Publisher
PLoS
Abstract
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.
Year
2020
Category
Refereed journal