title: Backbone reconstruction in temporal networks from epidemic data

text: Many complex systems are characterized by time-varying patterns of interactions. These interactions comprise strong ties, driven by dyadic relationships, and weak ties, based on node-specific attributes. The interplay between strong and weak ties plays an important role on dynamical processes that could unfold on complex systems. However, seldom do we have access to precise information about the time-varying topology of interaction patterns. A particularly elusive question is to distinguish strong from weak ties, on the basis of the sole node dynamics. Building upon analytical results, we propose a statistically-principled algorithm to reconstruct the backbone of strong ties from data of a spreading process, consisting of the time series of individuals' states. Our method is numerically validated over a range of synthetic datasets, encapsulating salient features of real-world systems. Motivated by compelling evidence, we propose the integration of our algorithm in a targeted immunization strategy that prioritizes influential nodes in the inferred backbone. Through Monte Carlo simulations on synthetic networks and a real-world case study, we demonstrate the viability of our approach.


title: Epidemic spreading with awareness and different timescales in multiplex networks

text: One of the major issues in theoretical modeling of epidemic spreading is the development of methods to control the transmission of an infectious agent. Human behavior plays a fundamental role in the spreading dynamics and can be used to stop a disease from spreading or to reduce its burden, as individuals aware of the presence of a disease can take measures to reduce their exposure to contagion. In this paper, we propose a mathematical model for the spread of diseases with awareness in complex networks. Unlike previous models, the information is propagated following a generalized Maki-Thompson rumor model. Flexibility on the timescale between information and disease spreading is also included. We verify that the velocity characterizing the diffusion of information awareness greatly influences the disease prevalence. We also show that a reduction in the fraction of unaware individuals does not always imply a decrease of the prevalence, as the relative timescale between disease and awareness spreading plays a crucial role in the systems' dynamics. This result is shown to be independent of the network topology. We finally calculate the epidemic threshold of our model, and show that it does not depend on the relative timescale. Our results provide a new view on how information influence disease spreading and can be used for the development of more efficient methods for disease control.


title: Limitations of discrete-time approaches to continuous-time contagion dynamics

text: Continuous-time Markov process models of contagions are widely studied, not least because of their utility in predicting the evolution of real-world contagions and in formulating control measures. It is often the case, however, that discrete-time approaches are employed to analyze such models or to simulate them numerically. In such cases, time is discretized into uniform steps and transition rates between states are replaced by transition probabilities. In this paper, we illustrate potential limitations to this approach. We show how discretizing time leads to a restriction on the values of the model parameters that can accurately be studied. We examine numerical simulation schemes employed in the literature, showing how synchronous-type updating schemes can bias discrete-time formalisms when compared against continuous-time formalisms. Event-based simulations, such as the Gillespie algorithm, are proposed as optimal simulation schemes both in terms of replicating the continuous-time process and computational speed. Finally, we show how discretizing time can affect the value of the epidemic threshold for large values of the infection rate and the recovery rate, even if the ratio between the former and the latter is small.


title: Equivalence of several generalized percolation models on networks

text: In recent years, many variants of percolation have been used to study network structure and the behavior of processes spreading on networks. These include bond percolation, site percolation, [Formula: see text]-core percolation, bootstrap percolation, the generalized epidemic process, and the Watts threshold model (WTM). We show that—except for bond percolation—each of these processes arises as a special case of the WTM, and bond percolation arises from a small modification. In fact “heterogeneous [Formula: see text]-core percolation,” a corresponding “heterogeneous bootstrap percolation” model, and the generalized epidemic process are completely equivalent to one another and the WTM. We further show that a natural generalization of the WTM in which individuals “transmit” or “send a message” to their neighbors with some probability less than 1 can be reformulated in terms of the WTM, and so this apparent generalization is in fact not more general. Finally, we show that in bond percolation, finding the set of nodes in the component containing a given node is equivalent to finding the set of nodes activated if that node is initially activated and the node thresholds are chosen from the appropriate distribution. A consequence of these results is that mathematical techniques developed for the WTM apply to these other models as well, and techniques that were developed for some particular case may in fact apply much more generally.


title: Hysteresis loop of nonperiodic outbreaks of recurrent epidemics

text: Most of the studies on epidemics so far have focused on the growing phase, such as how an epidemic spreads and what are the conditions for an epidemic to break out in a variety of cases. However, we discover from real data that on a large scale, the spread of an epidemic is in fact a recurrent event with distinctive growing and recovering phases, i.e., a hysteresis loop. We show here that the hysteresis loop can be reproduced in epidemic models provided that the infectious rate is adiabatically increased or decreased before the system reaches its stationary state. Two ways to the hysteresis loop are revealed, which is helpful in understanding the mechanics of infections in real evolution. Moreover, a theoretical analysis is presented to explain the mechanism of the hysteresis loop.


title: Outbreaks in susceptible-infected-removed epidemics with multiple seeds

text: We study a susceptible-infected-removed (SIR) model with multiple seeds on a regular random graph. Many researchers have studied the epidemic threshold of epidemic models above which a global outbreak can occur, starting from an infinitesimal fraction of seeds. However, there have been few studies of epidemic models with finite fractions of seeds. The aim of this paper is to clarify what happens in phase transitions in such cases. The SIR model in networks exhibits two percolation transitions. We derive the percolation transition points for the SIR model with multiple seeds to show that as the infection rate increases epidemic clusters generated from each seed percolate before a single seed can induce a global outbreak.


title: Discrete-time dynamic network model for the spread of susceptible-infective-recovered diseases

text: We propose a discrete-time dynamic network model describing the spread of susceptible-infective-recovered diseases in a population. We consider the case in which the nodes in the network change their links due to social mixing dynamics as well as in response to the disease. The model shows the behavior that, as we increase social mixing, disease spread is inhibited in certain cases, while in other cases it is enhanced. We also extend this dynamic network model to take into account the case of hidden infection. Here we find that, as expected, the disease spreads more readily if there is a time period after contracting the disease during which an individual is infective but is not known to have the disease.


title: Epidemic spreading in random rectangular networks

text: The use of network theory to model disease propagation on populations introduces important elements of reality to the classical epidemiological models. The use of random geometric graphs (RGGs) is one of such network models that allows for the consideration of spatial properties on disease propagation. In certain real-world scenarios—like in the analysis of a disease propagating through plants—the shape of the plots and fields where the host of the disease is located may play a fundamental role in the propagation dynamics. Here we consider a generalization of the RGG to account for the variation of the shape of the plots or fields where the hosts of a disease are allocated. We consider a disease propagation taking place on the nodes of a random rectangular graph and we consider a lower bound for the epidemic threshold of a susceptible-infected-susceptible model or a susceptible-infected-recovered model on these networks. Using extensive numerical simulations and based on our analytical results we conclude that (ceteris paribus) the elongation of the plot or field in which the nodes are distributed makes the network more resilient to the propagation of a disease due to the fact that the epidemic threshold increases with the elongation of the rectangle. These results agree with accumulated empirical evidence and simulation results about the propagation of diseases on plants in plots or fields of the same area and different shapes.


title: Influence of trust in the spreading of information

text: The understanding and prediction of information diffusion processes on networks is a major challenge in network theory with many implications in social sciences. Many theoretical advances occurred due to stochastic spreading models. Nevertheless, these stochastic models overlooked the influence of rational decisions on the outcome of the process. For instance, different levels of trust in acquaintances do play a role in information spreading, and actors may change their spreading decisions during the information diffusion process accordingly. Here, we study an information-spreading model in which the decision to transmit or not is based on trust. We explore the interplay between the propagation of information and the trust dynamics happening on a two-layer multiplex network. Actors' trustable or untrustable states are defined as accumulated cooperation or defection behaviors, respectively, in a Prisoner's Dilemma setup, and they are controlled by a memory span. The propagation of information is abstracted as a threshold model on the information-spreading layer, where the threshold depends on the trustability of agents. The analysis of the model is performed using a tree approximation and validated on homogeneous and heterogeneous networks. The results show that the memory of previous actions has a significant effect on the spreading of information. For example, the less memory that is considered, the higher is the diffusion. Information is highly promoted by the emergence of trustable acquaintances. These results provide insight into the effect of plausible biases on spreading dynamics in a multilevel networked system.


title: Memory effects on epidemic evolution: The susceptible-infected-recovered epidemic model

text: Memory has a great impact on the evolution of every process related to human societies. Among them, the evolution of an epidemic is directly related to the individuals' experiences. Indeed, any real epidemic process is clearly sustained by a non-Markovian dynamics: memory effects play an essential role in the spreading of diseases. Including memory effects in the susceptible-infected-recovered (SIR) epidemic model seems very appropriate for such an investigation. Thus, the memory prone SIR model dynamics is investigated using fractional derivatives. The decay of long-range memory, taken as a power-law function, is directly controlled by the order of the fractional derivatives in the corresponding nonlinear fractional differential evolution equations. Here we assume “fully mixed” approximation and show that the epidemic threshold is shifted to higher values than those for the memoryless system, depending on this memory “length” decay exponent. We also consider the SIR model on structured networks and study the effect of topology on threshold points in a non-Markovian dynamics. Furthermore, the lack of access to the precise information about the initial conditions or the past events plays a very relevant role in the correct estimation or prediction of the epidemic evolution. Such a “constraint” is analyzed and discussed.


title: Sampling methods for the quasistationary regime of epidemic processes on regular and complex networks

text: A major hurdle in the simulation of the steady state of epidemic processes is that the system will unavoidably visit an absorbing, disease-free state at sufficiently long times due to the finite size of the networks where epidemics evolves. In the present work, we compare different quasistationary (QS) simulation methods where the absorbing states are suitably handled and the thermodynamical limit of the original dynamics can be achieved. We analyze the standard QS (SQS) method, where the sampling is constrained to active configurations, the reflecting boundary condition (RBC), where the dynamics returns to the pre-absorbing configuration, and hub reactivation (HR), where the most connected vertex of the network is reactivated after a visit to an absorbing state. We apply the methods to the contact process (CP) and susceptible-infected-susceptible (SIS) models on regular and scale free networks. The investigated methods yield the same epidemic threshold for both models. For CP, that undergoes a standard collective phase transition, the methods are equivalent. For SIS, whose phase transition is ruled by the hub mutual reactivation, the SQS and HR methods are able to capture localized epidemic phases while RBC is not. We also apply the autocorrelation time as a tool to characterize the phase transition and observe that this analysis provides the same finite-size scaling exponents for the critical relaxation time for the investigated methods. Finally, we verify the equivalence between RBC method and a weak external field for epidemics on networks.


title: Impact of environmental colored noise in single-species population dynamics

text: Variability on external conditions has important consequences for the dynamics and the organization of biological systems. In many cases, the characteristic timescale of environmental changes as well as their correlations play a fundamental role in the way living systems adapt and respond to it. A proper mathematical approach to understand population dynamics, thus, requires approaches more refined than, e.g., simple white-noise approximations. To shed further light onto this problem, in this paper we propose a unifying framework based on different analytical and numerical tools available to deal with “colored” environmental noise. In particular, we employ a “unified colored noise approximation” to map the original problem into an effective one with white noise, and then we apply a standard path integral approach to gain analytical understanding. For the sake of specificity, we present our approach using as a guideline a variation of the contact process—which can also be seen as a birth-death process of the Malthus-Verhulst class—where the propagation or birth rate varies stochastically in time. Our approach allows us to tackle in a systematic manner some of the relevant questions concerning population dynamics under environmental variability, such as determining the stationary population density, establishing the conditions under which a population may become extinct, and estimating extinction times. We focus on the emerging phase diagram and its possible phase transitions, underlying how these are affected by the presence of environmental noise time-correlations.


title: Clustering determines the dynamics of complex contagions in multiplex networks

text: We present the mathematical analysis of generalized complex contagions in a class of clustered multiplex networks. The model is intended to understand spread of influence, or any other spreading process implying a threshold dynamics, in setups of interconnected networks with significant clustering. The contagion is assumed to be general enough to account for a content-dependent linear threshold model, where each link type has a different weight (for spreading influence) that may depend on the content (e.g., product, rumor, political view) that is being spread. Using the generating functions formalism, we determine the conditions, probability, and expected size of the emergent global cascades. This analysis provides a generalization of previous approaches and is especially useful in problems related to spreading and percolation. The results present nontrivial dependencies between the clustering coefficient of the networks and its average degree. In particular, several phase transitions are shown to occur depending on these descriptors. Generally speaking, our findings reveal that increasing clustering decreases the probability of having global cascades and their size, however, this tendency changes with the average degree. There exists a certain average degree from which on clustering favors the probability and size of the contagion. By comparing the dynamics of complex contagions over multiplex networks and their monoplex projections, we demonstrate that ignoring link types and aggregating network layers may lead to inaccurate conclusions about contagion dynamics, particularly when the correlation of degrees between layers is high.


title: Mutually cooperative epidemics on power-law networks

text: The spread of an infectious disease can, in some cases, promote the propagation of other pathogens favoring violent outbreaks, which cause a discontinuous transition to an endemic state. The topology of the contact network plays a crucial role in these cooperative dynamics. We consider a susceptible-infected-removed-type model with two mutually cooperative pathogens: An individual already infected with one disease has an increased probability of getting infected by the other. We present a heterogeneous mean-field theoretical approach to the coinfection dynamics on generic uncorrelated power-law degree-distributed networks and validate its results by means of numerical simulations. We show that, when the second moment of the degree distribution is finite, the epidemic transition is continuous for low cooperativity, while it is discontinuous when cooperativity is sufficiently high. For scale-free networks, i.e., topologies with diverging second moment, the transition is instead always continuous. In this way we clarify the effect of heterogeneity and system size on the nature of the transition, and we validate the physical interpretation about the origin of the discontinuity.


title: Efficiency of prompt quarantine measures on a susceptible-infected-removed model in networks

text: This study focuses on investigating the manner in which a prompt quarantine measure suppresses epidemics in networks. A simple and ideal quarantine measure is considered in which an individual is detected with a probability immediately after it becomes infected and the detected one and its neighbors are promptly isolated. The efficiency of this quarantine in suppressing a susceptible-infected-removed (SIR) model is tested in random graphs and uncorrelated scale-free networks. Monte Carlo simulations are used to show that the prompt quarantine measure outperforms random and acquaintance preventive vaccination schemes in terms of reducing the number of infected individuals. The epidemic threshold for the SIR model is analytically derived under the quarantine measure, and the theoretical findings indicate that prompt executions of quarantines are highly effective in containing epidemics. Even if infected individuals are detected with a very low probability, the SIR model under a prompt quarantine measure has finite epidemic thresholds in fat-tailed scale-free networks in which an infected individual can always cause an outbreak of a finite relative size without any measure. The numerical simulations also demonstrate that the present quarantine measure is effective in suppressing epidemics in real networks.


title: Control of epidemics via social partnership adjustment

text: Epidemic control is of great importance for human society. Adjusting interacting partners is an effective individualized control strategy. Intuitively, it is done either by shortening the interaction time between susceptible and infected individuals or by increasing the opportunities for contact between susceptible individuals. Here, we provide a comparative study on these two control strategies by establishing an epidemic model with nonuniform stochastic interactions. It seems that the two strategies should be similar, since shortening the interaction time between susceptible and infected individuals somehow increases the chances for contact between susceptible individuals. However, analytical results indicate that the effectiveness of the former strategy sensitively depends on the infectious intensity and the combinations of different interaction rates, whereas the latter one is quite robust and efficient. Simulations are shown to verify our analytical predictions. Our work may shed light on the strategic choice of disease control.


title: Takeover times for a simple model of network infection

text: We study a stochastic model of infection spreading on a network. At each time step a node is chosen at random, along with one of its neighbors. If the node is infected and the neighbor is susceptible, the neighbor becomes infected. How many time steps [Formula: see text] does it take to completely infect a network of [Formula: see text] nodes, starting from a single infected node? An analogy to the classic “coupon collector” problem of probability theory reveals that the takeover time [Formula: see text] is dominated by extremal behavior, either when there are only a few infected nodes near the start of the process or a few susceptible nodes near the end. We show that for [Formula: see text] , the takeover time [Formula: see text] is distributed as a Gumbel distribution for the star graph, as the convolution of two Gumbel distributions for a complete graph and an Erdős-Rényi random graph, as a normal for a one-dimensional ring and a two-dimensional lattice, and as a family of intermediate skewed distributions for [Formula: see text]-dimensional lattices with [Formula: see text] (these distributions approach the convolution of two Gumbel distributions as [Formula: see text] approaches infinity). Connections to evolutionary dynamics, cancer, incubation periods of infectious diseases, first-passage percolation, and other spreading phenomena in biology and physics are discussed.


title: Three faces of node importance in network epidemiology: Exact results for small graphs

text: We investigate three aspects of the importance of nodes with respect to susceptible-infectious-removed (SIR) disease dynamics: influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size), and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes). We calculate the exact expressions of these quantities, as functions of the SIR parameters, for all connected graphs of three to seven nodes. We obtain the smallest graphs where the optimal node sets are not overlapping. We find that (i) node separation is more important than centrality for more than one active node, (ii) vaccination and influence maximization are the most different aspects of importance, and (iii) the three aspects are more similar when the infection rate is low.


title: Griffiths phase on hierarchical modular networks with small-world edges

text: The Griffiths phase has been proposed to induce a stretched critical regime that facilitates self-organizing of brain networks for optimal function. This phase stems from the intrinsic structural heterogeneity of brain networks, i.e., the hierarchical modular structure. In this work, the Griffiths phase is studied in modified hierarchical networks with small-world connections based on the 3-regular Hanoi network. Through extensive simulations, the hierarchical level-dependent inter-module wiring probabilities are identified to determine the emergence of the Griffiths phase. Numerical results and the complementary spectral analysis of the relevant networks can be helpful for a deeper understanding of the essential structural characteristics of finite-dimensional networks to support the Griffiths phase.


title: Effect of risk perception on epidemic spreading in temporal networks

text: Many progresses in the understanding of epidemic spreading models have been obtained thanks to numerous modeling efforts and analytical and numerical studies, considering host populations with very different structures and properties, including complex and temporal interaction networks. Moreover, a number of recent studies have started to go beyond the assumption of an absence of coupling between the spread of a disease and the structure of the contacts on which it unfolds. Models including awareness of the spread have been proposed, to mimic possible precautionary measures taken by individuals that decrease their risk of infection, but have mostly considered static networks. Here, we adapt such a framework to the more realistic case of temporal networks of interactions between individuals. We study the resulting model by analytical and numerical means on both simple models of temporal networks and empirical time-resolved contact data. Analytical results show that the epidemic threshold is not affected by the awareness but that the prevalence can be significantly decreased. Numerical studies on synthetic temporal networks highlight, however, the presence of very strong finite-size effects, resulting in a significant shift of the effective epidemic threshold in the presence of risk awareness. For empirical contact networks, the awareness mechanism leads as well to a shift in the effective threshold and to a strong reduction of the epidemic prevalence.


title: Accurate ranking of influential spreaders in networks based on dynamically asymmetric link weights

text: We propose an efficient and accurate measure for ranking spreaders and identifying the influential ones in spreading processes in networks. While the edges determine the connections among the nodes, their specific role in spreading should be considered explicitly. An edge connecting nodes [Formula: see text] and [Formula: see text] may differ in its importance for spreading from [Formula: see text] to [Formula: see text] and from [Formula: see text] to [Formula: see text]. The key issue is whether node [Formula: see text] , after infected by [Formula: see text] through the edge, would reach out to other nodes that [Formula: see text] itself could not reach directly. It becomes necessary to invoke two unequal weights [Formula: see text] and [Formula: see text] characterizing the importance of an edge according to the neighborhoods of nodes [Formula: see text] and [Formula: see text]. The total asymmetric directional weights originating from a node leads to a novel measure [Formula: see text] , which quantifies the impact of the node in spreading processes. An [Formula: see text]-shell decomposition scheme further assigns an [Formula: see text]-shell index or weighted coreness to the nodes. The effectiveness and accuracy of rankings based on [Formula: see text] and the weighted coreness are demonstrated by applying them to nine real-world networks. Results show that they generally outperform rankings based on the nodes' degree and [Formula: see text]-shell index while maintaining a low computational complexity. Our work represents a crucial step towards understanding and controlling the spread of diseases, rumors, information, trends, and innovations in networks.


title: Fundamental difference between superblockers and superspreaders in networks

text: Two important problems regarding spreading phenomena in complex topologies are the optimal selection of node sets either to minimize or maximize the extent of outbreaks. Both problems are nontrivial when a small fraction of the nodes in the network can be used to achieve the desired goal. The minimization problem is equivalent to a structural optimization. The “superblockers,” i.e., the nodes that should be removed from the network to minimize the size of outbreaks, are those nodes that make connected components as small as possible. “Superspreaders” are instead the nodes such that, if chosen as initiators, they maximize the average size of outbreaks. The identity of superspreaders is expected to depend not just on the topology, but also on the specific dynamics considered. Recently, it has been conjectured that the two optimization problems might be equivalent, in the sense that superblockers act also as superspreaders. In spite of its potential groundbreaking importance, no empirical study has been performed to validate this conjecture. In this paper, we perform an extensive analysis over a large set of real-world networks to test the similarity between sets of superblockers and of superspreaders. We show that the two optimization problems are not equivalent: superblockers do not act as optimal spreaders.


title: Stochastic dynamics for reinfection by transmitted diseases

text: The use of stochastic models to study the dynamics of infectious diseases is an important tool to understand the epidemiological process. For several directly transmitted diseases, reinfection is a relevant process, which can be expressed by endogenous reactivation of the pathogen or by exogenous reinfection due to direct contact with an infected individual (with smaller reinfection rate [Formula: see text] than infection rate [Formula: see text]). In this paper, we examine the stochastic susceptible, infected, recovered, infected (SIRI) model simulating the endogenous reactivation by a spontaneous reaction, while exogenous reinfection by a catalytic reaction. Analyzing the mean-field approximations of a site and pairs of sites, and Monte Carlo (MC) simulations for the particular case of exogenous reinfection, we obtained continuous phase transitions involving endemic, epidemic, and no transmission phases for the simple approach; the approach of pairs is better to describe the phase transition from endemic phase (susceptible, infected, susceptible (SIS)-like model) to epidemic phase (susceptible, infected, and removed or recovered (SIR)-like model) considering the comparison with MC results; the reinfection increases the peaks of outbreaks until the system reaches endemic phase. For the particular case of endogenous reactivation, the approach of pairs leads to a continuous phase transition from endemic phase (SIS-like model) to no transmission phase. Finally, there is no phase transition when both effects are taken into account. We hope the results of this study can be generalized for the susceptible, exposed, infected, and removed or recovered ([Formula: see text]) model, for which the state exposed (infected but not infectious), describing more realistically transmitted diseases such as tuberculosis. In future work, we also intend to investigate the effect of network topology on phase transitions when the SIRI model describes both transmitted diseases ([Formula: see text]) and social contagions ([Formula: see text]).


title: Critical behavior of a two-step contagion model with multiple seeds

text: A two-step contagion model with a single seed serves as a cornerstone for understanding the critical behaviors and underlying mechanism of discontinuous percolation transitions induced by cascade dynamics. When the contagion spreads from a single seed, a cluster of infected and recovered nodes grows without any cluster merging process. However, when the contagion starts from multiple seeds of [Formula: see text] where [Formula: see text] is the system size, a node weakened by a seed can be infected more easily when it is in contact with another node infected by a different pathogen seed. This contagion process can be viewed as a cluster merging process in a percolation model. Here we show analytically and numerically that when the density of infectious seeds is relatively small but [Formula: see text] , the epidemic transition is hybrid, exhibiting both continuous and discontinuous behavior, whereas when it is sufficiently large and reaches a critical point, the transition becomes continuous. We determine the full set of critical exponents describing the hybrid and the continuous transitions. Their critical behaviors differ from those in the single-seed case.


title: Epidemic spreading on activity-driven networks with attractiveness

text: We study SIS epidemic spreading processes unfolding on a recent generalization of the activity-driven modeling framework. In this model of time-varying networks, each node is described by two variables: activity and attractiveness. The first describes the propensity to form connections, while the second defines the propensity to attract them. We derive analytically the epidemic threshold considering the time scale driving the evolution of contacts and the contagion as comparable. The solutions are general and hold for any joint distribution of activity and attractiveness. The theoretical picture is confirmed via large-scale numerical simulations performed considering heterogeneous distributions and different correlations between the two variables. We find that heterogeneous distributions of attractiveness alter the contagion process. In particular, in the case of uncorrelated and positive correlations between the two variables, heterogeneous attractiveness facilitates the spreading. On the contrary, negative correlations between activity and attractiveness hamper the spreading. The results presented contribute to the understanding of the dynamical properties of time-varying networks and their effects on contagion phenomena unfolding on their fabric.


title: Tuning the overlap and the cross-layer correlations in two-layer networks: Application to a susceptible-infectious-recovered model with awareness dissemination

text: We study the properties of the potential overlap between two networks [Formula: see text] sharing the same set of [Formula: see text] nodes (a two-layer network) whose respective degree distributions [Formula: see text] are given. Defining the overlap coefficient [Formula: see text] as the Jaccard index, we prove that [Formula: see text] is very close to 0 when [Formula: see text] and [Formula: see text] are random and independently generated. We derive an upper bound [Formula: see text] for the maximum overlap coefficient permitted in terms of [Formula: see text] , and [Formula: see text]. Then we present an algorithm based on cross rewiring of links to obtain a two-layer network with any prescribed [Formula: see text] inside the range [Formula: see text]. A refined version of the algorithm allows us to minimize the cross-layer correlations that unavoidably appear for values of [Formula: see text] beyond a critical overlap [Formula: see text]. Finally, we present a very simple example of a susceptible-infectious-recovered epidemic model with information dissemination and use the algorithms to determine the impact of the overlap on the final outbreak size predicted by the model.


title: Interplay between cost and benefits triggers nontrivial vaccination uptake

text: The containment of epidemic spreading is a major challenge in science. Vaccination, whenever available, is the best way to prevent the spreading, because it eventually immunizes individuals. However, vaccines are not perfect, and total immunization is not guaranteed. Imperfect immunization has driven the emergence of antivaccine movements that totally alter the predictions about the epidemic incidence. Here, we propose a mathematically solvable mean-field vaccination model to mimic the spontaneous adoption of vaccines against influenzalike diseases and the expected epidemic incidence. The results are in agreement with extensive Monte Carlo simulations of the epidemics and vaccination coevolutionary processes. Interestingly, the results reveal a nonmonotonic behavior on the vaccination coverage that increases with the imperfection of the vaccine and after decreases. This apparent counterintuitive behavior is analyzed and understood from stability principles of the proposed mathematical model.


title: Infection dynamics on spatial small-world network models

text: The study of complex networks, and in particular of social networks, has mostly concentrated on relational networks, abstracting the distance between nodes. Spatial networks are, however, extremely relevant in our daily lives, and a large body of research exists to show that the distances between nodes greatly influence the cost and probability of establishing and maintaining a link. A random geometric graph (RGG) is the main type of synthetic network model used to mimic the statistical properties and behavior of many social networks. We propose a model, called REDS, that extends energy-constrained RGGs to account for the synergic effect of sharing the cost of a link with our neighbors, as is observed in real relational networks. We apply both the standard Watts-Strogatz rewiring procedure and another method that conserves the degree distribution of the network. The second technique was developed to eliminate unwanted forms of spatial correlation between the degree of nodes that are affected by rewiring, limiting the effect on other properties such as clustering and assortativity. We analyze both the statistical properties of these two network types and their epidemiological behavior when used as a substrate for a standard susceptible-infected-susceptible compartmental model. We consider and discuss the differences in properties and behavior between RGGs and REDS as rewiring increases and as infection parameters are changed. We report considerable differences both between the network types and, in the case of REDS, between the two rewiring schemes. We conclude that REDS represent, with the application of these rewiring mechanisms, extremely useful and interesting tools in the study of social and epidemiological phenomena in synthetic complex networks.


title: Rapid decay in the relative efficiency of quarantine to halt epidemics in networks

text: Several recent studies have tackled the issue of optimal network immunization by providing efficient criteria to identify key nodes to be removed in order to break apart a network, thus preventing the occurrence of extensive epidemic outbreaks. Yet, although the efficiency of those criteria has been demonstrated also in empirical networks, preventive immunization is rarely applied to real-world scenarios, where the usual approach is the a posteriori attempt to contain epidemic outbreaks using quarantine measures. Here we compare the efficiency of prevention with that of quarantine in terms of the tradeoff between the number of removed and saved nodes on both synthetic and empirical topologies. We show how, consistent with common sense, but contrary to common practice, in many cases preventing is better than curing: depending on network structure, rescuing an infected network by quarantine could become inefficient soon after the first infection.


title: Bursting endemic bubbles in an adaptive network

text: The spread of an infectious disease is known to change people's behavior, which in turn affects the spread of disease. Adaptive network models that account for both epidemic and behavioral change have found oscillations, but in an extremely narrow region of the parameter space, which contrasts with intuition and available data. In this paper we propose a simple susceptible-infected-susceptible epidemic model on an adaptive network with time-delayed rewiring, and show that oscillatory solutions are now present in a wide region of the parameter space. Altering the transmission or rewiring rates reveals the presence of an endemic bubble—an enclosed region of the parameter space where oscillations are observed.


title: Impact of the infectious period on epidemics

text: The duration of the infectious period is a crucial determinant of the ability of an infectious disease to spread. We consider an epidemic model that is network based and non-Markovian, containing classic Kermack-McKendrick, pairwise, message passing, and spatial models as special cases. For this model, we prove a monotonic relationship between the variability of the infectious period (with fixed mean) and the probability that the infection will reach any given subset of the population by any given time. For certain families of distributions, this result implies that epidemic severity is decreasing with respect to the variance of the infectious period. The striking importance of this relationship is demonstrated numerically. We then prove, with a fixed basic reproductive ratio ([Formula: see text]), a monotonic relationship between the variability of the posterior transmission probability (which is a function of the infectious period) and the probability that the infection will reach any given subset of the population by any given time. Thus again, even when [Formula: see text] is fixed, variability of the infectious period tends to dampen the epidemic. Numerical results illustrate this but indicate the relationship is weaker. We then show how our results apply to message passing, pairwise, and Kermack-McKendrick epidemic models, even when they are not exactly consistent with the stochastic dynamics. For Poissonian contact processes, and arbitrarily distributed infectious periods, we demonstrate how systems of delay differential equations and ordinary differential equations can provide upper and lower bounds, respectively, for the probability that any given individual has been infected by any given time.


title: Spontaneous repulsion in the [Formula: see text] reaction on coupled networks

text: We study the transient dynamics of an [Formula: see text] process on a pair of randomly coupled networks, where reactants are initially separated. We find that, for sufficiently small fractions [Formula: see text] of cross couplings, the concentration of [Formula: see text] (or [Formula: see text]) particles decays linearly in a first stage and crosses over to a second linear decrease at a mixing time [Formula: see text]. By numerical and analytical arguments, we show that for symmetric and homogeneous structures [Formula: see text] where [Formula: see text] is the mean degree of both networks. Being this behavior is in marked contrast with a purely diffusive process, where the mixing time would go simply like [Formula: see text] , we identify the logarithmic slowing down in [Formula: see text] to be the result of a spontaneous mechanism of repulsion between the reactants [Formula: see text] and [Formula: see text] due to the interactions taking place at the networks' interface. We show numerically how this spontaneous repulsion effect depends on the topology of the underlying networks.


title: Autocorrelation of the susceptible-infected-susceptible process on networks

text: In this paper, we focus on the autocorrelation of the susceptible-infected-susceptible (SIS) process on networks. The [Formula: see text]-intertwined mean-field approximation (NIMFA) is applied to calculate the autocorrelation properties of the exact SIS process. We derive the autocorrelation of the infection state of each node and the fraction of infected nodes both in the steady and transient states as functions of the infection probabilities of nodes. Moreover, we show that the autocorrelation can be used to estimate the infection and curing rates of the SIS process. The theoretical results are compared with the simulation of the exact SIS process. Our work fully utilizes the potential of the mean-field method and shows that NIMFA can indeed capture the autocorrelation properties of the exact SIS process.


title: Two golden times in two-step contagion models: A nonlinear map approach

text: The two-step contagion model is a simple toy model for understanding pandemic outbreaks that occur in the real world. The model takes into account that a susceptible person either gets immediately infected or weakened when getting into contact with an infectious one. As the number of weakened people increases, they eventually can become infected in a short time period and a pandemic outbreak occurs. The time required to reach such a pandemic outbreak allows for intervention and is often called golden time. Understanding the size-dependence of the golden time is useful for controlling pandemic outbreak. Using an approach based on a nonlinear mapping, here we find that there exist two types of golden times in the two-step contagion model, which scale as [Formula: see text] and [Formula: see text] with the system size [Formula: see text] on Erdős-Rényi networks, where the measured [Formula: see text] is slightly larger than [Formula: see text]. They are distinguished by the initial number of infected nodes, [Formula: see text] and [Formula: see text] , respectively. While the exponent [Formula: see text] of the [Formula: see text]-dependence of the golden time is universal even in other models showing discontinuous transitions induced by cascading dynamics, the measured [Formula: see text] exponents are all close to [Formula: see text] but show model-dependence. It remains open whether or not [Formula: see text] reduces to [Formula: see text] in the asymptotically large- [Formula: see text] limit. Our method can be applied to several models showing a hybrid percolation transition and gives insight into the origin of the two golden times.


title: Correlations between thresholds and degrees: An analytic approach to model attacks and failure cascades

text: Two node variables determine the evolution of cascades in random networks: a node's degree and threshold. Correlations between both fundamentally change the robustness of a network, yet they are disregarded in standard analytic methods as local tree or heterogeneous mean field approximations, since order statistics are difficult to capture analytically because of their combinatorial nature. We show how they become tractable in the thermodynamic limit of infinite network size. This enables the analytic description of node attacks that are characterized by threshold allocations based on node degree. Using two examples, we discuss possible implications of irregular phase transitions and different speeds of cascade evolution for the control of cascades.


docid: ymhngjrt

title: Optimization of targeted node set in complex networks under percolation and selection

text: Most of the existing methods for the robustness and targeted immunization problems can be viewed as greedy strategies, which are quite efficient but readily induce a local optimization. In this paper, starting from a percolation perspective, we develop two strategies, the relationship-related (RR) strategy and the prediction relationship (PR) strategy, to avoid a local optimum only through the investigation of interrelationships among nodes. Meanwhile, RR combines the sum rule and the product rule from explosive percolation, and PR holds the assumption that nodes with high degree are usually more important than those with low degree. In this manner our methods have a better capability to collapse or protect a network. The simulations performed on a number of networks also demonstrate their effectiveness, especially on large real-world networks where RR fragments each of them into the same size of the giant component; however, RR needs only less than [Formula: see text] of the number of nodes which are necessary for the most excellent existing methods.


title: Robustness and fragility of the susceptible-infected-susceptible epidemic models on complex networks

text: We analyze two alterations of the standard susceptible-infected-susceptible (SIS) dynamics that preserve the central properties of spontaneous healing and infection capacity of a vertex increasing unlimitedly with its degree. All models have the same epidemic thresholds in mean-field theories but depending on the network properties, simulations yield a dual scenario, in which the epidemic thresholds of the modified SIS models can be either dramatically altered or remain unchanged in comparison with the standard dynamics. For uncorrelated synthetic networks having a power-law degree distribution with exponent [Formula: see text] , the SIS dynamics are robust exhibiting essentially the same outcomes for all investigated models. A threshold in better agreement with the heterogeneous rather than quenched mean-field theory is observed in the modified dynamics for exponent [Formula: see text]. Differences are more remarkable for [Formula: see text] , where a finite threshold is found in the modified models in contrast with the vanishing threshold of the original one. This duality is elucidated in terms of epidemic lifespan on star graphs. We verify that the activation of the modified SIS models is triggered in the innermost component of the network given by a [Formula: see text]-core decomposition for [Formula: see text] while it happens only for [Formula: see text] in the standard model. For [Formula: see text] , the activation in the modified dynamics is collective involving essentially the whole network while it is triggered by hubs in the standard SIS. The duality also appears in the finite-size scaling of the critical quantities where mean-field behaviors are observed for the modified but not for the original dynamics. Our results feed the discussions about the most proper conceptions of epidemic models to describe real systems and the choices of the most suitable theoretical approaches to deal with these models.


title: Role of hubs in the synergistic spread of behavior

text: The spread of behavior in a society has two major features: the synergy of multiple spreaders and the dominance of hubs. While strong synergy is known to induce mixed-order transitions (MOTs) at percolation, the effects of hubs on the phenomena are yet to be clarified. By analytically solving the generalized epidemic process on random scale-free networks with the power-law degree distribution [Formula: see text] , we clarify how the dominance of hubs in social networks affects the conditions for MOTs. Our results show that, for [Formula: see text] , an abundance of hubs drive MOTs, even if a synergistic spreading event requires an arbitrarily large number of adjacent spreaders. In particular, for [Formula: see text] , we find that a global cascade is possible even when only synergistic spreading events are allowed. These transition properties are substantially different from those of cooperative contagions, which are another class of synergistic cascading processes exhibiting MOTs.


title: Emergence of power laws in noncritical neuronal systems

text: Experimental and computational studies provide compelling evidence that neuronal systems are characterized by power-law distributions of neuronal avalanche sizes. This fact is interpreted as an indication that these systems are operating near criticality, and, in turn, typical properties of critical dynamical processes, such as optimal information transmission and stability, are attributed to neuronal systems. The purpose of this Rapid Communication is to show that the presence of power-law distributions for the size of neuronal avalanches is not a sufficient condition for the system to operate near criticality. Specifically, we consider a simplistic model of neuronal dynamics on networks and show that the degree distribution of the underlying neuronal network may trigger power-law distributions for neuronal avalanches even when the system is not in its critical regime. To certify and explain our findings we develop an analytical approach based on percolation theory and branching processes techniques.


docid: qdvqgwyi

title: Morphological inversion of complex diffusion

text: Epidemics, neural cascades, power failures, and many other phenomena can be described by a diffusion process on a network. To identify the causal origins of a spread, it is often necessary to identify the triggering initial node. Here, we define a new morphological operator and use it to detect the origin of a diffusive front, given the final state of a complex network. Our method performs better than algorithms based on distance (closeness) and Jordan centrality. More importantly, our method is applicable regardless of the specifics of the forward model, and therefore can be applied to a wide range of systems such as identifying the patient zero in an epidemic, pinpointing the neuron that triggers a cascade, identifying the original malfunction that causes a catastrophic infrastructure failure, and inferring the ancestral species from which a heterogeneous population evolves.


title: Effective distances for epidemics spreading on complex networks

text: We show that the recently introduced logarithmic metrics used to predict disease arrival times on complex networks are approximations of more general network-based measures derived from random walks theory. Using the daily air-traffic transportation data we perform numerical experiments to compare the infection arrival time with this alternative metric that is obtained by accounting for multiple walks instead of only the most probable path. The comparison with direct simulations reveals a higher correlation compared to the shortest-path approach used previously. In addition our method allows to connect fundamental observables in epidemic spreading with the cumulant-generating function of the hitting time for a Markov chain. Our results provides a general and computationally efficient approach using only algebraic methods.


title: Objective measures for sentinel surveillance in network epidemiology

text: Assume one has the capability of determining whether a node in a network is infectious or not by probing it. Then problem of optimizing sentinel surveillance in networks is to identify the nodes to probe such that an emerging disease outbreak can be discovered early or reliably. Whether the emphasis should be on early or reliable detection depends on the scenario in question. We investigate three objective measures from the literature quantifying the performance of nodes in sentinel surveillance: the time to detection or extinction, the time to detection, and the frequency of detection. As a basis for the comparison, we use the susceptible-infectious-recovered model on static and temporal networks of human contacts. We show that, for some regions of parameter space, the three objective measures can rank the nodes very differently. This means sentinel surveillance is a class of problems, and solutions need to chose an objective measure for the particular scenario in question. As opposed to other problems in network epidemiology, we draw similar conclusions from the static and temporal networks. Furthermore, we do not find one type of network structure that predicts the objective measures, i.e., that depends both on the data set and the SIR parameter values.


title: Hipsters on networks: How a minority group of individuals can lead to an antiestablishment majority

text: The spread of opinions, memes, diseases, and “alternative facts” in a population depends both on the details of the spreading process and on the structure of the social and communication networks on which they spread. One feature that can change spreading dynamics substantially is heterogeneous behavior among different types of individuals in a social network. In this paper, we explore how antiestablishment nodes (e.g., hipsters) influence the spreading dynamics of two competing products. We consider a model in which spreading follows a deterministic rule for updating node states (which indicate which product has been adopted) in which an adjustable probability [Formula: see text] of the nodes in a network are hipsters, who choose to adopt the product that they believe is the less popular of the two. The remaining nodes are conformists, who choose which product to adopt by considering which products their immediate neighbors have adopted. We simulate our model on both synthetic and real networks, and we show that the hipsters have a major effect on the final fraction of people who adopt each product: even when only one of the two products exists at the beginning of the simulations, a small fraction of hipsters in a network can still cause the other product to eventually become the more popular one. To account for this behavior, we construct an approximation for the steady-state adoption fractions of the products on [Formula: see text]-regular trees in the limit of few hipsters. Additionally, our simulations demonstrate that a time delay [Formula: see text] in the knowledge of the product distribution in a population, as compared to immediate knowledge of product adoption among nearest neighbors, can have a large effect on the final distribution of product adoptions. Using a local-tree approximation, we derive an analytical estimate of the spreading of products and obtain good agreement if a sufficiently small fraction of the population consists of hipsters. In all networks, we find that either of the two products can become the more popular one at steady state, depending on the fraction of hipsters in the network and on the amount of delay in the knowledge of the product distribution. Our simple model and analysis may help shed light on the road to success for antiestablishment choices in elections, as such success—and qualitative differences in final outcomes between competing products, political candidates, and so on—can arise rather generically in our model from a small number of antiestablishment individuals and ordinary processes of social influence on normal individuals.


title: Understanding the temporal pattern of spreading in heterogeneous networks: Theory of the mean infection time

text: For a reliable prediction of an epidemic or information spreading pattern in complex systems, well-defined measures are essential. In the susceptible-infected model on heterogeneous networks, the cluster of infected nodes in the intermediate-time regime exhibits too large fluctuation in size to use its mean size as a representative value. The cluster size follows quite a broad distribution, which is shown to be derived from the variation of the cluster size with the time when a hub node was first infected. On the contrary, the distribution of the time taken to infect a given number of nodes is well concentrated at its mean, suggesting the mean infection time is a better measure. We show that the mean infection time can be evaluated by using the scaling behaviors of the boundary area of the infected cluster and use it to find a nonexponential but algebraic spreading phase in the intermediate stage on strongly heterogeneous networks. Such slow spreading originates in only small-degree nodes left susceptible, while most hub nodes are already infected in the early exponential-spreading stage. Our results offer a way to detour around large statistical fluctuations and quantify reliably the temporal pattern of spread under structural heterogeneity.


title: Meldepflichten für Infektionskrankheiten und Infektionserreger in Deutschland: Entwicklung und Verbesserungsvorschläge

text: With the entry into force of the Infection Protection Act (IfSG) in 2001, the reporting obligations for infectious diseases and infectious agents were placed on a new foundation. For the first time, a distinction was made between an obligation for the notification of infectious diseases by physicians and a notification obligation for infectious agents by laboratories. The aim was to reduce the notification burden on physicians and thus to improve the quality of the notifications. Since then, numerous new obligations for notifications have been added. The aim of this work is to describe and discuss the mandatory notification of infectious diseases in Germany on the basis of their development – compared to previous regulations in Germany (Federal Communicable Diseases Act) as well as international and Europe-wide recommendations (IHR; decisions of the EU Commission 1999, 2018) – and to submit suggestions for improvement. Regarding the considerable increase in reporting requirements and reports in recent years, and the fact that the IfSG provides other surveillance systems in addition to mandatory reporting, the mandatory reporting system should be focused on the necessary reporting requirements. In a first step, the proposed abolition of the mandatory reporting of noroviruses and rotaviruses could relieve both the notifiers and the health authorities, thus enabling more efficient reporting and more intensive and better investigation by the health authorities.


title: Seasonal fluctuation of beak and feather disease virus (BFDV) infection in wild Crimson Rosellas (Platycercus elegans)

text: Understanding patterns of pathogen emergence can help identify mechanisms involved in transmission dynamics. Beak and feather disease virus (BFDV) poses a major threat world-wide to wild and captive parrots. Yet data from wild birds on seasonal fluctuations in prevalence and infection intensity, and thereby the potential high-risk times for virus transmission, have been lacking. We screened wild Crimson Rosellas (Platycercus elegans) for BFDV in blood and cloacal swabs. Prevalence in blood samples and cloacal swabs, as well as viral load varied with Julian date and in blood, were highest after the breeding season. Breeding birds had lower viral load and lower BFDV prevalence in blood than non-breeding birds (10.1% prevalence in breeding vs. 43.2% in non-breeding birds). BFDV prevalence was much higher in younger (<3 years) than older (≥3 years) birds for both blood samples (42.9% vs. 4.5%) and cloacal swabs (56.4% vs. 12.3%). BFDV status in blood and cloacal samples was not correlated within individuals. We show that, at least in P. elegans, BFDV infection seems to occur year-round, with seasonal changes in prevalence and load found in our samples. Our analyses suggest that the seasonal changes were associated primarily with the breeding season. We also discuss age and sex as important predictors of BFDV infection.


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title: Epidemic Extinction and Control in Heterogeneous Networks

text: We consider epidemic extinction in finite networks with a broad variation in local connectivity. Generalizing the theory of large fluctuations to random networks with a given degree distribution, we are able to predict the most probable, or optimal, paths to extinction in various configurations, including truncated power laws. We find that paths for heterogeneous networks follow a limiting form in which infection first decreases in low-degree nodes, which triggers a rapid extinction in high-degree nodes, and finishes with a residual low-degree extinction. The usefulness of our approach is further demonstrated through optimal control strategies that leverage the dependence of finite-size fluctuations on network topology. Interestingly, we find that the optimal control is a mix of treating both high- and low-degree nodes based on theoretical predictions, in contrast to methods that ignore dynamical fluctuations.


title: Critical Behaviors in Contagion Dynamics

text: We study the critical behavior of a general contagion model where nodes are either active (e.g., with opinion [Formula: see text] , or functioning) or inactive (e.g., with opinion [Formula: see text] , or damaged). The transitions between these two states are determined by (i) spontaneous transitions independent of the neighborhood, (ii) transitions induced by neighboring nodes, and (iii) spontaneous reverse transitions. The resulting dynamics is extremely rich including limit cycles and random phase switching. We derive a unifying mean-field theory. Specifically, we analytically show that the critical behavior of systems whose dynamics is governed by processes (i)–(iii) can only exhibit three distinct regimes: (a) uncorrelated spontaneous transition dynamics, (b) contact process dynamics, and (c) cusp catastrophes. This ends a long-standing debate on the universality classes of complex contagion dynamics in mean field and substantially deepens its mathematical understanding.


title: Epidemic Threshold in Continuous-Time Evolving Networks

text: Current understanding of the critical outbreak condition on temporal networks relies on approximations (time scale separation, discretization) that may bias the results. We propose a theoretical framework to compute the epidemic threshold in continuous time through the infection propagator approach. We introduce the weak commutation condition allowing the interpretation of annealed networks, activity-driven networks, and time scale separation into one formalism. Our work provides a coherent connection between discrete and continuous time representations applicable to realistic scenarios.


title: Finite Temperature Phase Behavior of Viral Capsids as Oriented Particle Shells

text: A general phase plot is proposed for discrete particle shells that allows for thermal fluctuations of the shell geometry and of the inter-particle connectivities. The phase plot contains a first-order melting transition, a buckling transition, and a collapse transition and is used to interpret the thermodynamics of microbiological shells.


title: Degree Dispersion Increases the Rate of Rare Events in Population Networks

text: There is great interest in predicting rare and extreme events in complex systems, and in particular, understanding the role of network topology in facilitating such events. In this Letter, we show that degree dispersion—the fact that the number of local connections in networks varies broadly—increases the probability of large, rare fluctuations in population networks generically. We perform explicit calculations for two canonical and distinct classes of rare events: network extinction and switching. When the distance to threshold is held constant, and hence stochastic effects are fairly compared among networks, we show that there is a universal, exponential increase in the rate of rare events proportional to the variance of a network’s degree distribution over its mean squared.


title: Critical Stretching of Mean-Field Regimes in Spatial Networks

text: We study a spatial network model with exponentially distributed link lengths on an underlying grid of points, undergoing a structural crossover from a random, Erdős-Rényi graph, to a [Formula: see text]-dimensional lattice at the characteristic interaction range [Formula: see text]. We find that, whilst far from the percolation threshold the random part of the giant component scales linearly with [Formula: see text] , close to criticality it extends in space until the universal length scale [Formula: see text] , for [Formula: see text] , before crossing over to the spatial one. We demonstrate the universal behavior of the spatiotemporal scales characterizing this critical stretching phenomenon of mean-field regimes in percolation and in dynamical processes on [Formula: see text] networks, and we discuss its general implications to real-world phenomena, such as neural activation, traffic flows or epidemic spreading.


title: A conformation-based intra-molecular initiation factor identified in the flavivirus RNA-dependent RNA polymerase

text: The flaviviruses pose serious threats to human health. Being a natural fusion of a methyltransferase (MTase) and an RNA-dependent RNA polymerase (RdRP), NS5 is the most conserved flavivirus protein and an important antiviral target. Previously reported NS5 structures represented by those from the Japanese encephalitis virus (JEV) and Dengue virus serotype 3 (DENV3) exhibit two apparently different global conformations, defining two sets of intra-molecular MTase-RdRP interactions. However, whether these NS5 conformations are conserved in flaviviruses and their specific functions remain elusive. Here we report two forms of DENV serotype 2 (DENV2) NS5 crystal structures representing two conformational states with defined analogies to the JEV-mode and DENV3-mode conformations, respectively, demonstrating the conservation of both conformation modes and providing clues for how different conformational states may be interconnected. Data from in vitro polymerase assays further demonstrate that perturbing the JEV-mode but not the DENV3-mode intra-molecular interactions inhibits catalysis only at initiation, while the cell-based virological analysis suggests that both modes of interactions are important for virus proliferation. Our work highlights the role of MTase as a unique intra-molecular initiation factor specifically only through the JEV-mode conformation, providing an example of conformation-based crosstalk between naturally fused protein functional modules.


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title: Interacting opinion and disease dynamics in multiplex networks: Discontinuous phase transition and nonmonotonic consensus times

text: Opinion formation and disease spreading are among the most studied dynamical processes on complex networks. In real societies, it is expected that these two processes depend on and affect each other. However, little is known about the effects of opinion dynamics over disease dynamics and vice versa, since most studies treat them separately. In this work we study the dynamics of the voter model for opinion formation intertwined with that of the contact process for disease spreading, in a population of agents that interact via two types of connections, social and contact. These two interacting dynamics take place on two layers of networks, coupled through a fraction [Formula: see text] of links present in both networks. The probability that an agent updates its state depends on both the opinion and disease states of the interacting partner. We find that the opinion dynamics has striking consequences on the statistical properties of disease spreading. The most important is that the smooth (continuous) transition from a healthy to an endemic phase observed in the contact process, as the infection probability increases beyond a threshold, becomes abrupt (discontinuous) in the two-layer system. Therefore, disregarding the effects of social dynamics on epidemics propagation may lead to a misestimation of the real magnitude of the spreading. Also, an endemic-healthy discontinuous transition is found when the coupling [Formula: see text] overcomes a threshold value. Furthermore, we show that the disease dynamics delays the opinion consensus, leading to a consensus time that varies nonmonotonically with [Formula: see text] in a large range of the model's parameters. A mean-field approach reveals that the coupled dynamics of opinions and disease can be approximately described by the dynamics of the voter model decoupled from that of the contact process, with effective probabilities of opinion and disease transmission.


title: Schlechte Luft, miese Stimmung: Depression

text: Statistiken zeigen, dass Luftverschmutzung neben Gefäßen und Atemwegen wohl auch das Gehirn schädigt. Eine Metaanalyse zeigt eine Häufung von Depressionen und Suiziden im Zusammenhang mit Feinstaubbelastung.


title: COVID-19: Rätsel um den Verlauf bei Kindern: Coronavirus-Pandemie

text: Auch ein halbes Jahr nach dem Auftauchen von SARS-CoV-2 weiß die Fachwelt wenig über den Verlauf der Infektion bei Kindern. Schwere Verläufe sind bei ihnen trotz normaler Ansteckungsraten offenbar sehr selten. Aber warum?


title: Behandlung von Patienten in Corona-Ambulanzen: COVID-19-Pandemie

text: Die Kassenärztlichen Vereinigungen haben dazu aufgerufen, Corona-Ambulanzen aufzubauen. Dort sollen gezielt Patienten mit einer möglichen Corona-Infektion behandelt werden. Im Folgenden wird der Aufbau einer Corona-Ambulanz beschrieben und ein Ausblick gegeben, wie die weitere Entwicklung der ambulanten Behandlung in der Corona-Krise aussehen könnte.


title: Congenital diaphragmatic eventration and hernia sac compared to CDH with true defects: a retrospective cohort study

text: Congenital diaphragmatic eventration (CDE) and congenital diaphragmatic hernia (CDH) with or without hernia sac are three different types of congenital diaphragmatic malformations, which this study evaluates. All surgically treated patients with CDE or Bochdalek type CDH between 2000 and 2016 were included in this retrospective analysis. Demographics, CDH-characteristics, treatment, and clinical outcome were evaluated. In total, 200 patients were included. Patients with an eventration or hernia sac had no significant differences and were compared as patients without a true defect to patients with a true defect. The 1-year survival of patients with a true defect was significantly lower than patients with no true defect (76% versus 97%, p = 0.001). CDH with no true defect had significantly better short-term outcomes than CDH with true defect requiring patch repair. However, at 30 days, they more often required oxygen supplementation (46% versus 26%, p = 0.03) and had a higher recurrence rate (8% versus 0%, p = 0.006) (three eventration and two hernia sac patients). Conclusion: Patients without a true defect seem to have a more similar clinical outcome than CDH patients with a true defect, with a better survival. However, the recurrence rate and duration of oxygen supplementation at 30 days are higher than CDH patients with a true defect.


title: Proportional assist ventilation (PAV) versus neurally adjusted ventilator assist (NAVA): effect on oxygenation in infants with evolving or established bronchopulmonary dysplasia

text: Both proportional assist ventilation (PAV) and neurally adjusted ventilatory assist (NAVA) provide pressure support synchronised throughout the respiratory cycle proportional to the patient’s respiratory demand. Our aim was to compare the effect of these two modes on oxygenation in infants with evolving or established bronchopulmonary dysplasia. Two-hour periods of PAV and NAVA were delivered in random order to 18 infants born less than 32 weeks of gestation. Quasi oxygenation indices (“OI”) and alveolar-arterial (“A-a”) oxygen gradients at the end of each period on PAV, NAVA and baseline ventilation were calculated using capillary blood samples. The mean “OI” was not significantly different on PAV compared to NAVA (7.8 (standard deviation (SD) 3.2) versus 8.1 (SD 3.4), respectively, p = 0.70, but lower on both than on baseline ventilation (mean baseline “OI” 11.0 (SD 5.0)), p = 0.002, 0.004, respectively). The “A-a” oxygen gradient was higher on PAV and baseline ventilation than on NAVA (20.8 (SD 12.3) and 22.9 (SD 11.8) versus 18.5 (SD 10.8) kPa, p = 0.015, < 0.001, respectively). Conclusion: Both NAVA and PAV improved oxygenation compared to conventional ventilation. There was no significant difference in the mean “OI” between the two modes, but the mean “A-a” gradient was better on NAVA.


title: Beneficial effect modification on survival outcome of sepsis between ART-123 and polymyxin B‑immobilised haemoperfusion: a nationwide Japanese registry study

text: BACKGROUND: Although recently published randomised controlled trials did not confirm significant positive effect of ART-123 or polymyxin B‑immobilised haemoperfusion (PMX-HP) on survival outcome, previous studies using a dataset of 3195 patients with sepsis registered at 42 intensive care units throughout Japan revealed significantly reduced mortality following these treatments. A study has suggested the efficacy of combination therapy with ART-123 and PMX-HP; however, it did not evaluate the effect modification between them. We hypothesised that coadministration of ART-123 and PMX-HP has a significant positive effect modification on survival outcome. The purpose of this study was to evaluate the effect modification between ART-123 and PMX-HP treatment on the survival outcome of sepsis using post hoc analysis of the dataset of the Japan Septic Disseminated Intravascular Coagulation registry. RESULTS: Of the 3195 patients recorded in the registry, 2350 were analysed. The product term between ART-123 and PMX-HP was analysed by the Cox regression model to evaluate significance. The primary outcome of this study was hospital mortality. Although the administration of ART-123 was independently positively associated with survival outcome (adjusted hazard ratio [HR]: 0.834, 95% confidence interval [CI] 0.695–0.999; P = 0.049) in the model prior to the introduction of the product term, a significant effect modification on survival outcome was observed between the administration of ART-123 and PMX-HP treatment (adjusted HR: 0.667, 95% CI 0.462–0.961; P = 0.030). CONCLUSIONS: The main effect of the administration of ART-123 may be beneficial for survival outcome in patients with sepsis. In addition, a significant beneficial effect modification on survival outcome was observed between the administration of ART-123 and PMX-HP treatment.


title: In vitro characterization of PrismaLung+: a novel ECCO(2)R device

text: BACKGROUND: Invasive mechanical ventilation is lifesaving in the setting of severe acute respiratory failure but can cause ventilation-induced lung injury. Advances in extracorporeal CO(2) removal (ECCO(2)R) technologies may facilitate more protective lung ventilation in acute respiratory distress syndrome, and enable earlier weaning and/or avoid invasive mechanical ventilation entirely in chronic obstructive pulmonary disease exacerbations. We evaluated the in vitro CO(2) removal capacity of the novel PrismaLung+ ECCO(2)R device compared with two existing gas exchangers. METHODS: The in vitro CO(2) removal capacity of the PrismaLung+ (surface area 0.8 m(2), Baxter) was compared with the PrismaLung (surface area 0.35 m(2), Baxter) and A.L.ONE (surface area 1.35 m(2), Eurosets) devices, using a closed-loop bovine blood–perfused extracorporeal circuit. The efficacy of each device was measured at varying pCO(2) inlet (p(in)CO(2)) levels (45, 60, and 80 mmHg) and blood flow rates (Q(B)) of 200–450 mL/min; the PrismaLung+ and A.L.ONE devices were also tested at a Q(B) of 600 mL/min. The amount of CO(2) removed by each device was assessed by measurement of the CO(2) infused to maintain circuit equilibrium (CO(2) infusion method) and compared with measured CO(2) concentrations in the inlet and outlet of the CO(2) removal device (blood gas analysis method). RESULTS: The PrismaLung+ device performed similarly to the A.L.ONE device, with both devices demonstrating CO(2) removal rates ~ 50% greater than the PrismaLung device. CO(2) removal rates were 73 ± 4.0, 44 ± 2.5, and 72 ± 1.9 mL/min, for PrismaLung+, PrismaLung, and A.L.ONE, respectively, at Q(B) 300 mL/min and p(in)CO(2) 45 mmHg. A Bland–Altman plot demonstrated that the CO(2) infusion method was comparable to the blood gas analysis method for calculating CO(2) removal. The resistance to blood flow across the test device, as measured by pressure drop, varied as a function of blood flow rate, and was greatest for PrismaLung and lowest for the A.L.ONE device. CONCLUSIONS: The newly developed PrismaLung+ performed more effectively than PrismaLung, with performance of CO(2) removal comparable to A.L.ONE at the flow rates tested, despite the smaller membrane surface area of PrismaLung+ versus A.L.ONE. Clinical testing of PrismaLung+ is warranted to further characterize its performance.


title: Infectious Complications Following CD19 Chimeric Antigen Receptor T-cell Therapy for Children, Adolescents, and Young Adults

text: BACKGROUND: Infectious complications of chimeric antigen receptor (CAR) T-cell immunotherapy in children and young adults have not been well described. METHODS: Medical records of patients ≤26 years old receiving CD19 CAR T-cell infusion (CTI) at a single institution between 2014 and 2017 were reviewed. The number of infections per 100 days-at-risk (infection density) in the 90 days preceding and 0–28 and 29–90 days after CTI was calculated. Poisson regression and Cox analyses were utilized to identify risk factors for infections. RESULTS: Eighty-three patients received CTI during the study period. Most patients (98%) had refractory or relapsed acute lymphoblastic leukemia (ALL). Infections occurred in 54% of patients in the 90 days before CTI (infection density, 1.23) and in 40% of patients in the first 28 days following CTI (infection density, 2.89). Infection density decreased to 0.55 in the 29–90 days post-CTI. Most infections were bacteremias (39%) or respiratory viral infections (43%). Pre-CTI risk factors associated with infection included prior hematopoietic cell transplantation (HCT), immunoglobulin G (IgG) level <400 mg/dL, and lymphodepletion other than cyclophosphamide plus fludarabine; post-CTI risk factors included higher-severity CRS and IgG <400 mg/dL. CONCLUSIONS: Infection rates in children and young adults receiving CD19 CAR T-cell therapy increase in the first month and then decline. Understanding types and timing of infections and contributing risk factors may help inform prophylactic and monitoring strategies. Specific attention should be given to patients with prior HCT, severe hypogammaglobulinemia, and severe CRS.


title: Pervasive refusal syndrome: systematic review of case reports

text: Pervasive refusal syndrome (PRS) is a complex condition that affects young people leading to social withdrawal, inability or refusal to eat, drink, mobilise or speak. The affected individual regresses and is unable to self-care and quite characteristically will resist rehabilitation, worsen with praise or remain entirely passive. This systematic review was aimed at describing clinical features of PRS, current interventions and to summarise some of the nosological aspects of the condition. Without language restriction, an electronic search was conducted in Embase, PsychInfo, Medline, Cochrane library, and PubMed databases yielding 29 articles with a total of 79 cases. We performed a risk of assessment bias using an adapted Newcastle–Ottawa Scale and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. 124 articles were identified, of which 29 were included and these yielded 79 cases. Seventy-six percent of the studies had a low rate of risk of assessment bias (good quality). Our results show that PRS overlaps with several conditions, mainly affects young females aged 7–15 years and has a recovery rate of 78% if diagnosed and treated early but the duration of inpatient treatment may last up to 9.44 months (8.82 SD). The patients had multiple inter-dependent risks. The major predisposing factors included vulnerable premorbid personality and pre-existing mental disorder. Precipitating factors were stressors such as infection and traumatic experiences. Enmeshed parent–child relationship served as a maintaining factor. The themes of treatment approach are essentially rehabilitative: (1) working collaboratively with patient and family, (2) having access to multidisciplinary team, and (3) peer/group supervision. This study has systematically evaluated a large sample of patients with PRS to ascertain its clinical features and the core elements of its treatment. Its key treatment approach is a multi-modal rehabilitative strategy that is compassionate, transparent and inclusive. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00787-020-01536-1) contains supplementary material, which is available to authorized users.


Less daily oral hygiene is more in the ICU: not sure


title: Optimization and One-Step Purification of Recombinant V Antigen Production from Yersinia pestis

text: The purpose of this study was to develop an efficient and inexpensive method for the useful production of recombinant protein V antigen, an important virulence factor for Yersinia pestis. To this end, the synthetic gene encoding the V antigen was subcloned into the downstream of the intein (INT) and chitin-binding domain (CBD) from the pTXB1 vector using specific primers. In the following, the produced new plasmid, pTX-V, was transformed into E. coli ER(2566) strain, and the expression accuracy was confirmed using electrophoresis and Western blotting. In addition, the effects of medium, inducer, and temperature on the enhancement of protein production were studied using the Taguchi method. Finally, the V antigen was purified by a chitin affinity column using INT and CBD tag. The expression was induced by 0.05 mM IPTG at 25 °C under optimal conditions including TB medium. It was observed that the expression of the V-INT–CBD fusion protein was successfully increased to more than 40% of the total protein. The purity of V antigen was as high as 90%. This result indicates that V antigen can be produced at low cost and subjected to one-step purification using a self-cleaving INT tag.


title: Pulmonary non-tuberculous mycobacterial infections: current state and future management

text: Currently, there is a trend of increasing incidence in pulmonary non-tuberculous mycobacterial infections (PNTM) together with a decrease in tuberculosis (TB) incidence, particularly in developed countries. The prevalence of PNTM in underdeveloped and developing countries remains unclear as there is still a lack of detection methods that could clearly diagnose PNTM applicable in these low-resource settings. Since non-tuberculous mycobacteria (NTM) are environmental pathogens, the vicinity favouring host-pathogen interactions is known as important predisposing factor for PNTM. The ongoing changes in world population, as well as socio-political and economic factors, are linked to the rise in the incidence of PNTM. Development is an important factor for the improvement of population well-being, but it has also been linked, in general, to detrimental environmental consequences, including the rise of emergent (usually neglected) infectious diseases, such as PNTM. The rise of neglected PNTM infections requires the expansion of the current efforts on the development of diagnostics, therapies and vaccines for mycobacterial diseases, which at present, are mainly focused on TB. This review discuss the current situation of PNTM and its predisposing factors, as well as the efforts and challenges for their control.


title: Australia’s Gambling Epidemic: The Role of Neighbourhood Ethnic Diversity

text: Gambling is a popular leisure activity but also a significant public health issue in Australia. The severity of gambling in Australia is characterised by unprecedented gambling expenditures and losses representing the highest gambling rates in the world, which has led to renewed interests in understanding the factors influencing gambling behaviours. We contribute to the debate on the determinants of gambling by providing the first study that examines the impact of ethnic diversity on gambling. Using data from the Household, Income and Labour Dynamics in Australia survey, we find that ethnic diversity is positively associated with gambling. This result is robust to alternative estimation approaches, alternative ways of measuring ethnic diversity and irrespective of whether gambling is measured using the Problem Gambling Severity Index, gambling expenditures or number of gambling activities. Our results also suggest stronger effects of ethnic diversity for problem gamblers compared to gamblers in other risk categories. These results support the need for policies that promote social capital in diverse communities to reduce the risks of social isolation, which is an important motivator of gambling behaviour.


title: The Evaluation of a Health-Based Education and Behavioral Change Retreat on Quality of Life, Physical Activity and Psychosocial Health in Adult Cancer Survivors: An Exploratory Analysis

text: Knights Cabin Cancer retreats may represent an ideal opportunity and environment to fill the gap in health education and behavioral change. It is unknown, however, whether the program improves quality of life, physical activity, and psychosocial health. Cancer survivors who enrolled in a retreat completed online questionnaires prior to and 3 months post to assess health-related quality of life, physical activity, and psychosocial variables; perceived stress, depression, and self-esteem. Paired t tests were utilized to compare changes pre and 3 months post the retreat. The majority of the 51 cancer survivors were female with a mean age of 53. The mental component summary of health-related quality of life (p < 0.016) and physical activity score (p = 0.048) was significantly improved at follow-up, while the physical component summary of health-related quality of life was not (p = 0.139). In addition, participants showed an improvement in psychosocial variables of self-esteem (p = 0.016) and depression (p = 0.016) after 3 months but did not show a significant improvement in perceived stress (p = 0.054). Improvements were seen following a Knights Cabin Cancer retreat in mental health-related quality of life and physical activity as well as some psychosocial variables. Further research is needed to evaluate the individual program components and the sustainability of the program in the community.


title: Hydrocortisone and bronchopulmonary dysplasia: variables associated with response in premature infants

text: OBJECTIVE: The primary objective was to evaluate hydrocortisone’s efficacy for decreasing respiratory support in premature infants with developing bronchopulmonary dysplasia (BPD). Secondary objectives included assessment of the impact of intrauterine growth restriction (IUGR), maternal history of chorioamnionitis, side effects and route of administration associated with hydrocortisone’s efficacy. Dexamethasone as second-line treatment to decrease respiratory support was reviewed. METHODS: Retrospective chart review of preterm infants requiring respiratory support receiving hydrocortisone. RESULTS: A total of 48 patients were included. Successful extubation was achieved in 50% of intubated patients after hydrocortisone treatment with no major complications. In our small study, history of maternal chorioamnionitis, IUGR or route of administration did not affect the response. Rescue dexamethasone after hydrocortisone therapy was ineffective in the ten patients who failed extubation following hydrocortisone. CONCLUSION: Hydrocortisone is effective in decreasing respiratory support in patients with developing BPD without major complications. Randomized studies are warranted to confirm our findings.


title: Detection of Group A Beta Hemolytic Streptococci Species, emm, and Exotoxin Genes Isolated from Patients with Tonsillopharyngitis

text: Group A Beta Hemolytic Streptococci (GAS) is the most critical human pathogen that leads to tonsillopharyngitis. The aims of this study were to identify GAS isolates and to determine emm typing, the coverage rate of available vaccines, and the distribution of superantigen gene profiles. 15 GAS isolates were isolated from throat cultures of 200 patients with tonsillopharyngitis, who were admitted to Canakkale Health Application and Research Hospital between October 2017 and May 2018. Identification of the isolates was performed by conventional methods and 16S rRNA sequence analysis. emm typing and exotoxin profiling of the isolates were performed by polymerase chain reaction. 7.5% GAS was detected in 200 patients. All the GAS isolates were identified as S. pyogenes. emm typing can be carried out in 13 S. pyogenes isolates. emm89 (33.3%), emm44 (20%), emm6 (13.3%), emm84 (6.7%), emm1 (6.7%), and emm18.1 (6.7%) were found to be six emm types. The coverage rate of S. pyogenes strains for 26-valent vaccine was 61.5% and for the 30-valent vaccine 84.6%. The most common exotoxin was speB (86.7%), followed by speC (60%), speF (33.3%), ssa (26.7%), speA (20%), speM (20%), speJ (13.3%), speL (6.7%), and speI (6.7%). As a result of determining the emm types of S. pyogenes species in Canakkale, it was concluded that the potential of 30-valent vaccine should be considered in Turkey and development of vaccines containing exotoxin types may be beneficial.


title: Incidence and risk factors for acquired colonization and infection due to extended-spectrum beta-lactamase-producing Gram-negative bacilli: a retrospective analysis in three ICUs with low multidrug resistance rate

text: The purpose of this study is to assess risk factors for the acquisition of extended-spectrum β-lactamase-producing Gram-negative bacilli (ESBL-GNB) colonization and infection (AI) in ICUs with low ESBL-GNB prevalence rate. We conducted a retrospective observational study in three ICUs in Bretagne, France. All patients admitted from January 2016 to September 2017 with a length of stay of 2 days or more were included. Universal screening for ESBL-GNB colonization was performed in all participating ICUs. Of the 3250 included patients, 131 (4.0%) were colonized at admission, 59 acquired colonization while hospitalized (1.9%; 95% CI [1.5–2.5%]), and 15 (0.5%; 95% CI [0.3–0.8%]) acquired ESBL-GNB infections. In the case of infection, the specificity and the negative predictive values of preexistent colonization for the ESBL-GNB etiology were 93.2% [91.5–95.1%] and 95.2% [93.5–97.1%], respectively. Colonization was the main risk factor for ESBL-GNB AI (OR = 9.61; 95% CI [2.86–32.29]; p < 0.001). Antimicrobial susceptibility of non-ESBL-GNB isolates responsible for AI was similar for any non-carbapenem β-lactam (95%) and imipenem (94%). ESBL-GNB AIs were rare in ICUs with low ESBL-GNB prevalence rate. Prior colonization was the main risk factor for subsequent infection. Empirical carbapenem therapy could be avoided in non ESBL-GNB colonized patients with suspected AI. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10096-019-03800-y) contains supplementary material, which is available to authorized users.


title: Long-term outcome of surgical excision for treatment of cervicofacial granulomatous lymphadenitis in children

text: PURPOSE: Granulomatous inflammation is a common cause of subacute cervicofacial lymphadenitis in children. Nontuberculous mycobacterial (NTM) infections and cat-scratch disease (CSD) are the most frequent causes. Optimal treatment, which may include surgery, antibiotic treatment or wait-and-see approach, is debatable. The goal of this study was to compare the short- and long-term outcome of various surgical procedures. METHODS: Case series with a chart review of all children treated by surgical excision of granulomatous lymph nodes in the cervicofacial area from 2000 to 2016 at two tertiary care centers. RESULTS: Forty patients were included in this study. The median age at first symptoms was 3.7 years (13 months–14 years). Mean follow-up was 5.8 years (6 months–15.3 years). 25 patients fit with diagnosis of NTM infection, 6 with CSD while diagnosis remained uncertain in 9 patients. The primary surgical procedure consisted of total excision (n = 27), incision/drainage (n = 9) or incomplete excision (n = 4). None of the patients treated by primary complete excision needed further intervention contrary to the group of patients with incomplete surgical procedures where additional surgical management was required in 54%. At follow-up, all patients were healthy without evidence of recurrence. CONCLUSION: We advocate early surgical intervention with complete excision to reach quick resolution and reduce the need for additional surgery. The long-term outcome was favorable.


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title: Integrity of plasma DNA is inversely correlated with vaccine-induced antitumor immunity in ovarian cancer patients

text: Cancer immunotherapy including vaccine therapy is a promising modality for cancer treatment, but few patients show its clinical benefits currently. The identification of biomarkers that can identify patients who will benefit from cancer immunotherapy is thus important. Here, we investigated the potential utility of the circulating cell-free DNA (cfDNA) integrity—a ratio of necrotic cell-derived, longer DNA fragments versus apoptotic cell-derived shorter fragments of Alu gene—as a biomarker of vaccine therapy for patients with ovarian cancer. We analyzed plasma samples from 39 patients with advanced or recurrent ovarian cancer enrolled in clinical trials for personalized peptide vaccinations. We observed that (1) the cfDNA integrity was decreased after the first cycle of vaccination, and (2) the decreased levels of cfDNA integrity were correlated with vaccine-induced immune responses; i.e., decreased cfDNA integrity was observed in 91.7% and 59.3% of the IgG-positive and negative patients, respectively (p = 0.0445). Similarly, decreased cfDNA integrity was observed in 92.9% and 56.0% of CTL response-positive and negative patients, respectively (p = 0.0283). These results suggest that the circulating cfDNA integrity is a possible biomarker for cancer vaccine therapy.