The dynamics of transmission and the dynamics of networks

The contribution of movement to social network structure and spreading dynamics under simple and complex transmission

The structure of social networks fundamentally influences spreading dynamics. In general, the more contact between individuals, the more opportunity there is for the transmission of information or disease to take place. Yet, contact between individuals, and any resulting transmission events, are determined by a combination of spatial (where individuals choose to move) and social rules (who they choose to interact with or learn from). Here, we examine the effect of the social-spatial interface on spreading dynamics using a simulation model. We quantify the relative effects of different movement rules (localized, semi-localized, nomadic and resource-based movement) and social transmission rules (simple transmission, anti-conformity, proportional, conformity and threshold rules) to both the structure of social networks and spread of a novel behaviour. Localized movement created weakly connected sparse networks, nomadic movement created weakly connected dense networks, and resource-based movement generated strongly connected modular networks. The resulting rate of spreading varied with different combinations of movement and transmission rules, but-importantly-the relative rankings of transmission rules changed when running simulations on static versus dynamic representations of networks. Our results emphasize that individual-level social and spatial behaviours influence emergent network structure, and are of particular consequence for the spread of information under complex transmission rules.This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

Social information use shapes the coevolution of sociality and virulence

Social contacts can facilitate the spread of both survival-related information and infectious diseases, but little is known about how these processes combine to shape host and parasite evolution. Here, we use a theoretical model that captures both infection and information transmission processes to investigate how host sociality (contact effort) and parasite virulence (disease-associated mortality rate) (co)evolve. We show that selection for sociality (and in turn, virulence) depends on both the intrinsic costs and benefits of social information and infection as well as their relative prevalence in the population. Specifically, greater sociality and lower virulence evolve when the risk of infection is either low or high and social information is neither very common nor too rare. Lower sociality and higher virulence evolve when the prevalence patterns are reversed. When infection and social information are both at moderate levels in the population, the direction of selection depends on the relative costs and benefits of being infected or informed. We also show that sociality varies inversely with virulence, and that parasites may be unable to prevent runaway selection for higher contact efforts. Together, these findings provide new insights for our understanding of group living and how apparently opposing ecological processes can influence the evolution of sociality and virulence in a range of ways.

Analysis of English Cultural Teaching Model Based on Machine Learning

According to the world population, nearly five billion people use mobile phones in their daily lives, and this has increased by 20% in the last twelve months compared to the previous report. An average survey conducted by researchers to find the amount of data consumed in a month by every mobile phone in the world has finally resulted in 45 exabytes of data being collected from a single user within a month. In today's world, data consumption and data analytics are being considered as one of the most important necessities for e-commerce companies. With the help of such collected data from a person, it is possible to predict the future signature or activity of the person. If 45 terabytes of data can be stored for a single user, determining the average calculation and amount of data to be collected for five billion users appears to be much more difficult. More than the human working concept, it looks like it would be difficult for a traditional computer system to handle this amount of data. To study and understand a concept from machine learning and artificial intelligence requires quite a collection of data to predict according to a person's activity. This article explains the roles of faculty and students, as well as the requirements for academic evaluation. Even before the pandemic, most people did not have any idea about the online teaching model. It is only after the disability of conducting direct (offline) classes that people are forced to get into the online world of teaching. Nearly 60% of countries are trying to convert their education systems to such online models, which improve communication between students and teachers and also enable different schemes for students. Big data can be considered as one of the technological revolutions in information technology companies that became popular after the crisis of cloud computing. A support vector machine (SVM) is proposed for analyzing English culture teaching and is compared with the traditional fuzzy logic. The results show the proposed model achieves an accuracy of 98%, which is 5% higher than the existing algorithm.

Temporal Graphs and Temporal Network Characteristics for Bio-Inspired Networks during Optimization

Temporal network analysis and time evolution of network characteristics are powerful tools in describing the changing topology of dynamic networks. This paper uses such approaches to better visualize and provide analytical measures for the changes in performance that we observed in Voronoi-type spatial coverage, particularly for the example of time-evolving networks with a changing number of wireless sensors being deployed. Specifically, our analysis focuses on the role different combinations of impenetrable obstacles and environmental noise play in connectivity and overall network structure. It is shown how the use of (i) temporal network graphs, and (ii) network centrality and regularity measures illustrate the differences between various options developed for the balancing act of energy and time efficiency in network coverage. Last, we compare the outcome of these measures with the less abstract classification variables, such as percent area covered and cumulative distance traveled.

Cooperative breeding and the emergence of multilevel societies in birds

Multilevel societies (MLSs), where social levels are hierarchically nested within each other, are considered one of the most complex forms of animal societies. Although thought to mainly occurs in mammals, it is suggested that MLSs could be under-detected in birds. Here, we propose that the emergence of MLSs could be common in cooperatively breeding birds, as both systems are favoured by similar ecological and social drivers. We first investigate this proposition by systematically comparing evidence for multilevel social structure in cooperative and non-cooperative birds in Australia and New Zealand, a global hotspot for cooperative breeding. We then analyse non-breeding social networks of cooperatively breeding superb fairy-wrens (Malurus cyaneus) to reveal their structured multilevel society, with three hierarchical social levels that are stable across years. Our results confirm recent predictions that MLSs are likely to be widespread in birds and suggest that these societies could be particularly common in cooperatively breeding birds.

Revealing mechanisms of infectious disease spread through empirical contact networks

The spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling the dynamics of infectious disease spread through contact networks, however, can be challenging due to limited knowledge of how an infectious disease spreads and its transmission rate. We developed a novel statistical tool, INoDS (Identifying contact Networks of infectious Disease Spread) that estimates the transmission rate of an infectious disease outbreak, establishes epidemiological relevance of a contact network in explaining the observed pattern of infectious disease spread and enables model comparison between different contact network hypotheses. We show that our tool is robust to incomplete data and can be easily applied to datasets where infection timings of individuals are unknown. We tested the reliability of INoDS using simulation experiments of disease spread on a synthetic contact network and find that it is robust to incomplete data and is reliable under different settings of network dynamics and disease contagiousness compared with previous approaches. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumblebee colonies and Salmonella in wild Australian sleepy lizard populations. INoDS thus provides a novel and reliable statistical tool for identifying transmission pathways of infectious disease spread. In addition, application of INoDS extends to understanding the spread of novel or emerging infectious disease, an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.

Network models are widely used to understand and predict infectious disease spread in human and animal populations. However, the choice of network model often relies on subjective expert knowledge or disease transmission experiments that are time-consuming and difficult to perform. We developed a novel tool, called INoDS (Identifying contact Networks of infectious Disease Spread), that uses robust statistical approach to establish relevance of a network model in explaining transmission pathways of an infectious disease outbreak. We used computer simulations and real-world dataset to test the accuracy of our tool and robustness to missing data. We found that INoDS is robust to common data-collection constraints, broadly applicable and accurate compared to current approaches. The tool that we have developed can therefore provide immediate epidemiological insights in the event of an epidemic outbreak, and can be used to improve targeted disease control.

Social Network Predicts Exposure to Respiratory Infection in a Wild Chimpanzee Group

Respiratory pathogens are expected to spread through social contacts, but outbreaks often occur quickly and unpredictably, making it challenging to simultaneously record social contact and disease incidence data, especially in wildlife. Thus, the role of social contacts in the spread of infectious disease is often treated as an assumption in disease simulation studies, and few studies have empirically demonstrated how pathogens spread through social networks. In July-August 2015, an outbreak of respiratory disease was observed in a wild chimpanzee community in Kibale National Park, Uganda, during an ongoing behavioral study of male chimpanzees, offering a rare opportunity to evaluate how social behavior affects individual exposure to socially transmissible diseases. From May to August 2015, we identified adult and adolescent male chimpanzees displaying coughs and rhinorrhea and recorded 5-m proximity data on males (N = 40). Using the network k-test, we found significant relationships between male network connectivity and the distribution of cases within the network, supporting the importance of short-distance contacts for the spread of the respiratory outbreak. Additionally, chimpanzees central to the network were more likely to display clinical signs than those with fewer connections. Although our analyses were limited to male chimpanzees, these findings underscore the value of social connectivity data in predicting disease outcomes and elucidate a potential evolutionary cost of being social.

Tracking sickness effects on social encounters via continuous proximity sensing in wild vampire bats

Sickness behaviors can slow the spread of pathogens across a social network. We conducted a field experiment to investigate how sickness behavior affects individual connectedness over time using a dynamic social network created from high-resolution proximity data. After capturing adult female vampire bats (Desmodus rotundus) from a roost, we created “sick” bats by injecting a random half of bats with the immune-challenging substance, lipopolysaccharide, while the control group received saline injections. Over the next 3 days, we used proximity sensors to continuously track dyadic associations between 16 “sick” bats and 15 control bats under natural conditions. Compared to control bats, “sick” bats associated with fewer bats, spent less time near others, and were less socially connected to more well-connected individuals (sick bats had on average a lower degree, strength, and eigenvector centrality). High-resolution proximity data allow researchers to flexibly define network connections (association rates) based on how a particular pathogen is transmitted (e.g., contact duration of >1 vs. >60 min, contact proximity of <1 vs. <10 m). Therefore, we inspected how different ways of measuring association rates changed the observed effect of LPS. How researchers define association rates influences the magnitude and detectability of sickness effects on network centrality. When animals are sick, they often encounter fewer individuals. We tracked this unintentional “social distancing” effect hour-by-hour in a wild colony of vampire bats. Using bat-borne proximity sensors, we compared changes in the social network connectedness of immune-challenged “sick” bats versus “control” bats over time. “Sick” bats had fewer encounters with others and spent less time near others. Associations changed dramatically by time of day, and different measures of association influenced the sickness effect estimates.

Dynamic Network of Interactions in the Wildlife-Livestock Interface in Mediterranean Spain: An Epidemiological Point of View

The correct management of diseases that are transmitted between wildlife and livestock requires a reliable estimate of the pathogen transmission rate. The calculation of this parameter is a challenge for epidemiologists, since transmission can occur through multiple pathways. The social network analysis is a widely used tool in epidemiology due to its capacity to identify individuals and communities with relevant roles for pathogen transmission. In the present work, we studied the dynamic network of interactions in a complex epidemiological scenario using information from different methodologies. In 2015, nine red deer, seven fallow deer, six wild boar and nine cattle were simultaneously monitored using GPS-GSM-Proximity collars in Doñana National Park. In addition, 16 proximity loggers were set in aggregation points. Using the social network analysis, we studied the dynamic network of interactions, including direct and indirect interactions, between individuals of different species and the potential transmission of pathogens within this network. The results show a high connection between species through indirect interactions, with a marked seasonality in the conformation of new interactions. Within the network, we differentiated four communities that included individuals of all the species. Regarding the transmission of pathogens, we observed the important role that fallow deer could be playing in the maintenance and transmission of pathogens to livestock. The present work shows the need to consider different types of methodologies in order to understand the complete functioning of the network of interactions at the wildlife/livestock interface. It also provides a methodological approach applicable to the management of shared diseases.

Transmission risk predicts avoidance of infected conspecifics in Trinidadian guppies

Associating with conspecifics afflicted with infectious diseases increases the risk of becoming infected, but engaging in avoidance behaviour incurs the cost of lost social benefits. Across systems, infected individuals vary in the transmission risk they pose, so natural selection should favour risk-sensitive avoidance behaviour that optimally balances the costs and benefits of sociality. Here, we use the guppy Poecilia reticulata-Gyrodactylus turnbulli host-parasite system to test the prediction that individuals avoid infected conspecifics in proportion to the transmission risk they pose. In dichotomous choice tests, uninfected fish avoided both the chemical and visual cues, presented separately, of infected conspecifics only in the later stages of infection. A transmission experiment indicated that this avoidance behaviour accurately tracked transmission risk (quantified as both the speed at which transmission occurs and the number of parasites transmitting) through the course of infection. Together, these findings reveal that uninfected hosts can use redundant cues across sensory systems to inform dynamic risk-sensitive avoidance behaviour. This correlation between the transmission risk posed by infected individuals and the avoidance response they elicit has implications for the evolutionary ecology of infectious disease, and its explicit inclusion may improve the ability of epidemic models to predict disease spread.

Advances from the nexus of animal behaviour and pathogen transmission: new directions and opportunities using contact networks

Contact network models have enabled significant advances in understanding the influence of behaviour on parasite and pathogen transmission. They are an important tool that links variation in individual behaviour, to epidemiological consequences at the population level. Here, in our introduction to this special issue, we highlight the importance of applying network approaches to disease ecological and epidemiological questions, and how this has provided a much deeper understanding of these research areas. Recent advances in tracking host behaviour (bio-logging: e.g., GPS tracking, barcoding) and tracking pathogens (high-resolution sequencing), as well as methodological advances (multi-layer networks, computational techniques) started producing exciting new insights into disease transmission through contact networks. We discuss some of the exciting directions that the field is taking, some of the challenges, and importantly the opportunities that lie ahead. For instance, we suggest to integrate multiple transmission pathways, multiple pathogens, and in some systems, multiple host species, into the next generation of network models. Corresponding opportunities exist in utilising molecular techniques, such as high-resolution sequencing, to establish causality in network connectivity and disease outcomes. Such novel developments and the continued integration of network tools offers a more complete understanding of pathogen transmission processes, their underlying mechanisms and their evolutionary consequences.