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 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.

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.

Using empirical social contact data to model person to person infectious disease transmission: An illustration for varicella

2009 ◽  
Vol 218 (2) ◽  
pp. 80-87 ◽  
Author(s):  
Benson Ogunjimi ◽  
Niel Hens ◽  
Nele Goeyvaerts ◽  
Marc Aerts ◽  
Pierre Van Damme ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Social Contact ◽  
Infectious Disease Transmission ◽  
Contact Data

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

2019 ◽  
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 ◽  
Disease Status ◽  
Disease Spread ◽  
Simultaneous Estimation ◽  
Parameter Estimates ◽  
Infectious Disease Transmission ◽  
Wide Range

AbstractIndividuals 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 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 status measurements. 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.

Travel Connectivity and Infectious Disease Transmission Risks in Oceania

2020 ◽  
Author(s):  
Angela Maria Cadavid Restrepo ◽  
Luis Furuya-Kanamori ◽  
Helen Mayfield ◽  
Eric J. Nilles ◽  
Colleen L. Lau
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Infectious Disease Transmission

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 SPATIAL HETEROGENEITY IN SIMPLE DETERMINISTIC SIR MODELS ASSESSED ECOLOGICALLY

2012 ◽  
Vol 54 (1-2) ◽  
pp. 23-36 ◽  
Author(s):  
E. K. WATERS ◽  
H. S. SIDHU ◽  
G. N. MERCER
Keyword(s):  
Infectious Disease ◽  
Spatial Heterogeneity ◽  
Disease Transmission ◽  
Herd Immunity ◽  
Epidemic Models ◽  
Final Size ◽  
Infectious Disease Transmission ◽  
Epidemic Dynamics ◽  
Mean Crowding ◽  
Rate Of Movement

AbstractPatchy or divided populations can be important to infectious disease transmission. We first show that Lloyd’s mean crowding index, an index of patchiness from ecology, appears as a term in simple deterministic epidemic models of the SIR type. Using these models, we demonstrate that the rate of movement between patches is crucial for epidemic dynamics. In particular, there is a relationship between epidemic final size and epidemic duration in patchy habitats: controlling inter-patch movement will reduce epidemic duration, but also final size. This suggests that a strategy of quarantining infected areas during the initial phases of a virulent epidemic might reduce epidemic duration, but leave the population vulnerable to future epidemics by inhibiting the development of herd immunity.

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