Estimating individuals' genetic and non-genetic effects underlying infectious disease transmission from temporal epidemic data.
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 recoverabil...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-12-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008447&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849337487141371904 |
|---|---|
| author | Christopher M Pooley Glenn Marion Stephen C Bishop Richard I Bailey Andrea B Doeschl-Wilson |
| author_facet | Christopher M Pooley Glenn Marion Stephen C Bishop Richard I Bailey Andrea B Doeschl-Wilson |
| author_sort | Christopher M Pooley |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7d89e2aec95a4618892f3b8bef9d58a2 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2020-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-7d89e2aec95a4618892f3b8bef9d58a22025-08-20T03:44:40ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-12-011612e100844710.1371/journal.pcbi.1008447Estimating individuals' genetic and non-genetic effects underlying infectious disease transmission from temporal epidemic data.Christopher M PooleyGlenn MarionStephen C BishopRichard I BaileyAndrea B Doeschl-WilsonIndividuals 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.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008447&type=printable |
| spellingShingle | Christopher M Pooley Glenn Marion Stephen C Bishop Richard I Bailey Andrea B Doeschl-Wilson Estimating individuals' genetic and non-genetic effects underlying infectious disease transmission from temporal epidemic data. PLoS Computational Biology |
| title | Estimating individuals' genetic and non-genetic effects underlying infectious disease transmission from temporal epidemic data. |
| title_full | Estimating individuals' genetic and non-genetic effects underlying infectious disease transmission from temporal epidemic data. |
| title_fullStr | Estimating individuals' genetic and non-genetic effects underlying infectious disease transmission from temporal epidemic data. |
| title_full_unstemmed | Estimating individuals' genetic and non-genetic effects underlying infectious disease transmission from temporal epidemic data. |
| title_short | Estimating individuals' genetic and non-genetic effects underlying infectious disease transmission from temporal epidemic data. |
| title_sort | estimating individuals genetic and non genetic effects underlying infectious disease transmission from temporal epidemic data |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008447&type=printable |
| work_keys_str_mv | AT christophermpooley estimatingindividualsgeneticandnongeneticeffectsunderlyinginfectiousdiseasetransmissionfromtemporalepidemicdata AT glennmarion estimatingindividualsgeneticandnongeneticeffectsunderlyinginfectiousdiseasetransmissionfromtemporalepidemicdata AT stephencbishop estimatingindividualsgeneticandnongeneticeffectsunderlyinginfectiousdiseasetransmissionfromtemporalepidemicdata AT richardibailey estimatingindividualsgeneticandnongeneticeffectsunderlyinginfectiousdiseasetransmissionfromtemporalepidemicdata AT andreabdoeschlwilson estimatingindividualsgeneticandnongeneticeffectsunderlyinginfectiousdiseasetransmissionfromtemporalepidemicdata |