Using genetic data to identify transmission risk factors: Statistical assessment and application to tuberculosis transmission.
Identifying host factors that influence infectious disease transmission is an important step toward developing interventions to reduce disease incidence. Recent advances in methods for reconstructing infectious disease transmission events using pathogen genomic and epidemiological data open the door...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-12-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010696 |
| _version_ | 1797974028597592064 |
|---|---|
| author | Isaac H Goldstein Damon Bayer Ivan Barilar Balladiah Kizito Ogopotse Matsiri Chawangwa Modongo Nicola M Zetola Stefan Niemann Volodymyr M Minin Sanghyuk S Shin |
| author_facet | Isaac H Goldstein Damon Bayer Ivan Barilar Balladiah Kizito Ogopotse Matsiri Chawangwa Modongo Nicola M Zetola Stefan Niemann Volodymyr M Minin Sanghyuk S Shin |
| author_sort | Isaac H Goldstein |
| collection | DOAJ |
| description | Identifying host factors that influence infectious disease transmission is an important step toward developing interventions to reduce disease incidence. Recent advances in methods for reconstructing infectious disease transmission events using pathogen genomic and epidemiological data open the door for investigation of host factors that affect onward transmission. While most transmission reconstruction methods are designed to work with densely sampled outbreaks, these methods are making their way into surveillance studies, where the fraction of sampled cases with sequenced pathogens could be relatively low. Surveillance studies that use transmission event reconstruction then use the reconstructed events as response variables (i.e., infection source status of each sampled case) and use host characteristics as predictors (e.g., presence of HIV infection) in regression models. We use simulations to study estimation of the effect of a host factor on probability of being an infection source via this multi-step inferential procedure. Using TransPhylo-a widely-used method for Bayesian estimation of infectious disease transmission events-and logistic regression, we find that low sensitivity of identifying infection sources leads to dilution of the signal, biasing logistic regression coefficients toward zero. We show that increasing the proportion of sampled cases improves sensitivity and some, but not all properties of the logistic regression inference. Application of these approaches to real world data from a population-based TB study in Botswana fails to detect an association between HIV infection and probability of being a TB infection source. We conclude that application of a pipeline, where one first uses TransPhylo and sparsely sampled surveillance data to infer transmission events and then estimates effects of host characteristics on probabilities of these events, should be accompanied by a realistic simulation study to better understand biases stemming from imprecise transmission event inference. |
| first_indexed | 2024-04-11T04:13:36Z |
| format | Article |
| id | doaj.art-ff734196f7de4d52b684c0ed1d80a41d |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-11T04:13:36Z |
| publishDate | 2022-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-ff734196f7de4d52b684c0ed1d80a41d2023-01-01T05:31:09ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-12-011812e101069610.1371/journal.pcbi.1010696Using genetic data to identify transmission risk factors: Statistical assessment and application to tuberculosis transmission.Isaac H GoldsteinDamon BayerIvan BarilarBalladiah KizitoOgopotse MatsiriChawangwa ModongoNicola M ZetolaStefan NiemannVolodymyr M MininSanghyuk S ShinIdentifying host factors that influence infectious disease transmission is an important step toward developing interventions to reduce disease incidence. Recent advances in methods for reconstructing infectious disease transmission events using pathogen genomic and epidemiological data open the door for investigation of host factors that affect onward transmission. While most transmission reconstruction methods are designed to work with densely sampled outbreaks, these methods are making their way into surveillance studies, where the fraction of sampled cases with sequenced pathogens could be relatively low. Surveillance studies that use transmission event reconstruction then use the reconstructed events as response variables (i.e., infection source status of each sampled case) and use host characteristics as predictors (e.g., presence of HIV infection) in regression models. We use simulations to study estimation of the effect of a host factor on probability of being an infection source via this multi-step inferential procedure. Using TransPhylo-a widely-used method for Bayesian estimation of infectious disease transmission events-and logistic regression, we find that low sensitivity of identifying infection sources leads to dilution of the signal, biasing logistic regression coefficients toward zero. We show that increasing the proportion of sampled cases improves sensitivity and some, but not all properties of the logistic regression inference. Application of these approaches to real world data from a population-based TB study in Botswana fails to detect an association between HIV infection and probability of being a TB infection source. We conclude that application of a pipeline, where one first uses TransPhylo and sparsely sampled surveillance data to infer transmission events and then estimates effects of host characteristics on probabilities of these events, should be accompanied by a realistic simulation study to better understand biases stemming from imprecise transmission event inference.https://doi.org/10.1371/journal.pcbi.1010696 |
| spellingShingle | Isaac H Goldstein Damon Bayer Ivan Barilar Balladiah Kizito Ogopotse Matsiri Chawangwa Modongo Nicola M Zetola Stefan Niemann Volodymyr M Minin Sanghyuk S Shin Using genetic data to identify transmission risk factors: Statistical assessment and application to tuberculosis transmission. PLoS Computational Biology |
| title | Using genetic data to identify transmission risk factors: Statistical assessment and application to tuberculosis transmission. |
| title_full | Using genetic data to identify transmission risk factors: Statistical assessment and application to tuberculosis transmission. |
| title_fullStr | Using genetic data to identify transmission risk factors: Statistical assessment and application to tuberculosis transmission. |
| title_full_unstemmed | Using genetic data to identify transmission risk factors: Statistical assessment and application to tuberculosis transmission. |
| title_short | Using genetic data to identify transmission risk factors: Statistical assessment and application to tuberculosis transmission. |
| title_sort | using genetic data to identify transmission risk factors statistical assessment and application to tuberculosis transmission |
| url | https://doi.org/10.1371/journal.pcbi.1010696 |
| work_keys_str_mv | AT isaachgoldstein usinggeneticdatatoidentifytransmissionriskfactorsstatisticalassessmentandapplicationtotuberculosistransmission AT damonbayer usinggeneticdatatoidentifytransmissionriskfactorsstatisticalassessmentandapplicationtotuberculosistransmission AT ivanbarilar usinggeneticdatatoidentifytransmissionriskfactorsstatisticalassessmentandapplicationtotuberculosistransmission AT balladiahkizito usinggeneticdatatoidentifytransmissionriskfactorsstatisticalassessmentandapplicationtotuberculosistransmission AT ogopotsematsiri usinggeneticdatatoidentifytransmissionriskfactorsstatisticalassessmentandapplicationtotuberculosistransmission AT chawangwamodongo usinggeneticdatatoidentifytransmissionriskfactorsstatisticalassessmentandapplicationtotuberculosistransmission AT nicolamzetola usinggeneticdatatoidentifytransmissionriskfactorsstatisticalassessmentandapplicationtotuberculosistransmission AT stefanniemann usinggeneticdatatoidentifytransmissionriskfactorsstatisticalassessmentandapplicationtotuberculosistransmission AT volodymyrmminin usinggeneticdatatoidentifytransmissionriskfactorsstatisticalassessmentandapplicationtotuberculosistransmission AT sanghyuksshin usinggeneticdatatoidentifytransmissionriskfactorsstatisticalassessmentandapplicationtotuberculosistransmission |