scholarly journals Incorporating genomic methods into contact networks to reveal new insights into animal behaviour and infectious disease dynamics

Behaviour ◽  
2018 ◽  
Vol 155 (7-9) ◽  
pp. 759-791 ◽  
Author(s):  
Marie L.J. Gilbertson ◽  
Nicholas M. Fountain-Jones ◽  
Meggan E. Craft
Keyword(s):  
Infectious Disease ◽  
Disease Ecology ◽  
Pathogen Transmission ◽  
Contact Network ◽  
Contact Networks ◽  
Host Behaviour ◽  
Infectious Disease Dynamics ◽  
Insight Into ◽  
Dynamic Interplay ◽  
Broad Overview

Abstract Utilization of contact networks has provided opportunities for assessing the dynamic interplay between pathogen transmission and host behaviour. Genomic techniques have, in their own right, provided new insight into complex questions in disease ecology, and the increasing accessibility of genomic approaches means more researchers may seek out these tools. The integration of network and genomic approaches provides opportunities to examine the interaction between behaviour and pathogen transmission in new ways and with greater resolution. While a number of studies have begun to incorporate both contact network and genomic approaches, a great deal of work has yet to be done to better integrate these techniques. In this review, we give a broad overview of how network and genomic approaches have each been used to address questions regarding the interaction of social behaviour and infectious disease, and then discuss current work and future horizons for the merging of these techniques.

scholarly journals Revealing mechanisms of infectious disease spread through empirical contact networks

2021 ◽  
Vol 17 (12) ◽  
pp. e1009604
Author(s):  
Pratha Sah ◽  
Michael Otterstatter ◽  
Stephan T. Leu ◽  
Sivan Leviyang ◽  
Shweta Bansal
Keyword(s):  
Infectious Disease ◽  
Incomplete Data ◽  
Model Comparison ◽  
Transmission Rate ◽  
Disease Spread ◽  
Contact Network ◽  
Statistical Tool ◽  
Contact Networks ◽  
Crithidia Bombi ◽  
Transmission Pathways

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.

scholarly journals Seasonality and pathogen transmission in pastoral cattle contact networks

2017 ◽  
Vol 4 (12) ◽  
pp. 170808 ◽  
Author(s):  
Kimberly VanderWaal ◽  
Marie Gilbertson ◽  
Sharon Okanga ◽  
Brian F. Allan ◽  
Meggan E. Craft
Keyword(s):  
Water Resources ◽  
Disease Outbreaks ◽  
Dry Season ◽  
Pathogen Transmission ◽  
Contact Network ◽  
Wet Season ◽  
Contact Networks ◽  
Contact Rates ◽  
Animal Populations ◽  
The Impact

Capturing heterogeneity in contact patterns in animal populations is essential for understanding the spread of infectious diseases. In contrast to other regions of the world in which livestock movement networks are integral to pathogen prevention and control policies, contact networks are understudied in pastoral regions of Africa due to the challenge of measuring contact among mobile herds of cattle whose movements are driven by access to resources. Furthermore, the extent to which seasonal changes in the distribution of water and resources impacts the structure of contact networks in cattle is uncertain. Contact networks may be more conducive to pathogen spread in the dry season due to congregation at limited water sources. Alternatively, less abundant forage may result in decreased pathogen transmission due to competitive avoidance among herds, as measured by reduced contact rates. Here, we use GPS technology to concurrently track 49 free-roaming cattle herds within a semi-arid region of Kenya, and use these data to characterize seasonal contact networks and model the spread of a highly infectious pathogen. This work provides the first empirical data on the local contact network structure of mobile herds based on quantifiable contact events. The contact network demonstrated high levels of interconnectivity. An increase in contacts near to water resources in the dry season resulted in networks with both higher contact rates and higher potential for pathogen spread than in the wet season. Simulated disease outbreaks were also larger in the dry season. Results support the hypothesis that limited water resources enhance connectivity and transmission within contact networks, as opposed to reducing connectivity as a result of competitive avoidance. These results cast light on the impact of seasonal heterogeneity in resource availability on predicting pathogen transmission dynamics, which has implications for other free-ranging wild and domestic populations.

scholarly journals Human mobility patterns predict divergent epidemic dynamics among cities

2013 ◽  
Vol 280 (1766) ◽  
pp. 20130763 ◽  
Author(s):  
Benjamin D. Dalziel ◽  
Babak Pourbohloul ◽  
Stephen P. Ellner
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Population Size ◽  
Human Mobility ◽  
Pathogen Transmission ◽  
Full Time ◽  
Disease Dynamics ◽  
Mobility Patterns ◽  
Epidemic Dynamics ◽  
Infectious Disease Dynamics

The epidemic dynamics of infectious diseases vary among cities, but it is unclear how this is caused by patterns of infectious contact among individuals. Here, we ask whether systematic differences in human mobility patterns are sufficient to cause inter-city variation in epidemic dynamics for infectious diseases spread by casual contact between hosts. We analyse census data on the mobility patterns of every full-time worker in 48 Canadian cities, finding a power-law relationship between population size and the level of organization in mobility patterns, where in larger cities, a greater fraction of workers travel to work in a few focal locations. Similarly sized cities also vary in the level of organization in their mobility patterns, equivalent on average to the variation expected from a 2.64-fold change in population size. Systematic variation in mobility patterns is sufficient to cause significant differences among cities in infectious disease dynamics—even among cities of the same size—according to an individual-based model of airborne pathogen transmission parametrized with the mobility data. This suggests that differences among cities in host contact patterns are sufficient to drive differences in infectious disease dynamics and provides a framework for testing the effects of host mobility patterns in city-level disease data.

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

Behaviour ◽  
2018 ◽  
Vol 155 (7-9) ◽  
pp. 567-583 ◽  
Author(s):  
Stephan T. Leu ◽  
Stephanie S. Godfrey
Keyword(s):  
High Resolution ◽  
Disease Transmission ◽  
Network Models ◽  
Population Level ◽  
Pathogen Transmission ◽  
Molecular Techniques ◽  
Gps Tracking ◽  
Contact Network ◽  
Computational Techniques ◽  
Contact Networks

Abstract 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.

scholarly journals Revealing mechanisms of infectious disease transmission through empirical contact networks

2017 ◽  
Author(s):  
Pratha Sah ◽  
Michael Otterstatter ◽  
Stephan T. Leu ◽  
Sivan Leviyang ◽  
Shweta Bansal
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Network Models ◽  
Pathogen Transmission ◽  
Disease Spread ◽  
Contact Networks ◽  
Transmission Mechanisms ◽  
Disease Information ◽  
Crithidia Bombi ◽  
Transmission Pathways

AbstractThe spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling infectious disease dynamics through contact networks is sometimes challenging, however, due to a limited understanding of pathogen transmission routes and infectivity. We developed a novel tool, INoDS (Identifying Network models of infectious Disease Spread) that estimates the predictive power of empirical contact networks to explain observed patterns of infectious disease spread. We show that our method is robust to partially sampled contact networks, incomplete disease information, and enables hypothesis testing on transmission mechanisms. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumble bee colonies and Salmonella in wild Australian sleepy lizard populations. The performance of INoDS in synthetic and complex empirical systems highlights its role in identifying transmission pathways of novel or neglected pathogens, as an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.

scholarly journals Inferring population-level contact heterogeneity from common epidemic data

2013 ◽  
Vol 10 (78) ◽  
pp. 20120578 ◽  
Author(s):  
J. Conrad Stack ◽  
Shweta Bansal ◽  
V. S. Anil Kumar ◽  
Bryan Grenfell
Keyword(s):  
Infectious Disease ◽  
Contact Structure ◽  
Population Level ◽  
Host Population ◽  
Pathogen Transmission ◽  
Disease Spread ◽  
Reproductive Number ◽  
Contact Network ◽  
Final Size ◽  
Ground Truth Data

Models of infectious disease spread that incorporate contact heterogeneity through contact networks are an important tool for epidemiologists studying disease dynamics and assessing intervention strategies. One of the challenges of contact network epidemiology has been the difficulty of collecting individual and population-level data needed to develop an accurate representation of the underlying host population's contact structure. In this study, we evaluate the utility of common epidemiological measures ( R 0 , epidemic peak size, duration and final size) for inferring the degree of heterogeneity in a population's unobserved contact structure through a Bayesian approach. We test the method using ground truth data and find that some of these epidemiological metrics are effective at classifying contact heterogeneity. The classification is also consistent across pathogen transmission probabilities, and so can be applied even when this characteristic is unknown. In particular, the reproductive number, R 0 , turns out to be a poor classifier of the degree heterogeneity, while, unexpectedly, final epidemic size is a powerful predictor of network structure across the range of heterogeneity. We also evaluate our framework on empirical epidemiological data from past and recent outbreaks to demonstrate its application in practice and to gather insights about the relevance of particular contact structures for both specific systems and general classes of infectious disease. We thus introduce a simple approach that can shed light on the unobserved connectivity of a host population given epidemic data. Our study has the potential to inform future data-collection efforts and study design by driving our understanding of germane epidemic measures, and highlights a general inferential approach to learning about host contact structure in contemporary or historic populations of humans and animals.

scholarly journals The ecology of movement and behaviour: a saturated tripartite network for describing animal contacts

2018 ◽  
Vol 285 (1887) ◽  
pp. 20180670 ◽  
Author(s):  
Kezia Manlove ◽  
Christina Aiello ◽  
Pratha Sah ◽  
Bree Cummins ◽  
Peter J. Hudson ◽  
...  
Keyword(s):  
Home Range ◽  
Information Transfer ◽  
Ad Hoc ◽  
Pathogen Transmission ◽  
Contact Network ◽  
Ecological Data ◽  
Patch Occupancy ◽  
Contact Networks ◽  
Movement Trajectories ◽  
Animal Contact

Ecologists regularly use animal contact networks to describe interactions underlying pathogen transmission, gene flow, and information transfer. However, empirical descriptions of contact often overlook some features of individual movement, and decisions about what kind of network to use in a particular setting are commonly ad hoc . Here, we relate individual movement trajectories to contact networks through a tripartite network model of individual, space, and time nodes. Most networks used in animal contact studies (e.g. individual association networks, home range overlap networks, and spatial networks) are simplifications of this tripartite model. The tripartite structure can incorporate a broad suite of alternative ecological metrics like home range sizes and patch occupancy patterns into inferences about contact network metrics such as modularity and degree distribution. We demonstrate the model's utility with two simulation studies using alternative forms of ecological data to constrain the tripartite network's structure and inform expectations about the harder-to-measure metrics related to contact.

scholarly journals Contact structure, mobility, environmental impact and behaviour: the importance of social forces to infectious disease dynamics and disease ecology

2017 ◽  
Vol 372 (1719) ◽  
pp. 20160454 ◽  
Author(s):  
Ronan F. Arthur ◽  
Emily S. Gurley ◽  
Henrik Salje ◽  
Laura S. P. Bloomfield ◽  
James H. Jones
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Social Factors ◽  
Disease Ecology ◽  
Contact Structure ◽  
Human Populations ◽  
Disease Dynamics ◽  
Social Ecological ◽  
The Social ◽  
Infectious Disease Dynamics

Human factors, including contact structure, movement, impact on the environment and patterns of behaviour, can have significant influence on the emergence of novel infectious diseases and the transmission and amplification of established ones. As anthropogenic climate change alters natural systems and global economic forces drive land-use and land-cover change, it becomes increasingly important to understand both the ecological and social factors that impact infectious disease outcomes for human populations. While the field of disease ecology explicitly studies the ecological aspects of infectious disease transmission, the effects of the social context on zoonotic pathogen spillover and subsequent human-to-human transmission are comparatively neglected in the literature. The social sciences encompass a variety of disciplines and frameworks for understanding infectious diseases; however, here we focus on four primary areas of social systems that quantitatively and qualitatively contribute to infectious diseases as social–ecological systems. These areas are social mixing and structure, space and mobility, geography and environmental impact, and behaviour and behaviour change. Incorporation of these social factors requires empirical studies for parametrization, phenomena characterization and integrated theoretical modelling of social–ecological interactions. The social–ecological system that dictates infectious disease dynamics is a complex system rich in interacting variables with dynamically significant heterogeneous properties. Future discussions about infectious disease spillover and transmission in human populations need to address the social context that affects particular disease systems by identifying and measuring qualitatively important drivers. This article is part of the themed issue ‘Opening the black box: re-examining the ecology and evolution of parasite transmission’.

scholarly journals Multi-Scale Drivers of Immunological Variation and Consequences for Infectious Disease Dynamics

2019 ◽  
Vol 59 (5) ◽  
pp. 1129-1137 ◽  
Author(s):  
Daniel J Becker ◽  
Cynthia J Downs ◽  
Lynn B Martin
Keyword(s):  
Infectious Disease ◽  
Host Defense ◽  
Disease Ecology ◽  
Landscape Heterogeneity ◽  
Population Level ◽  
Immune Defense ◽  
Model Systems ◽  
Disease Dynamics ◽  
Multi Scale ◽  
Infectious Disease Dynamics

Abstract The immune system is the primary barrier to parasite infection, replication, and transmission following exposure, and variation in immunity can accordingly manifest in heterogeneity in traits that govern population-level infectious disease dynamics. While much work in ecoimmunology has focused on individual-level determinants of host immune defense (e.g., reproductive status and body condition), an ongoing challenge remains to understand the broader evolutionary and ecological contexts of this variation (e.g., phylogenetic relatedness and landscape heterogeneity) and to connect these differences into epidemiological frameworks. Ultimately, such efforts could illuminate general principles about the drivers of host defense and improve predictions and control of infectious disease. Here, we highlight recent work that synthesizes the complex drivers of immunological variation across biological scales of organization and scales these within-host differences to population-level infection outcomes. Such studies note the limitations involved in making species-level comparisons of immune phenotypes, stress the importance of spatial scale for immunology research, showcase several statistical tools for translating within-host data into epidemiological parameters, and provide theoretical frameworks for linking within- and between-host scales of infection processes. Building from these studies, we highlight several promising avenues for continued work, including the application of machine learning tools and phylogenetically controlled meta-analyses to immunology data and quantifying the joint spatial and temporal dependencies in immune defense using range expansions as model systems. We also emphasize the use of organismal traits (e.g., host tolerance, competence, and resistance) as a way to interlink various scales of analysis. Such continued collaboration and disciplinary cross-talk among ecoimmunology, disease ecology, and mathematical modeling will facilitate an improved understanding of the multi-scale drivers and consequences of variation in host defense.

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