows us to determine the optimal policy interventions under a variety of scenarios that might prevail in the first period of a large-scale epidemic outbreak. Combining testing with isolation emerges as a more effective policy than lockdowns, substantially reducing deaths and the number of infected hosts, at lower economic cost. If a lockdown is put in place early in the course of the epidemic, it always dominates the "laissez-faire" policy of doing nothing.

Unraveling the epidemiology of Mycobacterium bovis using whole-genome sequencing combined with environmental and demographic data.

When studying the dynamics of a pathogen in a host population, one crucial question is whether it transitioned from an epidemic (i.e., the pathogen population and the number of infected hosts are increasing) to an endemic stable state (i.e., the pathogen population reached an equilibrium). For slow-growing and slow-evolving clonal pathogens such as Mycobacterium bovis, the causative agent of bovine (or animal) and zoonotic tuberculosis, it can be challenging to discriminate between these two states. This is a result of the combination of suboptimal detection tests so that the actual extent of the pathogen prevalence is often unknown, as well as of the low genetic diversity, which can hide the temporal signal provided by the accumulation of mutations in the bacterial DNA. In recent years, the increased availability, efficiency, and reliability of genomic reading techniques, such as whole-genome sequencing (WGS), have significantly increased the amount of information we can use to study infectious diseases, and therefore, it has improved the precision of epidemiological inferences for pathogens such as M. bovis. In this study, we use WGS to gain insights into the epidemiology of M. bovis in Cameroon, a developing country where the pathogen has been reported for decades. A total of 91 high-quality sequences were obtained from tissue samples collected in four abattoirs, 64 of which were with complete metadata. We combined these with environmental, demographic, ecological, and cattle movement data to generate inferences using phylodynamic models. Our findings suggest M. bovis in Cameroon is slowly expanding its epidemiological range over time; therefore, endemic stability is unlikely. This suggests that animal movement plays an important role in transmission. The simultaneous prevalence of M. bovis in co-located cattle and humans highlights the risk of such transmission being zoonotic. Therefore, using genomic tools as part of surveillance would vastly improve our understanding of disease ecology and control strategies.

Quiescence Generates Moving Average in a Stochastic Epidemiological Model with One Host and Two Parasites

Mathematical modelling of epidemiological and coevolutionary dynamics is widely being used to improve disease management strategies of infectious diseases. Many diseases present some form of intra-host quiescent stage, also known as covert infection, while others exhibit dormant stages in the environment. As quiescent/dormant stages can be resistant to drug, antibiotics, fungicide treatments, it is of practical relevance to study the influence of these two life-history traits on the coevolutionary dynamics. We develop first a deterministic coevolutionary model with two parasite types infecting one host type and study analytically the stability of the dynamical system. We specifically derive a stability condition for a five-by-five system of equations with quiescence. Second, we develop a stochastic version of the model to study the influence of quiescence on stochasticity of the system dynamics. We compute the steady state distribution of the parasite types which follows a multivariate normal distribution. Furthermore, we obtain numerical solutions for the covariance matrix of the system under symmetric and asymmetric quiescence rates between parasite types. When parasite strains are identical, quiescence increases the variance of the number of infected individuals at high transmission rate and vice versa when the transmission rate is low. However, when there is competition between parasite strains with different quiescent rates, quiescence generates a moving average behaviour which dampen off stochasticity and decreases the variance of the number of infected hosts. The strain with the highest rate of entering quiescence determines the strength of the moving average and the magnitude of reduction of stochasticity. Thus, it is worth investigating simple models of multi-strain parasite under quiescence/dormancy to improve disease management strategies.

Ecological specificity explains infection parameters of anuran parasites at different scales.

Understanding the determinants of parasite infection in different hosts is one of the main goals of disease ecology. Evaluating the relationship between parasite–host specificity and infection parameters within host communities and populations may contribute to this understanding. Here we propose two measures of specificity that encompasses phylogenetic and ecological relatedness among hosts and investigated how such metrics explain parasite infection prevalence and mean infection intensity (MII). We analysed the parasites associated with an anuran community in an area of Atlantic Forest and used the number of infected hosts and the net relatedness index to calculate the phylogenetic and ecological specificities of the parasites. These specificity measures were related to infection metrics (prevalence and MII) with generalized linear mixed models at community (all hosts) and population (infected host species) scales. Parasite prevalence was correlated with the number of infected hosts and, when considering only multi-host parasites, was positively related to parasite ecological specificity at community and population scales. Thus, parasite species have similar prevalences in ecologically closer hosts. No relationship was found for parasite MII. Incorporating ecological characteristics of hosts in parasite specificity analyses improves the detection of patterns of specificity across scales.

Dengue virus infection modifies mosquito blood-feeding behavior to increase transmission to the host

Mosquito blood-feeding behavior is a key determinant of the epidemiology of dengue viruses (DENV), the most-prevalent mosquito-borne viruses. However, despite its importance, how DENV infection influences mosquito blood-feeding and, consequently, transmission remains unclear. Here, we developed a high-resolution, video-based assay to observe the blood-feeding behavior of Aedes aegypti mosquitoes on mice. We then applied multivariate analysis on the high-throughput, unbiased data generated from the assay to ordinate behavioral parameters into complex behaviors. We showed that DENV infection increases mosquito attraction to the host and hinders its biting efficiency, the latter resulting in the infected mosquitoes biting more to reach similar blood repletion as uninfected mosquitoes. To examine how increased biting influences DENV transmission to the host, we established an invivo transmission model with immuno-competent mice and demonstrated that successive short probes result in multiple transmissions. Finally, to determine how DENV-induced alterations of host-seeking and biting behaviors influence dengue epidemiology, we integrated the behavioral data within a mathematical model. We calculated that the number of infected hosts per infected mosquito, as determined by the reproduction rate, tripled when mosquito behavior was influenced by DENV infection. Taken together, this multidisciplinary study details how DENV infection modulates mosquito blood-feeding behavior to increase vector capacity, proportionally aggravating DENV epidemiology. By elucidating the contribution of mosquito behavioral alterations on DENV transmission to the host, these results will inform epidemiological modeling to tailor improved interventions against dengue.

The dilution effect behind the scenes: testing the underlying assumptions of its mechanisms through quantifying the long-term dynamics and effects of a pathogen in multiple host species.

Disentangling the mechanisms that mediate the relationships between species diversity and disease risk has both theoretical and applied implications. We employed a model system of rodents and their Mycoplasma pathogens, in which an extreme negative diversity-disease relationship was demonstrated, to test the assumptions underlying three mechanisms that may explain this field pattern. Through quantifying the long-term dynamics and effects of the pathogen in its three host species, we estimated the between-host differences in pathogen spreading and transmission potentials, and host recovery potential and vulnerability to infection. The results suggest that one of the hosts is a pathogen amplifier and the other two hosts function as diluters. Considering the similarity in infection success and intensity among hosts, and the failure to detect any pathogen-induced damage, we could not validate the assumption underlying the hypotheses that diluters reduce the overall transmission or increase the mortality of infected hosts in the system. Instead, the results demonstrate that diluters clear the infection faster than amplifiers, supporting the possibility that the addition of diluters to the community may reduce the overall number of infected hosts through this mechanism. This study highlights the contribution of experimental studies that simultaneously explore different aspects of host-pathogen interactions in multiple hosts, in diversity-disease research.

Global Dynamics of a Vector-Borne Disease Model with Two Transmission Routes

In this paper, we study the dynamics of a vector-borne disease model with two transmission paths: direct transmission through contact and indirect transmission through vector. The direct transmission is considered to be a nonmonotone incidence function to describe the psychological effect of some severe diseases among the population when the number of infected hosts is large and/or the disease possesses high case fatality rate. The system has a disease-free equilibrium which is locally asymptotically stable when the basic reproduction number ([Formula: see text]) is less than unity and may have up to four endemic equilibria. Analytical expression representing the epidemic growth rate is obtained for the system. Sensitivity of the two transmission pathways were compared with respect to the epidemic growth rate. We numerically find that the direct transmission coefficient is more sensitive than the indirect transmission coefficient with respect to [Formula: see text] and the epidemic growth rate. Local stability of endemic equilibrium is studied. Further, the global asymptotic stability of the endemic equilibrium is proved using Li and Muldowney geometric approach. The explicit condition for which the system undergoes backward bifurcation is obtained. The basic model also exhibits the hysteresis phenomenon which implies diseases will persist even when [Formula: see text] although the system undergoes a forward bifurcation and this phenomenon is rarely observed in disease models. Consequently, our analysis suggests that the diseases with multiple transmission routes exhibit bistable dynamics. However, efficient application of temporary control in bistable regions will curb the disease to lower endemicity. Additionally, numerical simulations reveal that the equilibrium level of infected hosts decreases as psychological effect increases.

Event De-Noising Convolutional Neural Network for Detecting Malicious URL Sequences from Proxy Logs

The number of infected hosts on enterprise networks has been increased by drive-by download attacks. In these attacks, users of compromised popular websites are redirected toward websites that exploit vulnerabilities of a browser and its plugins. To prevent damage, detection of infected hosts on the basis of proxy logs rather than blacklist-based filtering has started to be researched. This is because blacklists have become difficult to create due to the short lifetime of malicious domains and concealment of exploit code. To detect accesses to malicious websites from proxy logs, we propose a system for detecting malicious URL sequences on the basis of three key ideas: focusing on sequences of URLs that include artifacts of malicious redirections, designing new features related to software other than browsers, and generating new training data with data augmentation. To find an effective approach for classifying URL sequences, we compared three approaches: an individual-based approach, a convolutional neural network (CNN), and our new event de-noising CNN (EDCNN). Our EDCNN reduces the negative effects of benign URLs redirected from compromised websites included in malicious URL sequences. Evaluation results show that only our EDCNN with proposed features and data augmentation achieved a practical classification performance: a true positive rate of 99.1%, and a false positive rate of 3.4%.

A stochastic worm model

Internet worm infection continues to be one of top security threats and has been widely used by botnets to recruit newbots. In order to defend against future worms, it is important to understand how worms propagate and how different scanning strategies affect worm propagation dynamics. In our study, we present a (stochastic) continuous-time Markov chain model for characterizing the propagation of Internet worms. The model is developed for uniform scanning worms, and further for local preference scanning worms and flash worms. Specifically, for uniform and local preference scanning worms, we are able to (1) provide a precise condition that determines whether the worm spread would eventually stop and (2) obtain the distribution of the total number of infected hosts. By using the same modeling approach, we reveal the underlying similarity and relationship between uniform scanning and local preference scanning worms. Finally, we validate the model by simulating the propagation of worms.

Modeling and analysis of a temperature-driven outbreak of waterfowl disease in the Upper Mississippi River

Bithynia tentaculata is an invasive snail that was discovered in the Upper Mississippi River (UMR) in 2002. In addition to being a threat to native benthos, the snail also harbors parasite associated with annual outbreaks of waterfowl mortality in the UMR. Trophic transmission of parasites between snails and birds occurs during seasonal waterfowl migrations, which can depend intimately on temperature. We developed an annual model for waterfowl disease in the UMR where transmission depends on water temperatures gleaned from empirical studies. By running simulations from annual temperature profiles selected randomly from a normal distribution, we quantified the association between the number of infected hosts and annual average temperatures. Model output demonstrated that as annual average temperatures rise, infected host populations initially increase and then decay after temperatures exceed a certain threshold. Results from this work suggest that increasing temperatures in the region may have a negative effect on parasites, decreasing their transmission and reducing infected host populations.

Feeding ecology informs parasite epidemiology: prey selection modulates encounter rate with Echinococcus multilocularis in urban coyotes.

We investigated the role of urban coyote feeding ecology in the transmission of Echinococcus multilocularis, the causative agent of Alveolar Echinococcosis in humans. As coyotes can play a main role in the maintenance of this zoonotic parasite within North American urban settings, such study can ultimately aid disease risk management. Between June 2012 and June 2013, we collected 251 coyote feces and conducted trapping of small mammals (n = 971) in five parks in the city of Calgary, Alberta, Canada. We investigated E. multilocularis epidemiology by assessing seasonal variations of coyote diet and the selective consumption of different rodent intermediate host species. Furthermore, accounting for small mammal digestibility and coyote defecation rates we estimated the number of small mammal preys ingested by coyote and consequently, coyote encounter rates with the parasite. Dominant food items included small mammals, fruit and vegetation, although hare and deer were seasonally relevant. The lowest frequency of occurrence per scat of small mammals was recorded in winter (39.4 %), when consumption of deer was highest (36.4 %). However, highest encounter rates (number of infected hosts predated/season) with E. multilocularis (95% CI: 1.0 - 22.4), combined with the lack of predation on non-competent small mammal species, suggest that winter is the critical season for transmission and control of this parasite. Within the small mammal assemblage, voles (Microtus pennsylvanicus and Myodes gapperi) were the selected preys of urban coyotes and likely played a key role for the maintenance of the urban sylvatic life-cycle of E. multilocularis in Calgary.

Data leakage triage: quantitative measure of effectiveness of containment plan

Increasingly organisations' deployed security defences are challenged by persistent malware attacks. There are many risks induced by successful malicious assaults against an organisation. One of the significant risks is data leakage. This risk has significant impacts to organisations and nations. To mitigate the likely occurrence of such risk, adequate preparation and response are needed. Epidemiological modelling of such malware outbreaks can aid in the development of mitigation plans. Most of these models used the number of infected hosts or time duration to analyse malware behaviours or the effectiveness of response plan. This paper proposes an extension of the agent-based malware containment model AMCM to provide a quantitative measurement of the amount of data leakage from malware infected hosts in order to evaluate the effectiveness of the response containment plan. It is believed that this form of measurement will enhance the preparation of incident response plans to deal with malware outbreak.

An Agent-Based Model for Studying the Impact of Herd Mobility on the Spread of Vector-Borne Diseases: The Case of Rift Valley Fever (Ferlo Senegal)

Vector-borne diseases are highly sensitive to environment and to environmental changes. Rift Valley Fever (RFV) is a mosquito-borne zootic virus associated with severe diseases in human beings and economic consequences to livestock sector. Animal and human movements have a fundamental impact on RVF transmission. In recent years, there has been a growing interest in the use of mathematics and agent based models to represent and analyze the dynamic of RFV transmission. However, no previous study has taken into consideration animal herds’ mobility and precipitation factors to understand the disease spread. This limitation underlines the necessity to use computational model approach based on multi-agent system in the study of vector-borne diseases transmission and diffusion. In this paper, a multi-agent system combining conceptual model expressiveness is used to study animal herds’ mobility and the precipitation parameter impact on the Rift Valley Fever outbreak in Ferlo Barkedji in Northern Senegal. Simulation scenarios with various parameters, including rain quality, hosts, vectors, camp dispersal around ponds, etc., are unrolled. The different results we have obtained show that the evolution of the number of infected hosts and infected vectors depend on the degree of animal herds’ mobility and on precipitations. Our model provides a framework that permits predicting the spread of the disease associated with the mobility of animal herds.

Predicting the Propagation Path of Random Worm by Subnet Infection Situation Using Fuzzy Reasoning

Predicting the propagation path of a network worm is highly beneficial for taking appropriate countermeasures in advance. Traditional worm propagation models mainly deal with the total number of infected hosts during a period of time, which cannot indicate a worm's track. We choose the worm using random scanning to study, as it was the basic type and all the others were derived from it. A novel model proposed in this paper locates the subnets going to be infected at a given time based on the infection measurement of the subnet. The time and frequency for victims in the subnet to increase were calculated according to common characteristics of worm diffusion and the relationship between malicious traffic and bandwidth usage. Taking the two factors above as input, fuzzy reasoning was adopted to deduce the real-time infection situation for each subnet. The bigger the value of infection situation, the more likely the corresponding subnet would be attacked in a short time. Simulation experimental results show that the model estimates the worm's track dynamically with acceptable accuracy. Furthermore, the increase interval of victims in subnet is much longer for worm with slower spread speed, which provides sufficient time to carry out pertinent response.

The prevalence and fertility of hydatid cysts in buffaloes from Iran

Cystic echinococcosis caused by Echinococcus granulosus is considered to be an important parasitic infection in livestock. In the present study, which aimed to determine the epidemiology of hydatidosis in buffalo in Iran, slaughterhouses of West Azerbaijan (Urmia), East Azerbaijan (Tabriz), Ardabil (Ardabil), Gilan (Rasht and Hashtpar) and Khuzestan (Ahvaz) were inspected. Age, sex and infected organs were recorded separately, and the observed cysts were examined for fertility and viability. Our results showed that 344 (9%) of 3832 inspected buffaloes were infected with hydatid cysts. The maximum and minimum infection rates occurred in Khuzestan (9.9%) and Ardabil (8%) provinces, respectively. There was no significant difference in the rate of infection in all provinces. Of 344 infected buffaloes, the rate of fertility was 7.3% and the rate of viability in fertile cysts was 78.75%. Hydatid cysts were more prevalent in female compared with male buffaloes (P < 0.05). There was a positive correlation between the age and number of infected hosts in all provinces except East Azerbaijan. The prevalence of infection in lungs was significantly higher than that in the livers of buffaloes in the provinces studied (P < 0.001). In conclusion, the fertility of hydatid cysts in buffaloes was low, as previously demonstrated in cattle, and this animal may play a minor role in the epidemiology of hydatidosis in Iran.

Multi-stage change-point detection scheme for large-scale simultaneous events

Change-point detection schemes, which represent one type of anomaly detection schemes, are a promising approach for detecting network anomalies, such as attacks and epidemics by unknown viruses and worms. These events are detected as change-points. However, the schemes generally also detect false-positive change-points caused by other events, such as improper parameter setting of detectors. Therefore there is a requirement for a scheme that detects only true-positive change-points caused by attacks and epidemics by unknown viruses and worms. The true-positive change-points tend to occur simultaneously and intensively in very large numbers, while the false-positive change-points tend to occur independently. Therefore, we expect that the multi-stage change-point detection scheme, which performs change-point detection in a distributed manner and takes account of the correlation among multiple change-points, can exclude false-positive change-points by neglecting those that occur independently. In this paper, we propose the multi-stage change-point detection scheme and introduce a weighting function that gives smaller weight to LDs with higher false-positive rate inferred by GD in order to avoid a set of false-positive alerts generated by the low-accuracy detectors from causing high false-positive rate of the scheme. We evaluate the performance of the scheme by a simulation using the parameter values obtained in an experiment using real random scan worms. In the evaluation, we modify AAWP (Analytical Active Worm Propagation) model so that it can derive the number of infected hosts (i.e., attack hosts) more accurately by considering a failure of infection behavior by random scan worms. The simulation results show that our scheme can achieve an optimal performance (detection rate of 1.0 and false-positive rate of 0) while the stand-alone change-point detection scheme, which does not use the correlation among multiple change-points, cannot attain such optimal performance, and our scheme with alert weighting always shows better detection performance than the scheme without alert weighting.

Stability Analysis of P2P Worm Propagation Model with Dynamic Quarantine Defense

The propagation of P2P worm covers large area and causes great damage. A dynamic quarantine protocol is designed to quarantine the suspicious host in the P2P system. Then a mathematical model of PWPQ is proposed which considering the dynamic process of peer joining and leaving. The effect of dynamic quarantine on active P2P worm is analyzed. Through stability analysis for PWPQ model, a key argument of infection-free stable point which affect the P2P worm propagation, the basic reproduction number, is deduced. Simulation results show that the propagation model of P2P worms can reflect the P2P worm behaviors and the performance of our model is significantly better than other models, in terms of decreasing the number of infected hosts and reducing the worm propagation speed. When the basic reproduction number is less than 1, infection-free stable point is global stability and the P2P worm are eliminated. The PWPQ model gives some way to control P2P worm break out and gives guidelines to P2P worm detection and defense.

Lightweight testbed for evaluating worm containment systems

Hazardous worms can compromise hundreds of thousands of hosts in just hours. Mitigating these worm threats requires fast and effective strategies for containment and is a difficult task. Many containment systems have been proposed including anomaly detection, address blacklisting and signature-based content filtering. Meanwhile recently developed worm models enable us to develop a testbed to quickly evaluate the efficiency of defense mechanisms. Existing testbeds either require a great deal of hardware resources, or do not account for network performance impact due to containment methods. In this paper, we present a testbed which utilizes software agents to allow large scale simulation while maintaining individual host functionality. Varying containment schemes and strategies have been evaluated using this testbed in terms of number of infected hosts and performance impacts. Our results indicate that a dynamic containment system achieves better performance and security. We believe our testbed is an effective tool to explore and evaluate varying worm containment systems.

An epidemiological model of host–parasite coevolution and sex

The Red Queen hypothesis posits a promising way to explain the widespread existence of sexual reproduction despite the cost of producing males. The essence of the hypothesis is that coevolutionary interactions between hosts and parasites select for the genetic diversification of offspring via cross-fertilization. Here, I relax a common assumption of many Red Queen models that each host is exposed to one parasite. Instead, I assume that the number of propagules encountered by each host depends on the number of infected hosts in the previous generation, which leads to additional complexities. The results suggest that epidemiological feedbacks, combined with frequency-dependent selection, could lead to the long-term persistence of sex under biologically reasonable conditions.