Incorporating Epidemiological Data into the Genomic Analysis of Partially Sampled Infectious Disease Outbreaks

Pathogen genomic data are increasingly being used to investigate transmission dynamics in infectious disease outbreaks. Combining genomic data with epidemiological data should substantially increase our understanding of outbreaks, but this is highly challenging when the outbreak under study is only...

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Vydané v:Molecular biology and evolution Ročník 42; číslo 4
Hlavní autori: Carson, Jake, Keeling, Matt, Ribeca, Paolo, Didelot, Xavier
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Oxford University Press 01.04.2025
Predmet:
Algorithms
Animals
Argentina - epidemiology
Communicable Diseases - epidemiology
Communicable Diseases - genetics
Disease Outbreaks
Genomics - methods
HIV Infections - epidemiology
Humans
Influenza in Birds - epidemiology
Methods
Netherlands - epidemiology
Tuberculosis - epidemiology
Tuberculosis - genetics
Netherlands
Argentina
transmission analysis
epidemiological data
genomic epidemiology
infectious disease outbreak
ISSN:0737-4038, 1537-1719, 1537-1719
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Shrnutí:Pathogen genomic data are increasingly being used to investigate transmission dynamics in infectious disease outbreaks. Combining genomic data with epidemiological data should substantially increase our understanding of outbreaks, but this is highly challenging when the outbreak under study is only partially sampled, so that both genomic and epidemiological data are missing for intermediate links in the transmission chains. Here, we present a new dynamic programming algorithm to perform this task efficiently. We implement this methodology into the well-established TransPhylo framework to reconstruct partially sampled outbreaks using a combination of genomic and epidemiological data. We use simulated datasets to show that including epidemiological data can improve the accuracy of the inferred transmission links compared with inference based on genomic data only. This also allows us to estimate parameters specific to the epidemiological data (such as transmission rates between particular groups), which would otherwise not be possible. We then apply these methods to two real-world examples. First, we use genomic data from an outbreak of tuberculosis in Argentina, for which data was also available on the HIV status of sampled individuals, in order to investigate the role of HIV coinfection in the spread of this tuberculosis outbreak. Second, we use genomic and geographical data from the 2003 epidemic of avian influenza H7N7 in the Netherlands to reconstruct its spatial epidemiology. In both cases, we show that incorporating epidemiological data into the genomic analysis allows us to investigate the role of epidemiological properties in the spread of infectious diseases.
Bibliografia:ObjectType-Article-1
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Conflict of interest: None declared.
ISSN:0737-4038
1537-1719
1537-1719
DOI:10.1093/molbev/msaf083