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 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.

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 Infectious disease transmission and contact networks in wildlife and livestock

2015 ◽  
Vol 370 (1669) ◽  
pp. 20140107 ◽  
Author(s):  
Meggan E. Craft
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Wild Animal ◽  
Disease Spread ◽  
Farmed Fish ◽  
Infectious Disease Transmission ◽  
Network Modelling ◽  
Flexible Tool ◽  
Contact Networks ◽  
Contact Data

The use of social and contact networks to answer basic and applied questions about infectious disease transmission in wildlife and livestock is receiving increased attention. Through social network analysis, we understand that wild animal and livestock populations, including farmed fish and poultry, often have a heterogeneous contact structure owing to social structure or trade networks. Network modelling is a flexible tool used to capture the heterogeneous contacts of a population in order to test hypotheses about the mechanisms of disease transmission, simulate and predict disease spread, and test disease control strategies. This review highlights how to use animal contact data, including social networks, for network modelling, and emphasizes that researchers should have a pathogen of interest in mind before collecting or using contact data. This paper describes the rising popularity of network approaches for understanding transmission dynamics in wild animal and livestock populations; discusses the common mismatch between contact networks as measured in animal behaviour and relevant parasites to match those networks; and highlights knowledge gaps in how to collect and analyse contact data. Opportunities for the future include increased attention to experiments, pathogen genetic markers and novel computational tools.

scholarly journals Identification of effective spreaders in contact networks using dynamical influence

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ruaridh A. Clark ◽  
Malcolm Macdonald
Keyword(s):  
Disease Transmission ◽  
Average Rate ◽  
Laplacian Matrix ◽  
Disease Spread ◽  
Eigenvector Centrality ◽  
Contact Networks ◽  
Human Contact ◽  
Spread Dynamics ◽  
Definition Of ◽  
High Degree

AbstractContact networks provide insights on disease spread due to the duration of close proximity interactions. For systems governed by consensus dynamics, network structure is key to optimising the spread of information. For disease spread over contact networks, the structure would be expected to be similarly influential. However, metrics that are essentially agnostic to the network’s structure, such as weighted degree (strength) centrality and its variants, perform near-optimally in selecting effective spreaders. These degree-based metrics outperform eigenvector centrality, despite disease spread over a network being a random walk process. This paper improves eigenvector-based spreader selection by introducing the non-linear relationship between contact time and the probability of disease transmission into the assessment of network dynamics. This approximation of disease spread dynamics is achieved by altering the Laplacian matrix, which in turn highlights why nodes with a high degree are such influential disease spreaders. From this approach, a trichotomy emerges on the definition of an effective spreader where, for susceptible-infected simulations, eigenvector-based selections can either optimise the initial rate of infection, the average rate of infection, or produce the fastest time to full infection of the network. Simulated and real-world human contact networks are examined, with insights also drawn on the effective adaptation of ant colony contact networks to reduce pathogen spread and protect the queen ant.

scholarly journals Stochastic Models of Emerging Infectious Disease Transmission on Adaptive Random Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Navavat Pipatsart ◽  
Wannapong Triampo ◽  
Charin Modchang
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Random Network ◽  
Network Models ◽  
Transmission Probability ◽  
Stochastic Simulations ◽  
Random Networks ◽  
Cumulative Number ◽  
Network Adaptation ◽  
Infectious Disease Dynamics

We presented adaptive random network models to describe human behavioral change during epidemics and performed stochastic simulations of SIR (susceptible-infectious-recovered) epidemic models on adaptive random networks. The interplay between infectious disease dynamics and network adaptation dynamics was investigated in regard to the disease transmission and the cumulative number of infection cases. We found that the cumulative case was reduced and associated with an increasing network adaptation probability but was increased with an increasing disease transmission probability. It was found that the topological changes of the adaptive random networks were able to reduce the cumulative number of infections and also to delay the epidemic peak. Our results also suggest the existence of a critical value for the ratio of disease transmission and adaptation probabilities below which the epidemic cannot occur.

scholarly journals Household members do not contact each other at random: implications for infectious disease modelling

2018 ◽  
Vol 285 (1893) ◽  
pp. 20182201 ◽  
Author(s):  
Nele Goeyvaerts ◽  
Eva Santermans ◽  
Gail Potter ◽  
Andrea Torneri ◽  
Kim Van Kerckhove ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Social Contact ◽  
Household Contact ◽  
Epidemic Models ◽  
Disease Spread ◽  
Contact Density ◽  
Contact Networks ◽  
Household Transmission ◽  
Household Members ◽  
Contact Survey

Airborne infectious diseases such as influenza are primarily transmitted from human to human by means of social contacts, and thus easily spread within households. Epidemic models, used to gain insight into infectious disease spread and control, typically rely on the assumption of random mixing within households. Until now, there has been no direct empirical evidence to support this assumption. Here, we present the first social contact survey specifically designed to study contact networks within households. The survey was conducted in Belgium (Flanders and Brussels) from 2010 to 2011. We analysed data from 318 households totalling 1266 individuals with household sizes ranging from two to seven members. Exponential-family random graph models (ERGMs) were fitted to the within-household contact networks to reveal the processes driving contact between household members, both on weekdays and weekends. The ERGMs showed a high degree of clustering and, specifically on weekdays, decreasing connectedness with increasing household size. Furthermore, we found that the odds of a contact between older siblings and between father and child are smaller than for any other pair. The epidemic simulation results suggest that within-household contact density is the main driver of differences in epidemic spread between complete and empirical-based household contact networks. The homogeneous mixing assumption may therefore be an adequate characterization of the within-household contact structure for the purpose of epidemic simulations. However, ignoring the contact density when inferring based on an epidemic model will result in biased estimates of within-household transmission rates. Further research regarding the implementation of within-household contact networks in epidemic models is necessary.

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 To die so far from Dixie : modeling epidemic dysentery in a Civil War prison camp

2015 ◽  
Author(s):  
◽  
Rebecca Shattuck Lander
Keyword(s):  
Civil War ◽  
Disease Transmission ◽  
National Level ◽  
Human Movement ◽  
Pathogen Transmission ◽  
Disease Spread ◽  
Computer Simulation Model ◽  
Prison Camp ◽  
The Impact ◽  
The Way

Epidemics have played a role in shaping human experiences of conflict among both soldiers and civilians. Prisoners of war, displaced populations, and confined refugees have experienced, and continue to experience, outbreaks of infectious disease, which are exacerbated by physical, environmental, and psychological stressors. Observations of epidemics at the global, regional, or national level are not always able to provide a complete picture of the unique health challenges of these wartime populations. This research develops and applies a computer simulation model to examine the way human behaviors, and the impact of those behaviors on the environment, can impact the way diarrheal diseases develop and spread in confined high-density living situations. This simulation was tested against the recorded death and sickness patterns for a dysentery outbreak at Camp Douglas, Illinois, a 19th Century Civil War prison camp. The agent-based simulation used in this research is a unique approach, and is based on the feedback relationship between human movement and behavior and the resulting contamination of physical spaces with infectious material, rather than direct person-to-person pathogen transmission. The results of this simulation suggests that modeling disease transmission based on environmental result in distinct epidemic dynamics. The results of this research emphasize the importance of examining the relationship between humans, their environment, and patterns of health and disease. Additionally, it highlights the way that model design can help to increase knowledge of how even limited movement and interaction options available to confined individuals can lead to significant differences in patterns of disease spread and epidemic development, which can help to better design public health interventions targeted at confined populations.

scholarly journals Pathogen transmission from vaccinated hosts can cause dose-dependent reduction in virulence

2019 ◽  
Author(s):  
Richard I. Bailey ◽  
Hans H. Cheng ◽  
Margo Chase-Topping ◽  
Jody K. Mays ◽  
Osvaldo Anacleto ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Infectious Virus ◽  
Pathogen Transmission ◽  
Transmission Dynamics ◽  
Disease Spread ◽  
Disease Development ◽  
Disease Symptoms ◽  
Pathogen Virulence ◽  
Pathogen Load ◽  
Dose Dependent

AbstractMany livestock and human vaccines are leaky as they block symptoms but do not prevent infection or onward transmission. This leakiness is concerning as it increases vaccination coverage required to prevent disease spread, and can promote evolution of increased pathogen virulence. Despite leakiness, vaccination may reduce pathogen load, affecting disease transmission dynamics. However, the impacts on post-transmission disease development and infectiousness in contact individuals are unknown. Here, we use transmission experiments involving Marek’s disease virus in chickens to show that vaccination with a leaky vaccine substantially reduces viral load in both vaccinated individuals and unvaccinated contact individuals they infect. Consequently, contact birds are less likely to develop disease symptoms or die, show less severe symptoms, and shed less infectious virus themselves, when infected by vaccinated birds. These results highlight that even partial vaccination with a leaky vaccine can have unforeseen positive consequences in controlling the spread and symptoms of disease.

scholarly journals Estimating individuals’ genetic and non-genetic effects underlying infectious disease transmission from temporal epidemic data

2020 ◽  
Vol 16 (12) ◽  
pp. e1008447
Author(s):  
Christopher M. Pooley ◽  
Glenn Marion ◽  
Stephen C. Bishop ◽  
Richard I. Bailey ◽  
Andrea B. Doeschl-Wilson
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Statistical Power ◽  
Genetic Effect ◽  
Software Tool ◽  
Disease Spread ◽  
Simultaneous Estimation ◽  
Parameter Estimates ◽  
Infectious Disease Transmission ◽  
Wide Range

Individuals differ widely in their contribution to the spread of infection within and across populations. Three key epidemiological host traits affect infectious disease spread: susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection to others) and recoverability (propensity to recover quickly). Interventions aiming to reduce disease spread may target improvement in any one of these traits, but the necessary statistical methods for obtaining risk estimates are lacking. In this paper we introduce a novel software tool called SIRE (standing for “Susceptibility, Infectivity and Recoverability Estimation”), which allows for the first time simultaneous estimation of the genetic effect of a single nucleotide polymorphism (SNP), as well as non-genetic influences on these three unobservable host traits. SIRE implements a flexible Bayesian algorithm which accommodates a wide range of disease surveillance data comprising any combination of recorded individual infection and/or recovery times, or disease diagnostic test results. Different genetic and non-genetic regulations and data scenarios (representing realistic recording schemes) were simulated to validate SIRE and to assess their impact on the precision, accuracy and bias of parameter estimates. This analysis revealed that with few exceptions, SIRE provides unbiased, accurate parameter estimates associated with all three host traits. For most scenarios, SNP effects associated with recoverability can be estimated with highest precision, followed by susceptibility. For infectivity, many epidemics with few individuals give substantially more statistical power to identify SNP effects than the reverse. Importantly, precise estimates of SNP and other effects could be obtained even in the case of incomplete, censored and relatively infrequent measurements of individuals’ infection or survival status, albeit requiring more individuals to yield equivalent precision. SIRE represents a new tool for analysing a wide range of experimental and field disease data with the aim of discovering and validating SNPs and other factors controlling infectious disease transmission.

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