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 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.

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 Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission

2022 ◽  
Author(s):  
Christopher Mark Pooley ◽  
Glenn Marion ◽  
Andrea Doeschl-Wilson
Keyword(s):  
Infectious Disease ◽  
Experimental Design ◽  
Disease Transmission ◽  
Software Tool ◽  
Experimental Designs ◽  
Disease Spread ◽  
Design Parameters ◽  
Categorical Variables ◽  
Group Design ◽  
Analytical Expressions

BACKGROUND: Infectious disease spread in populations is controlled by individuals' susceptibility (propensity to acquire infection), infectivity (propensity to pass on infection to others) and recoverability (propensity to recover/die). Estimating the effects of genetic risk factors on these host epidemiological traits can help reduce disease spread through genetic control strategies. However, the effects of previously identified "disease resistance SNPs" on these epidemiological traits are usually unknown. Recent advances in computational statistics make it now possible to estimate the effects of single nucleotide polymorphisms (SNPs) on these traits from longitudinal epidemic data (e.g. infection and/or recovery times of individuals or diagnostic test results). However, little is known how to optimally design disease transmission experiments or field studies to maximise the precision at which pleiotropic SNP effects estimates for susceptibility, infectivity and recoverability can be estimated. RESULTS: We develop and validate analytical expressions for the precision of SNP effects estimates on the three host traits assuming a disease transmission experiment with one or more non-interacting contact groups. Maximising these leads to three distinct "experimental" designs, each specifying a different set of ideal SNP genotype compositions across groups: a) appropriate for a single contact-group, b) a multi-group design termed "pure", and c) a multi-group design termed "mixed", where "pure" and "mixed" refer to contact groups consisting of individuals with the same or different SNP genotypes, respectively. Precision estimates for susceptibility and recoverability were found to be less sensitive to the experimental design than infectivity. Data from multiple groups were found more informative about infectivity effects than from a single group containing the same number of individuals. Whilst the analytical expressions suggest that the multi-group pure and mixed designs estimate SNP effects with similar precision, the mixed design is preferable because it uses information from naturally occurring infections rather than those artificially induced. The same optimal design principles apply to estimating other categorical fixed effects, such as vaccinations status, helping to more effectively quantify their epidemiological impact. An online software tool SIRE-PC has been developed which calculates the precision of estimated substitution and dominance effects of a single SNP (or vaccine status) associated with all three traits depending on experimental design parameters. CONCLUSIONS: The developed methodology and software tool can be used to aid the design of disease transmission experiments for estimating the effect of individual SNPs and other categorical variables underlying host susceptibility, infectivity and recoverability.

Regional Relative Risk, a Physics-Based Metric for Characterizing Airborne Infectious Disease Transmission

2021 ◽  
Author(s):  
Michael B Dillon ◽  
Charles F Dillon
Keyword(s):  
Infectious Disease ◽  
Relative Risk ◽  
Disease Transmission ◽  
Spatial Scales ◽  
Disease Outbreaks ◽  
Indoor Environments ◽  
Infectious Disease Transmission ◽  
Wide Range ◽  
Risk Metric ◽  
Airborne Disease

Airborne infectious disease transmission events occur over a wide range of spatial scales and can be an important means of disease transmission. Physics- and biology- based models can assist in predicting airborne transmission events, overall disease incidence, and disease control strategy efficacy. We develop a new theory that extends current approaches for the case in which an individual is infected by a single airborne particle, including the scenario in which numerous infectious particles are present in the air but only one causes infection. A single infectious particle can contain more than one pathogenic microorganism and be physically larger than the pathogen itself. This approach allows robust relative risk estimates even when there is wide variation in (a) individual exposures and (b) the individual response to that exposure (the pathogen dose-response function can take any mathematical form and vary by individual). Based on this theory, we propose the Regional Relative Risk – a new metric, distinct from the traditional relative risk metric, that compares the risk between two regions (in theory, these regions can range from individual rooms to large geographic areas). In this paper, we apply the Regional Relative Risk metric to outdoor disease transmission events over spatial scales ranging from 50 m to 20 km demonstrating that in many common cases, minimal input information is required to use the metric. Also, we demonstrate that it is consistent with data from prior outbreaks. Future efforts could apply and validate this theory for other spatial scales, such as indoor environments. This work provides context for (a) the initial stages of an airborne disease outbreak and (b) larger scale disease spread, including unexpected low probability disease “sparks” that potentially affect remote populations, a key practical issue in controlling airborne disease outbreaks. Importance Airborne infectious disease transmission events occur over a wide range of spatial scales and can be important to disease outbreaks. We develop a new physics- and biology- based theory for the important case in which individuals are infected by a single airborne particle (numerous infectious particles can be emitted into the air and inhaled). Based on this theory, we propose a new epidemiological metric, Regional Relative Risk, that compares the risk between two geographic regions (in theory, regions can range from individual rooms to large areas). Our modeling of outdoors transmission events predicts that for many scenarios of interest, minimal information is required to use this metric for locations 50 m to 20 km downwind. This prediction is consistent with data from prior disease outbreaks. Future efforts could apply and validate this theory for other spatial scales, such as indoor environments. Our results may a priori be applicable to many airborne diseases as these results depend on the physics of airborne particulate dispersion.

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 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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