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

Author(s):  
Michael B Dillon ◽  
Charles F Dillon

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.

2020 ◽  
Vol 16 (12) ◽  
pp. e1008447
Author(s):  
Christopher M. Pooley ◽  
Glenn Marion ◽  
Stephen C. Bishop ◽  
Richard I. Bailey ◽  
Andrea B. Doeschl-Wilson

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.


2019 ◽  
Vol 57 (6) ◽  
Author(s):  
Alexander L. Greninger

ABSTRACT The growth of pathogen genomics shows no signs of abating. Whole-genome sequencing of clinical viral and bacterial isolates continues to grow in nearly exponential bounds. Reductions in cost driven by new technology have created a seamless environment for generating, sharing, and analyzing pathogen genomes. The high-resolution view of infectious disease transmission dynamics offered by analyzing whole genomes from pathogens, coupled with the genomicist ethic of widespread data sharing, has created a veritable Internet of pathogens, which inadvertently produces new threats to patient privacy and protected heath information. The health care system, and society more generally, have yet to explore the far-reaching privacy concerns raised by readily accessible pathogen genomic data. The recent use of human genomic databases, the existence of freely available alternative data and metadata sources, and lax regulation of collecting publicly available genomes to identify individuals in a criminal context raise concerning parallels about what is possible with pathogen genomics. The growing ability to ascertain culpability for infectious disease transmission at a nearly individual level could change our perspective on disease outbreaks from one based on public health to one based on individual liability. These technological breakthroughs in the absence of an understanding of potential privacy and liability issues lead to questions about the dominant paradigm of better living through pathogen genomics.


2019 ◽  
Author(s):  
Christopher M. Pooley ◽  
Glenn Marion ◽  
Stephen C. Bishop ◽  
Richard I. Bailey ◽  
Andrea B. Doeschl-Wilson

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.


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

2012 ◽  
Vol 54 (1-2) ◽  
pp. 23-36 ◽  
Author(s):  
E. K. WATERS ◽  
H. S. SIDHU ◽  
G. N. MERCER

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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