Infectious disease spread is a fundamental process that takes place through host contact networks. Constructing network models of disease spread, however, requires knowledge of the transmission mode of a pathogen. While it has been possible to characterize contact networks of several human infectious diseases based on our understanding of how pathogens transmit (e.g., sexual contacts for HIV, physical proximity for measles), limited knowledge about how pathogens transmits and high costs of data collection makes network modeling of infectious diseases in several other systems particularly difficult.
We have developed a new tool, INoDS, that utilizes Bayesian inference to infer contact networks of disease transmission in human or animal populations, as well as disease transmission higher spatial scales. INoDS (Inferring Networks of infectious Disease Spread) uses time-stamped disease and network data to provide evidence supporting contact networks that represent competing hypotheses about transmission modes. We have evaluated this tool on synthetic network disease data, and shown that INoDS accurately identifies the underlying contact network even when the networks are partially sampled and information on disease spread is incomplete. We are currently using the tool to explain transmission mechanisms of infectious disease spread in three real animal populations (bumble bees, Australian sleepy lizards the desert tortoises). INoDS therefore provides a novel network inference tool which extends the potential of network modelling in disease ecology and provides insights towards biological mechanisms of pathogen spread which would take years to resolve through traditional laboratory techniques.
Past studies examining the disease costs of sociality have generally explored hypotheses that link larger group size to higher rates of infection transmission. However, beyond a simple dependence on group size, infection spread is largely influenced by the organization of infectionspreading interactions between individuals. Network analysis tools have allowed for rapid advances in our understanding of the disease consequences of sociality at an individual scale, but studies on species level sociality are still lacking. Here we conduct a comparative analysis of 666 interaction networks across 47 species to investigate the relationship between network complexity and the costs of disease transmission for four social systems – solitary, fissionfusion, social and socially hierarchical. Specifically, we use phylogeneticallycontrolled Bayesian MCMC modelling and insilico disease simulations to identify the relative costs of disease transmission for each social system as mediated by their network structure.
We find that solitary, fissionfusion, and higher social organizations can be distinguished from each other based on (a) degree of variation among social partners, (b) the extent to which the interaction network is fragmented and (c) the proportion of individuals that occupy socially central positions within the interaction networks. In particular, individuals of solitary species demonstrate the highest variation in the number of social partners, while the interaction networks of fissionfusion species are the most fragmented. The results of disease simulations show that the structure of interaction networks can alleviate the disease costs of group living for social, but not socially hierarchical species. Our findings, therefore, offer new perspectives on the debate about the disease costs of group living by evaluating how social organization strategies mediate pathogen pressures
Modular organization in animal social networks is hypothesized to alleviate the cost of disease burden in group living species. However, our analysis of empirical social networks of 43 animal species along with theoretical networks demonstrates that infectious disease spread is largely unaffected by the underlying modular organi- zation except when social networks are extremely subdivided. We show that high fragmentation and high subgroup cohesion, which are both associated with high modularity in social networks, induce structural delay and trapping of infections that spread through these networks, reducing disease burden. We validate our results using real animal social networks, and recommend the use of appropriate null network models when data-limited estimates of epidemic consequences are necessary.
Networks are mathematical representations of the interactions between different components of a system. In network analysis, the interacting components are represented as nodes (also called vertices) and their interactions are represented as edges. It is now well known that most of the networks around us including metabolic networks, neural networks, protein-protein interaction networks and even social networks are not random; some nodes are more closely associated with each other than with the rest of the networks, forming communities or modules in the network. Even though this property is known to be an almost universal structural feature, little is known about its functional role in a network.
In this study we develop a model that generates simple modular random graphs with tunable strength of community structure. The generated graphs are random with respect to other properties of the network, which makes these graphs an important tool to tease apart the role of community structure in complex real-world networks.
Effect of environmental, demographic drivers and the role of stressors on the social structure of the desert tortoise
Adaptive and social behavior that affects fitness is now being increasingly incorporated in the conservation and management of wildlife species. However, direct observations of social interactions in species considered to be solitary are difficult, and therefore integration of behavior in conservation and management decisions in such species has been infrequent. For such species, we propose quantifying refuge use behavior as it can provide insights towards their (hidden) social structure, establish relevant contact patterns of infectious disease spread, and provide early warning signals of population stressors. Our study highlights this approach in a long-lived and threatened species, the desert tortoise. We provide evidence toward the presence of and identify mechanisms behind the social structure in desert tortoises formed by their burrow use preferences. We also show how individuals burrow use behavior responds to the presence of population stressors.
Stabilizing the dynamics of biological populations and metapopulations has been a popular area of among population biologists, conservation biologists and non-linear dynamists. Out of the several theoretical models proposed, few have been empirically tested but have been of limited success. We proposed a novel control strategy called Adaptive Limiter Control (ALC), which reduces both fluctuation in population size as well as extinction probability of (meta)populations. We used this control strategy to successfully stabilize unstable laboratory populations and metapopulations of fruit flies, Drosophila melanogaster, making it the first control method that has been empirically shown to work for biological metapopulations.