Spatial analyses of wildlife contact networks

Integrating graph and reinforcement learning for vaccination strategies in complex networks

Pandemics like COVID-19 have a huge impact on human society and the global economy. Vaccines are effective in the fight against these pandemics but often in limited supplies, particularly in the early stages. Thus, it is imperative to distribute such crucial public goods efficiently. Identifying and vaccinating key spreaders (i.e., influential nodes) is an effective approach to break down the virus transmission network, thereby inhibiting the spread of the virus. Previous methods for identifying influential nodes in networks lack consistency in terms of effectiveness and precision. Their applicability also depends on the unique characteristics of each network. Furthermore, most of them rank nodes by their individual influence in the network without considering mutual effects among them. However, in many practical settings like vaccine distribution, the challenge is how to select a group of influential nodes. This task is more complex due to the interactions and collective influence of these nodes together. This paper introduces a new framework integrating Graph Neural Network (GNN) and Deep Reinforcement Learning (DRL) for vaccination distribution. This approach combines network structural learning with strategic decision-making. It aims to efficiently disrupt the network structure and stop disease spread through targeting and removing influential nodes. This method is particularly effective in complex environments, where traditional strategies might not be efficient or scalable. Its effectiveness is tested across various network types including both synthetic and real-world datasets, demonstrting a potential for real-world applications in fields like epidemiology and cybersecurity. This interdisciplinary approach shows the capabilities of deep learning in understanding and manipulating complex network systems.

Sparse Bayesian Learning for Switching Network Identification

Learning dynamical networks based on time series of nodal states is of significant interest in systems science, computer science, and control engineering. Despite recent progress in network identification, most research focuses on static structures rather than switching ones. Therefore, this article develops a method for identifying the structures of switching networks by exploring and leveraging both temporal and spatial structural information that characterizes the switching process. The proposed method employs a new sparse Bayesian learning algorithm based on coupled hyperblocks to estimate unknown switching instants. Experimental results on benchmark artificial and real networks are elaborated to demonstrate the effectiveness and superiority of the proposed method.

Effects of food supplementation and helminth removal on space use and spatial overlap in wild rodent populations

Animal space use and spatial overlap can have important consequences for population-level processes such as social interactions and pathogen transmission. Identifying how environmental variability and inter-individual variation affect spatial patterns and in turn influence interactions in animal populations is a priority for the study of animal behaviour and disease ecology. Environmental food availability and macroparasite infection are common drivers of variation, but there are few experimental studies investigating how they affect spatial patterns of wildlife. Bank voles (Clethrionomys glareolus) are a tractable study system to investigate spatial patterns of wildlife and are amenable to experimental manipulations. We conducted a replicated, factorial field experiment in which we provided supplementary food and removed helminths in vole populations in natural forest habitat and monitored vole space use and spatial overlap using capture-mark-recapture methods. Using network analysis, we quantified vole space use and spatial overlap. We compared the effects of food supplementation and helminth removal and investigated the impacts of season, sex and reproductive status on space use and spatial overlap. We found that food supplementation decreased vole space use while helminth removal increased space use. Space use also varied by sex, reproductive status and season. Spatial overlap was similar between treatments despite up to threefold differences in population size. By quantifying the spatial effects of food availability and macroparasite infection on wildlife populations, we demonstrate the potential for space use and population density to trade-off and maintain consistent spatial overlap in wildlife populations. This has important implications for spatial processes in wildlife including pathogen transmission.

Food availability leads to more connected contact networks among peridomestic zoonotic reservoir hosts

The North American deermouse (Peromyscus maniculatus) is a reservoir host for many zoonotic pathogens. Deermice have been well studied, but few studies have attempted to understand social interactions within the species despite these interactions being key to understanding disease transmission. We performed an experiment to determine if supplemental food or nesting material affected social interactions of deermice and tested if interactions increased with increasing population density. We constructed three simulated buildings that received one of three treatments: food, nesting material, or control. Mice were tagged with passive integrated transponder (PIT) tags, and their movement in and out of buildings was monitored with PIT tag readers. PIT tag readings were used to create contact networks, assuming a contact if two deermice were in the same building at the same time. We found that buildings with food led to contact networks that were approximately 10 times more connected than buildings with nesting material or control buildings. We also saw a significant effect of population density on the average number of contacts per individual. These results suggest that food supplementation which is common in peridomestic settings, can significantly increase contacts between reservoir hosts, potentially leading to increased transmission of zoonotic viruses within the reservoir host and from reservoir hosts to humans.

Effects of an IgE receptor polymorphism acting on immunity, susceptibility to infection, and reproduction in a wild rodent

The genotype of an individual is an important predictor of their immune function, and subsequently, their ability to control or avoid infection and ultimately contribute offspring to the next generation. However, the same genotype, subjected to different intrinsic and/or extrinsic environments, can also result in different phenotypic outcomes, which can be missed in controlled laboratory studies. Natural wildlife populations, which capture both genotypic and environmental variability, provide an opportunity to more fully understand the phenotypic expression of genetic variation. We identified a synonymous polymorphism in the high-affinity Immunoglobulin E (IgE) receptor (GC and non-GC haplotypes) that has sex-dependent effects on immune gene expression, susceptibility to infection, and reproductive success of individuals in a natural population of field voles (Microtus agrestis). We found that the effect of the GC haplotype on the expression of immune genes differed between sexes. Regardless of sex, both pro-inflammatory and anti-inflammatory genes were more highly relatively expressed in individuals with the GC haplotype than individuals without the haplotype. However, males with the GC haplotype showed a stronger signal for pro-inflammatory genes, while females showed a stronger signal for anti-inflammatory genes. Furthermore, we found an effect of the GC haplotype on the probability of infection with a common microparasite, Babesia microti, in females - with females carrying the GC haplotype being more likely to be infected. Finally, we found an effect of the GC haplotype on reproductive success in males - with males carrying the GC haplotype having a lower reproductive success. This is a rare example of a polymorphism whose consequences we are able to follow across immunity, infection, and reproduction for both males and females in a natural population.

Pathogen transmission modes determine contact network structure, altering other pathogen characteristics

Pathogen traits can vary greatly and heavily impact the ability of a pathogen to persist in a population. Although this variation is fundamental to disease ecology, little is known about the evolutionary pressures that drive these differences, particularly where they interact with host behaviour. We hypothesized that host behaviours relevant to different transmission routes give rise to differences in contact network structure, constraining the space over which pathogen traits can evolve to maximize fitness. Our analysis of 232 contact networks across mammals, birds, reptiles, amphibians, arthropods, fish and molluscs found that contact network topology varies by contact type, most notably in networks that are representative of fluid-exchange transmission. Using infectious disease model simulations, we showed that these differences in network structure suggest pathogens transmitted through fluid-exchange contact types will need traits associated with high transmissibility to successfully proliferate, compared to pathogens that transmit through other types of contact. These findings were supported through a review of known traits of pathogens that transmit in humans. Our work demonstrates that contact network structure may drive the evolution of compensatory pathogen traits according to transmission strategy, providing essential context for understanding pathogen evolution and ecology.

Flocking dynamics mediated by weighted social networks

We study the effects of animal social networks with a weighted pattern of interactions on the flocking transition exhibited by models of self-organized collective motion. We consider variations of traditional models of collective motion in which interactions between individuals are mediated by static complex weighted networks, representing patterns of social interactions. For a model representing dynamics on a one-dimensional substrate, application of a heterogeneous mean-field theory provides a phase diagram as function of the heterogeneity of the network connections and the correlations between weights and degree. In this diagram we observe two phases, one corresponding to the presence of a transition and other to a transition suppressed in an always ordered system, already observed in the nonweighted case. Interestingly, a third phase, with no transition in an always disordered state, is also obtained. These predictions, numerically recovered in computer simulations, are also fulfilled for the more realistic Vicsek model, with movement in a two-dimensional space. Additionally, we observe at finite network sizes the presence of a maximum threshold for particular weight configurations, indicating that it is possible to tune weights to achieve a maximum resilience to noise effects. Simulations in real weighted animal social networks show that, in general, the presence of weights diminishes the value of the flocking threshold, thus increasing the fragility of the flocking state. The shift in the threshold is observed to depend on the heterogeneity of the weight pattern.

A new method for characterising shared space use networks using animal trapping data

Abstract

Studying the social behaviour of small or cryptic species often relies on constructing networks from sparse point-based observations of individuals (e.g. live trapping data). A common approach assumes that individuals that have been detected sequentially in the same trapping location will also be more likely to have come into indirect and/or direct contact. However, there is very little guidance on how much data are required for making robust networks from such data. In this study, we highlight that sequential trap sharing networks broadly capture shared space use (and, hence, the potential for contact) and that it may be more parsimonious to directly model shared space use. We first use empirical data to show that characteristics of how animals use space can help us to establish new ways to model the potential for individuals to come into contact. We then show that a method that explicitly models individuals’ home ranges and subsequent overlap in space among individuals (spatial overlap networks) requires fewer data for inferring observed networks that are more strongly correlated with the true shared space use network (relative to sequential trap sharing networks). Furthermore, we show that shared space use networks based on estimating spatial overlap are also more powerful for detecting biological effects. Finally, we discuss when it is appropriate to make inferences about social interactions from shared space use. Our study confirms the potential for using sparse trapping data from cryptic species to address a range of important questions in ecology and evolution.

Significance statement

Characterising animal social networks requires repeated (co-)observations of individuals. Collecting sufficient data to characterise the connections among individuals represents a major challenge when studying cryptic organisms—such as small rodents. This study draws from existing spatial mark-recapture data to inspire an approach that constructs networks by estimating space use overlap (representing the potential for contact). We then use simulations to demonstrate that the method provides consistently higher correlations between inferred (or observed) networks and the true underlying network compared to current approaches and requires fewer observations to reach higher correlations. We further demonstrate that these improvements translate to greater network accuracy and to more power for statistical hypothesis testing.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00265-022-03222-5.

Mean-field theory for double-well systems on degree-heterogeneous networks

Many complex dynamical systems in the real world, including ecological, climate, financial and power-grid systems, often show critical transitions, or tipping points, in which the system’s dynamics suddenly transit into a qualitatively different state. In mathematical models, tipping points happen as a control parameter gradually changes and crosses a certain threshold. Tipping elements in such systems may interact with each other as a network, and understanding the behaviour of interacting tipping elements is a challenge because of the high dimensionality originating from the network. Here, we develop a degree-based mean-field theory for a prototypical double-well system coupled on a network with the aim of understanding coupled tipping dynamics with a low-dimensional description. The method approximates both the onset of the tipping point and the position of equilibria with a reasonable accuracy. Based on the developed theory and numerical simulations, we also provide evidence for multistage tipping point transitions in networks of double-well systems.

Group size and modularity interact to shape the spread of infection and information through animal societies

Social interactions between animals can provide many benefits, including the ability to gain useful environmental information through social learning. However, these social contacts can also facilitate the transmission of infectious diseases through a population. Animals engaging in social interactions therefore face a trade-off between the potential informational benefits and the risk of acquiring disease. Theoretical models have suggested that modular social networks, associated with the formation of groups or sub-groups, can slow spread of infection by trapping it within particular groups. However, these social structures will not necessarily impact the spread of information in the same way if its transmission follows a "complex contagion", e.g. through individuals disproportionally copying the majority (conformist learning). Here we use simulation models to demonstrate that modular networks can promote the spread of information relative to the spread of infection, but only when the network is fragmented and group sizes are small. We show that the difference in transmission between information and disease is maximised for more well-connected social networks when the likelihood of transmission is intermediate. Our results have important implications for understanding the selective pressures operating on the social structure of animal societies, revealing that highly fragmented networks such as those formed in fission-fusion social groups and multilevel societies can be effective in modulating the infection-information trade-off for individuals within them.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00265-021-03102-4.

Co-structure analysis and genetic associations reveal insights into pinworms (Trypanoxyuris) and primates (Alouatta palliata) microevolutionary dynamics

BACKGROUND

In parasitism arm race processes and red queen dynamics between host and parasites reciprocally mold many aspects of their genetics and evolution. We performed a parallel assessment of population genetics and demography of two species of pinworms with different degrees of host specificity (Trypanoxyuris multilabiatus, species-specific; and T. minutus, genus-specific) and their host, the mantled howler monkey (Alouatta palliata), based on mitochondrial DNA sequences and microsatellite loci (these only for the host). Given that pinworms and primates have a close co-evolutionary history, covariation in several genetic aspects of their populations is expected.

RESULTS

Mitochondrial DNA revealed two genetic clusters (West and East) in both pinworm species and howler monkeys, although population structure and genetic differentiation were stronger in the host, while genetic diversity was higher in pinworms than howler populations. Co-divergence tests showed no congruence between host and parasite phylogenies; nonetheless, a significant correlation was found between both pinworms and A. palliata genetic pairwise distances suggesting that the parasites' gene flow is mediated by the host dispersal. Moreover, the parasite most infective and the host most susceptible haplotypes were also the most frequent, whereas the less divergent haplotypes tended to be either more infective (for pinworms) or more susceptible (for howlers). Finally, a positive correlation was found between pairwise p-distance of host haplotypes and that of their associated pinworm haplotypes.

CONCLUSION

The genetic configuration of pinworm populations appears to be molded by their own demography and life history traits in conjunction with the biology and evolutionary history of their hosts, including host genetic variation, social interactions, dispersal and biogeography. Similarity in patterns of genetic structure, differentiation and diversity is higher between howler monkeys and T. multilabiatus in comparison with T. minutus, highlighting the role of host-specificity in coevolving processes. Trypanoxyuris minutus exhibits genetic specificity towards the most frequent host haplotype as well as geographic specificity. Results suggest signals of potential local adaptation in pinworms and further support the notion of correlated evolution between pinworms and their primate hosts.

Badger territoriality maintained despite disturbance of major road construction

Road ecology has traditionally focused on the impact of in-situ and functional roads on wildlife. However, road construction also poses a major, yet understudied, threat and the implications for key aspects of animal behaviour are unknown. Badgers (Meles meles) have been implicated in the transmission of tuberculosis to cattle. There are concerns that environmental disturbances, including major road construction, can disrupt badger territoriality, promoting the spread of the disease to cattle. To address these knowledge gaps the ranging behaviour of a medium-density Irish badger population was monitored using GPS-tracking collars before, during, and after a major road realignment project that bisected the study area. We estimated badgers' home range sizes, nightly distances travelled, and the distance and frequency of extra-territorial excursions during each phase of the study and quantified any changes to these parameters. We show that road construction had a very limited effect on ranging behaviour. A small increase in nightly distance during road construction did not translate into an increase in home range size, nor an increase in the distance or frequency of extra-territorial excursions during road construction. In addition, suitable mitigation measures to prevent badger deaths appeared to ensure that normal patterns of ranging behaviour continued once the new road was in place. We recommend that continuous badger-proof fencing be placed along the entire length of new major roads, in combination with appropriately sited underpasses. Our analysis supports the view that road construction did not cause badgers to change their ranging behaviour in ways likely to increase the spread of tuberculosis.

Adjustable reach in a network centrality based on current flows

Centrality, which quantifies the "importance" of individual nodes, is among the most essential concepts in modern network theory. Most prominent centrality measures can be expressed as an aggregation of influence flows between pairs of nodes. As there are many ways in which influence can be defined, many different centrality measures are in use. Parametrized centralities allow further flexibility and utility by tuning the centrality calculation to the regime most appropriate for a given purpose and network. Here we identify two categories of centrality parameters. Reach parameters control the attenuation of influence flows between distant nodes. Grasp parameters control the centrality's tendency to send influence flows along multiple, often nongeodesic paths. Combining these categories with Borgatti's centrality types [Borgatti, Soc. Networks 27, 55 (2005)0378-873310.1016/j.socnet.2004.11.008], we arrive at a classification system for parametrized centralities. Using this classification, we identify the notable absence of any centrality measures that are radial, reach parametrized, and based on acyclic, conserved flows of influence. We therefore introduce the ground-current centrality, which is a measure of precisely this type. Because of its unique position in the taxonomy, the ground-current centrality differs significantly from similar centralities. We demonstrate that, compared to other conserved-flow centralities, it has a simpler mathematical description. Compared to other reach-parametrized centralities, it robustly preserves an intuitive rank ordering across a wide range of network architectures, capturing aspects of both the closeness and betweenness centralities. We also show that it produces a consistent distribution of centrality values among the nodes, neither trivially equally spread (delocalization) nor overly focused on a few nodes (localization). Other reach-parametrized centralities exhibit both of these behaviors on regular networks and hub networks, respectively. We compare the properties of the ground-current centrality with several other reach-parametrized centralities on four artificial networks and seven real-world networks.

Increasing efficacy of contact-tracing applications by user referrals and stricter quarantining

We study the effects of two mechanisms which increase the efficacy of contact-tracing applications (CTAs) such as the mobile phone contact-tracing applications that have been used during the COVID-19 epidemic. The first mechanism is the introduction of user referrals. We compare four scenarios for the uptake of CTAs-(1) the p% of individuals that use the CTA are chosen randomly, (2) a smaller initial set of randomly-chosen users each refer a contact to use the CTA, achieving p% in total, (3) a small initial set of randomly-chosen users each refer around half of their contacts to use the CTA, achieving p% in total, and (4) for comparison, an idealised scenario in which the p% of the population that uses the CTA is the p% with the most contacts. Using agent-based epidemiological models incorporating a geometric space, we find that, even when the uptake percentage p% is small, CTAs are an effective tool for mitigating the spread of the epidemic in all scenarios. Moreover, user referrals significantly improve efficacy. In addition, it turns out that user referrals reduce the quarantine load. The second mechanism for increasing the efficacy of CTAs is tuning the severity of quarantine measures. Our modelling shows that using CTAs with mild quarantine measures is effective in reducing the maximum hospital load and the number of people who become ill, but leads to a relatively high quarantine load, which may cause economic disruption. Fortunately, under stricter quarantine measures, the advantages are maintained but the quarantine load is reduced. Our models incorporate geometric inhomogeneous random graphs to study the effects of the presence of super-spreaders and of the absence of long-distant contacts (e.g., through travel restrictions) on our conclusions.

Multiple spatial behaviours govern social network positions in a wild ungulate

The structure of wild animal social systems depends on a complex combination of intrinsic and extrinsic drivers. Population structuring and spatial behaviour are key determinants of individuals' observed social behaviour, but quantifying these spatial components alongside multiple other drivers remains difficult due to data scarcity and analytical complexity. We used a 43-year dataset detailing a wild red deer population to investigate how individuals' spatial behaviours drive social network positioning, while simultaneously assessing other potential contributing factors. Using Integrated Nested Laplace Approximation (INLA) multi-matrix animal models, we demonstrate that social network positions are shaped by two-dimensional landscape locations, pairwise space sharing, individual range size, and spatial and temporal variation in population density, alongside smaller but detectable impacts of a selection of individual-level phenotypic traits. These results indicate strong, multifaceted spatiotemporal structuring in this society, emphasising the importance of considering multiple spatial components when investigating the causes and consequences of sociality.

Analysis of node2vec random walks on networks

Random walks have been proven to be useful for constructing various algorithms to gain information on networks. Algorithm node2vec employs biased random walks to realize embeddings of nodes into low-dimensional spaces, which can then be used for tasks such as multi-label classification and link prediction. The performance of the node2vec algorithm in these applications is considered to depend on properties of random walks that the algorithm uses. In the present study, we theoretically and numerically analyse random walks used by the node2vec. Those random walks are second-order Markov chains. We exploit the mapping of its transition rule to a transition probability matrix among directed edges to analyse the stationary probability, relaxation times in terms of the spectral gap of the transition probability matrix, and coalescence time. In particular, we show that node2vec random walk accelerates diffusion when walkers are designed to avoid both backtracking and visiting a neighbour of the previously visited node but do not avoid them completely.

A mechanistic, stigmergy model of territory formation in solitary animals: Territorial behavior can dampen disease prevalence but increase persistence

Although movement ecology has leveraged models of home range formation to explore the effects of spatial heterogeneity and social cues on movement behavior, disease ecology has yet to integrate these potential drivers and mechanisms of contact behavior into a generalizable disease modeling framework. Here we ask how dynamic territory formation and maintenance might contribute to disease dynamics in a territorial, solitary predator for an indirectly transmitted pathogen. We developed a mechanistic individual-based model where stigmergy—the deposition of signals into the environment (e.g., scent marking, scraping)—dictates local movement choices and long-term territory formation, but also the risk of pathogen transmission. Based on a variable importance analysis, the length of the infectious period was the single most important variable in predicting outbreak success, maximum prevalence, and outbreak duration. Host density and rate of pathogen decay were also key predictors. We found that territoriality best reduced maximum prevalence in conditions where we would otherwise expect outbreaks to be most successful: slower recovery rates (i.e., longer infectious periods) and higher conspecific densities. However, for slower pathogen decay rates, stigmergy-driven movement increased outbreak durations relative to random movement simulations. Our findings therefore support a limited version of the “territoriality benefits” hypothesis—where reduced home range overlap leads to reduced opportunities for pathogen transmission, but with the caveat that reduction in outbreak severity may increase the likelihood of pathogen persistence. For longer infectious periods and higher host densities, key trade-offs emerged between the strength of pathogen load, the strength of the stigmergy cue, and the rate at which those two quantities decayed; this finding raises interesting questions about the evolutionary nature of these competing processes and the role of possible feedbacks between parasitism and territoriality. This work also highlights the importance of considering social cues as part of the movement landscape in order to better understand the consequences of individual behaviors on population level outcomes.

Making decisions about conservation and disease management relies on our understanding of what allows animal populations to be successful, which often depends on when and where animals encounter each other. However, disease ecology often focuses on the social behavior of animals without accounting for their individual movement patterns. We developed a simulation model that bridges the fields of disease and movement ecology by allowing hosts to inform their movement based on the past movements of other hosts. As hosts navigate their environment, they leave behind a scent trail while avoiding the scent trails of other individuals. We wanted to know if this means of territory formation could heighten or dampen disease spread when infectious hosts leave pathogens in their wake. We found that territoriality can inhibit disease spread under conditions that we would normally expect pathogens to be most successful: when there are many hosts on the landscape and hosts stay infectious for longer. This work points to how incorporating movement behavior into disease models can provide improved understanding of how diseases spread in wildlife populations; such understanding is particularly important in the face of combatting ongoing and emerging infectious diseases.

Accounting for space and uncertainty in real-time location system-derived contact networks

Point data obtained from real-time location systems (RTLSs) can be processed into animal contact networks, describing instances of interaction between tracked individuals. Proximity-based definitions of interanimal "contact," however, may be inadequate for describing epidemiologically and sociologically relevant interactions involving body parts or other physical spaces relatively far from tracking devices. This weakness can be overcome by using polygons, rather than points, to represent tracked individuals and defining "contact" as polygon intersections.We present novel procedures for deriving polygons from RTLS point data while maintaining distances and orientations associated with individuals' relocation events. We demonstrate the versatility of this methodology for network modeling using two contact network creation examples, wherein we use this procedure to create (a) interanimal physical contact networks and (b) a visual contact network. Additionally, in creating our networks, we establish another procedure to adjust definitions of "contact" to account for RTLS positional accuracy, ensuring all true contacts are likely captured and represented in our networks.Using the methods described herein and the associated R package we have developed, called contact, researchers can derive polygons from RTLS points. Furthermore, we show that these polygons are highly versatile for contact network creation and can be used to answer a wide variety of epidemiological, ethological, and sociological research questions.By introducing these methodologies and providing the means to easily apply them through the contact R package, we hope to vastly improve network-model realism and researchers' ability to draw inferences from RTLS data.

Dispersal patterns in a medium-density Irish badger population: Implications for understanding the dynamics of tuberculosis transmission

European badgers (Meles meles) are group-living mustelids implicated in the spread of bovine tuberculosis (TB) to cattle and act as a wildlife reservoir for the disease. In badgers, only a minority of individuals disperse from their natal social group. However, dispersal may be extremely important for the spread of TB, as dispersers could act as hubs for disease transmission. We monitored a population of 139 wild badgers over 7 years in a medium-density population (1.8 individuals/km2). GPS tracking collars were applied to 80 different individuals. Of these, we identified 25 dispersers, 14 of which were wearing collars as they dispersed. This allowed us to record the process of dispersal in much greater detail than ever before. We show that dispersal is an extremely complex process, and measurements of straight-line distance between old and new social groups can severely underestimate how far dispersers travel. Assumptions of straight-line travel can also underestimate direct and indirect interactions and the potential for disease transmission. For example, one female disperser which eventually settled 1.5 km from her natal territory traveled 308 km and passed through 22 different territories during dispersal. Knowledge of badgers' ranging behavior during dispersal is crucial to understanding the dynamics of TB transmission, and for designing appropriate interventions, such as vaccination.

The intrinsic vulnerability of networks to epidemics

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We searched for properties making a network intrinsically vulnerable to epidemics.

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We conducted simulations on both modelled and real-world contact networks.

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Network properties may affect outbreak magnitude more than pathogen features.

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We show how structural properties can be used to infer relative network vulnerability.

Contact networks are convenient models to investigate epidemics, with nodes and links representing potential hosts and infection pathways, respectively. The outcomes of outbreak simulations on networks are driven both by the underlying epidemic model, and by the networks’ structural properties, so that the same pathogen can generate different epidemic dynamics on different networks. Here we ask whether there are general properties that make a contact network intrinsically vulnerable to epidemics (that is, regardless of specific epidemiological parameters). By conducting simulations on a large set of modelled networks, we show that, when a broad range of network topologies is taken into account, the effect of specific network properties on outbreak magnitude is stronger than that of fundamental pathogen features such as transmission rate, infection duration, and immunization ability. Then, by focusing on a large set of real world networks of the same type (potential contacts between field voles, Microtus agrestis), we showed how network structure can be used to accurately assess the relative, intrinsic vulnerability of networks towards a specific pathogen, even when those have limited topological variability. These results have profound implications for how we prevent disease outbreaks; in many real world situations, the topology of host contact networks can be described and used to infer intrinsic vulnerability. Such an approach can increase preparedness and inform preventive measures against emerging diseases for which limited epidemiological information is available, enabling the identification of priority targets before an epidemic event.

Going through the motions: incorporating movement analyses into disease research

Though epidemiology dates back to the 1700s, most mathematical representations of epidemics still use transmission rates averaged at the population scale, especially for wildlife diseases. In simplifying the contact process, we ignore the heterogeneities in host movements that complicate the real world, and overlook their impact on spatiotemporal patterns of disease burden. Movement ecology offers a set of tools that help unpack the transmission process, letting researchers more accurately model how animals within a population interact and spread pathogens. Analytical techniques from this growing field can also help expose the reverse process: how infection impacts movement behaviours, and therefore other ecological processes like feeding, reproduction, and dispersal. Here, we synthesise the contributions of movement ecology in disease research, with a particular focus on studies that have successfully used movement-based methods to quantify individual heterogeneity in exposure and transmission risk. Throughout, we highlight the rapid growth of both disease and movement ecology and comment on promising but unexplored avenues for research at their overlap. Ultimately, we suggest, including movement empowers ecologists to pose new questions, expanding our understanding of host-pathogen dynamics and improving our predictive capacity for wildlife and even human diseases.

Super-ranging. A new ranging strategy in European badgers

We monitored the ranging of a wild European badger (Meles meles) population over 7 years using GPS tracking collars. Badger range sizes varied seasonally and reached their maximum in June, July and August. We analysed the summer ranging behaviour, using 83 home range estimates from 48 individuals over 6974 collar-nights. We found that while most adult badgers (males and females) remained within their own traditional social group boundaries, several male badgers (on average 22%) regularly ranged beyond these traditional boundaries. These adult males frequently ranged throughout two (or more) social group’s traditional territories and had extremely large home ranges. We therefore refer to them as super-rangers. While ranging across traditional boundaries has been recorded over short periods of time for extraterritorial mating and foraging forays, or for pre-dispersal exploration, the animals in this study maintained their super-ranges from 2 to 36 months. This study represents the first time such long-term extra-territorial ranging has been described for European badgers. Holding a super-range may confer an advantage in access to breeding females, but could also affect local interaction networks. In Ireland & the UK, badgers act as a wildlife reservoir for bovine tuberculosis (TB). Super-ranging may facilitate the spread of disease by increasing both direct interactions between conspecifics, particularly across social groups, and indirect interactions with cattle in their shared environment. Understanding super-ranging behaviour may both improve our understanding of tuberculosis epidemiology and inform future control strategies.

Disease implications of animal social network structure: A synthesis across social systems

The disease costs of sociality have largely been understood through the link between group size and transmission. However, infectious disease spread is driven primarily by the social organization of interactions in a group and not its size. We used statistical models to review the social network organization of 47 species, including mammals, birds, reptiles, fish and insects by categorizing each species into one of three social systems, relatively solitary, gregarious and socially hierarchical. Additionally, using computational experiments of infection spread, we determined the disease costs of each social system. We find that relatively solitary species have large variation in number of social partners, that socially hierarchical species are the least clustered in their interactions, and that social networks of gregarious species tend to be the most fragmented. However, these structural differences are primarily driven by weak connections, which suggest that different social systems have evolved unique strategies to organize weak ties. Our synthetic disease experiments reveal that social network organization can mitigate the disease costs of group living for socially hierarchical species when the pathogen is highly transmissible. In contrast, highly transmissible pathogens cause frequent and prolonged epidemic outbreaks in gregarious species. We evaluate the implications of network organization across social systems despite methodological challenges, and our findings offer new perspective on the debate about the disease costs of group living. Additionally, our study demonstrates the potential of meta-analytic methods in social network analysis to test ecological and evolutionary hypotheses on cooperation, group living, communication and resilience to extrinsic pressures.

Dynamic, spatial models of parasite transmission in wildlife: Their structure, applications and remaining challenges

Individual differences in contact rate can arise from host, group and landscape heterogeneity and can result in different patterns of spatial spread for diseases in wildlife populations with concomitant implications for disease control in wildlife of conservation concern, livestock and humans. While dynamic disease models can provide a better understanding of the drivers of spatial spread, the effects of landscape heterogeneity have only been modelled in a few well-studied wildlife systems such as rabies and bovine tuberculosis. Such spatial models tend to be either purely theoretical with intrinsic limiting assumptions or individual-based models that are often highly species- and system-specific, limiting the breadth of their utility. Our goal was to review studies that have utilized dynamic, spatial models to answer questions about pathogen transmission in wildlife and identify key gaps in the literature. We begin by providing an overview of the main types of dynamic, spatial models (e.g., metapopulation, network, lattice, cellular automata, individual-based and continuous-space) and their relation to each other. We investigate different types of ecological questions that these models have been used to explore: pathogen invasion dynamics and range expansion, spatial heterogeneity and pathogen persistence, the implications of management and intervention strategies and the role of evolution in host-pathogen dynamics. We reviewed 168 studies that consider pathogen transmission in free-ranging wildlife and classify them by the model type employed, the focal host-pathogen system, and their overall research themes and motivation. We observed a significant focus on mammalian hosts, a few well-studied or purely theoretical pathogen systems, and a lack of studies occurring at the wildlife-public health or wildlife-livestock interfaces. Finally, we discuss challenges and future directions in the context of unprecedented human-mediated environmental change. Spatial models may provide new insights into understanding, for example, how global warming and habitat disturbance contribute to disease maintenance and emergence. Moving forward, better integration of dynamic, spatial disease models with approaches from movement ecology, landscape genetics/genomics and ecoimmunology may provide new avenues for investigation and aid in the control of zoonotic and emerging infectious diseases.

A cross-validation-based approach for delimiting reliable home range estimates

BACKGROUND

With decreasing costs of GPS telemetry devices, data repositories of animal movement paths are increasing almost exponentially in size. A series of complex statistical tools have been developed in conjunction with this increase in data. Each of these methods offers certain improvements over previously proposed methods, but each has certain assumptions or shortcomings that make its general application difficult. In the case of the recently developed Time Local Convex Hull (T-LoCoH) method, the subjectivity in parameter selection serves as one of the primary impediments to its more widespread use. While there are certain advantages to the flexibility it offers for question-driven research, the lack of an objective approach for parameter selection may prevent some users from exploring the benefits of the method.

METHODS

Here we present a cross-validation-based approach for selecting parameter values to optimize the T-LoCoH algorithm. We demonstrate the utility of the approach using a case study from the Etosha National Park anthrax system.

RESULTS

Utilizing the proposed algorithm, rather than the guidelines in the T-LoCoH documentation, results in significantly different values for derived site fidelity metrics.

CONCLUSIONS

Due to its basis in principles of cross-validation, the application of this method offers a more objective approach than the relatively subjective guidelines set forth in the T-LoCoH documentation and enables a more accurate basis for the comparison of home ranges among individuals and species, as well as among studies.

Revealing the hidden structure of dynamic ecological networks

In ecology, recent technological advances and long-term data studies now provide longitudinal interaction data (e.g. between individuals or species). Most often, time is the parameter along which interactions evolve but any other one-dimensional gradient (temperature, altitude, depth, humidity, etc.) can be considered. These data can be modelled through a sequence of different snapshots of an evolving ecological network, i.e. a dynamic network. Here, we present how the dynamic stochastic block model approach developed by Matias & Miele (Matias & Miele In press J. R. Stat. Soc. B (doi:10.1111/rssb.12200)) can capture the complexity and dynamics of these networks. First, we analyse a dynamic contact network of ants and we observe a clear high-level assembly with some variations in time at the individual level. Second, we explore the structure of a food web evolving during a year and we detect a stable predator-prey organization but also seasonal differences in the prey assemblage. Our approach, based on a rigorous statistical method implemented in the R package dynsbm, can pave the way for exploration of evolving ecological networks.

Unraveling the disease consequences and mechanisms of modular structure in animal social networks

Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.

PATTERNS OF MYCOBACTERIUM LEPRAE INFECTION IN WILD NINE-BANDED ARMADILLOS (DASYPUS NOVEMCINCTUS) IN MISSISSIPPI, USA

The nine-banded armadillo ( Dasypus novemcinctus ) is the only known nonhuman reservoir of Mycobacterium leprae , the causative agent of Hansen's disease or leprosy. We conducted a 6-yr study on a wild population of armadillos in western Mississippi that was exposed to M. leprae to evaluate the importance of demographic and spatial risk factors on individual antibody status. We found that spatially derived covariates were not predictive of antibody status. Furthermore, analyses revealed no evidence of clustering by antibody-positive individuals. Lactating females and adult males had higher odds of being antibody positive than did nonlactating females. No juveniles or yearlings were antibody positive. Results of these analyses support the hypothesis that M. leprae infection patterns are spatially homogeneous within this armadillo population. Further research related to movement patterns, contact among individuals, antibody status, and environmental factors could help address hypotheses related to the role of environmental transmission on M. leprae infection and the mechanisms underlying the differential infection patterns among demographic groups.

Why infectious disease research needs community ecology

Infectious diseases often emerge from interactions among multiple species and across nested levels of biological organization. Threats as diverse as Ebola virus, human malaria, and bat white-nose syndrome illustrate the need for a mechanistic understanding of the ecological interactions underlying emerging infections. We describe how recent advances in community ecology can be adopted to address contemporary challenges in disease research. These analytical tools can identify the factors governing complex assemblages of multiple hosts, parasites, and vectors, and reveal how processes link across scales from individual hosts to regions. They can also determine the drivers of heterogeneities among individuals, species, and regions to aid targeting of control strategies. We provide examples where these principles have enhanced disease management and illustrate how they can be further extended.

Models for managing wildlife disease

Modelling wildlife disease poses some unique challenges. Wildlife disease systems are data poor in comparison with human or livestock disease systems, and the impact of disease on population size is often the key question of interest. This review concentrates specifically on the application of dynamic models to evaluate and guide management strategies. Models have proved useful particularly in two areas. They have been widely used to evaluate vaccination strategies, both for protecting endangered species and for preventing spillover from wildlife to humans or livestock. They have also been extensively used to evaluate culling strategies, again both for diseases in species of conservation interest and to prevent spillover. In addition, models are important to evaluate the potential of parasites and pathogens as biological control agents. The review concludes by identifying some key research gaps, which are further development of models of macroparasites, deciding on appropriate levels of complexity, modelling genetic management and connecting models to data.