Progress and challenges in virus genomic epidemiology

Abstract

Genomic epidemiology enables tracking of pathogen transmission over all spatial scales, from local outbreaks to global pandemics, thereby highlighting the scale of interventions required.Combining genomic data with individual-level metadata can identify demographic factors driving transmission patterns.The power of genomic epidemiology is increasing due to better models, methods, and novel technologies, including new sample-collection protocols and metadata.Future integration of virus genomic data may enable forecasting of the spatial spread and ignition/decline of epidemics. Genomic epidemiology, which links pathogen genomes with associated metadata to understand disease transmission, has become a key component of outbreak response. Decreasing costs of genome sequencing and increasing computational power provide opportunities to generate and analyse large viral genomic datasets that aim to uncover the spatial scales of transmission, the demographics contributing to transmission patterns, and to forecast epidemic trends. Emerging sources of genomic data and associated metadata provide new opportunities to further unravel transmission patterns. Key challenges include how to integrate genomic data with metadata from multiple sources, how to generate efficient computational algorithms to cope with large datasets, and how to establish sampling frameworks to enable robust conclusions. Genomic epidemiology, which links pathogen genomes with associated metadata to understand disease transmission, has become a key component of outbreak response. Decreasing costs of genome sequencing and increasing computational power provide opportunities to generate and analyse large viral genomic datasets that aim to uncover the spatial scales of transmission, the demographics contributing to transmission patterns, and to forecast epidemic trends. Emerging sources of genomic data and associated metadata provide new opportunities to further unravel transmission patterns. Key challenges include how to integrate genomic data with metadata from multiple sources, how to generate efficient computational algorithms to cope with large datasets, and how to establish sampling frameworks to enable robust conclusions. The decreasing cost of genomic sequencing combined with increasing computational power has led to an explosion of interest in the application of whole-genome sequencing to public health [1.Ladner J.T. et al.Precision epidemiology for infectious disease control.Nat. Med. 2019; 25: 206-211Crossref PubMed Scopus (45) Google Scholar,2.World Health Organisation Genomic Sequencing of SARS-CoV-2: a Guide to Implementation for Maximum Impact on Public Health. WHO, 2021Google Scholar]. This is facilitated by genomic epidemiology (see Glossary), the use of pathogen genomes to study the spread of infectious diseases through populations. Together with the field of phylodynamics – the combination of epidemiology, evolution, and immunodynamics [3.Grenfell B.T. et al.Unifying the epidemiological and evolutionary dynamics of pathogens.Science. 2004; 303: 327-332Crossref PubMed Scopus (854) Google Scholar] – this rapidly growing field has tackled key questions regarding epidemic preparedness and control, increasingly in real-time. While genomic data has been used for some time to investigate virus diversity, especially for influenza A virus and HIV [4.Grabowski M.K. Redd A.D. Molecular tools for studying HIV transmission in sexual networks.Curr. Opin. HIV AIDS. 2014; 9: 126-133Crossref PubMed Scopus (62) Google Scholar], the West African Ebola epidemic (2013–2016) was the first major and large-scale application of virus genomes to rapidly uncover epidemic origins and spread [5.Dudas G. et al.Virus genomes reveal factors that spread and sustained the Ebola epidemic.Nature. 2017; 544: 309-315Crossref PubMed Scopus (204) Google Scholar,6.Gire S.K. et al.Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak.Science. 2014; 345: 1369-1372Crossref PubMed Scopus (804) Google Scholar] as well as to identify late sources of localised outbreaks [7.Mate S.E. et al.Molecular evidence of sexual transmission of Ebola virus.N. Engl. J. Med. 2015; 373: 2448-2454Crossref PubMed Scopus (290) Google Scholar]. For every significant viral public health crisis since – poliovirus transmission in multiple countries [8.Famulare M. et al.Sabin vaccine reversion in the field: a comprehensive analysis of Sabin-like poliovirus isolates in Nigeria.J. Virol. 2016; 90: 317-331Crossref PubMed Scopus (37) Google Scholar], Zika virus in the Americas [9.Faria N.R. et al.Establishment and cryptic transmission of Zika virus in Brazil and the Americas.Nature. 2017; 546: 406-410Crossref PubMed Scopus (312) Google Scholar,10.Grubaugh N.D. et al.Genomic epidemiology reveals multiple introductions of Zika virus into the United States.Nature. 2017; 546: 401Crossref PubMed Scopus (202) Google Scholar], Middle Eastern respiratory syndrome (MERS) in Saudi Arabia [11.Cotten M. et al.Transmission and evolution of the Middle East respiratory syndrome coronavirus in Saudi Arabia: a descriptive genomic study.Lancet. 2013; 382: 1993Abstract Full Text Full Text PDF PubMed Scopus (222) Google Scholar, 12.Dudas G. et al.MERS-CoV spillover at the camel–human interface.eLife. 2018; 7e31257Crossref PubMed Scopus (107) Google Scholar, 13.Sabir J.S.M. et al.Co-circulation of three camel coronavirus species and recombination of MERS-CoVs in Saudi Arabia.Science. 2016; 351: 81-84Crossref PubMed Scopus (250) Google Scholar], Ebola virus in the Democratic Republic of the Congo (DRC) [14.Kinganda-Lusamaki E. et al.Integration of genomic sequencing into the response to the Ebola virus outbreak in Nord Kivu, Democratic Republic of the Congo.Nat. Med. 2021; 27: 710-716Crossref PubMed Scopus (4) Google Scholar], and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic (e.g., [15.du Plessis L. et al.Establishment and lineage dynamics of the SARS-CoV-2 epidemic in the UK.Science. 2021; 371: 708-712Crossref PubMed Scopus (112) Google Scholar, 16.Gudbjartsson D.F. et al.Spread of SARS-CoV-2 in the Icelandic population.N. Engl. J. Med. 2020; 382: 2302-2315Crossref PubMed Scopus (719) Google Scholar, 17.Resende P.C. et al.Genomic surveillance of SARS-CoV-2 reveals community transmission of a major lineage during the early pandemic phase in Brazil.bioRxiv. 2020; (Published online June 18, 2020)https://doi.org/10.1101/2020.06.17.158006Google Scholar]) – genomic epidemiology has played an important role in informing key stakeholders. These crises demanded a variety of approaches, including intensive genomic sequencing to understand transmission dynamics during the acute phase of epidemics (Ebola virus in the DRC), and broader genomic surveillance to identify cryptic increases in cases (polio). During the SARS-CoV-2 pandemic, many countries that did not previously utilise genomic data have begun to produce and rely on its outputs. With the SARS-CoV-2 genomic dataset now containing >2.5 million sequences from >185 countries (www.gisaid.org), and subsequent pressure from policy makers and funders to see returns on investment from genomic epidemiology, there has been a rapid development of new methodologies to fully utilise this dataset to help control the pandemic. This article explores the avenues of research that genomic epidemiology can help to elucidate and the challenges that remain in fully realising its potential. Transmission of all infections occurs at different spatial scales [18.Levin S.A. The problem of pattern and scale in ecology: The Robert H. Macarthur award lecture.Ecology. 1992; 73: 1943-1967Crossref Scopus (4820) Google Scholar,19.Snedden C.E. et al.SARS-CoV-2: Cross-scale insights from ecology and evolution.Trends Microbiol. 2021; 29: 593-605Abstract Full Text Full Text PDF PubMed Scopus (3) Google Scholar] that depend on the pathogen, host movement patterns, immunity, and other factors. Genomic data can help to identify how pathogens are related through space and time (Figure 1A ) and thereby track outbreaks across all spatial scales, from within-hospital outbreaks to international pandemics [20.Hodcroft E.B. et al.Spread of a SARS-CoV-2 variant through Europe in the summer of 2020.Nature. 2021; 595: 707-712Crossref PubMed Scopus (87) Google Scholar,21.Meredith L.W. et al.Rapid implementation of SARS-CoV-2 sequencing to investigate cases of health-care associated COVID-19: a prospective genomic surveillance study.Lancet Infect. Dis. 2020; 20: 1263-1271Abstract Full Text Full Text PDF PubMed Scopus (147) Google Scholar]. However, the link between the scales of transmission (where and how pathogens are related) and the optimal intervention scenario (spatial targeting of non-pharmaceutical interventions, isolation of superspreaders, etc.) has not been explored in detail. A relatively simple and well-tried way to examine transmission among regions is through a phylogeographic analysis. This involves treating locations (the sampling scale of which is often chosen arbitrarily, based on the geographic scale where the samples are collected) as sequence traits, and modelling their evolution across the phylogenetic tree in a discrete or continuous has used discrete for influenza movement among countries through international et al.Unifying viral and data to the global transmission dynamics of influenza 2014; PubMed Scopus Google Scholar] and the transmission of Ebola virus at a scale in West [5.Dudas G. et al.Virus genomes reveal factors that spread and sustained the Ebola epidemic.Nature. 2017; 544: 309-315Crossref PubMed Scopus (204) Google the continuous has been used to elucidate the spatial of West virus in the et a phylogeographic and 2020; PubMed Scopus Google Scholar]. et used phylogeographic to that the three countries in the 2014 West African epidemic and transmission on different scales, with cases in from a and sustained the epidemic in due to many introductions into the from and in different viral dynamics [5.Dudas G. et al.Virus genomes reveal factors that spread and sustained the Ebola epidemic.Nature. 2017; 544: 309-315Crossref PubMed Scopus (204) Google Scholar]. This in help with the between and interventions, and that in some cases a to disease control may not between different of the a can to the discrete to the of factors in transmission for For et that the of was a important of the global spread of influenza A a new spatial scale to the spread of through et al.Unifying viral and data to the global transmission dynamics of influenza 2014; PubMed Scopus Google Scholar]. can the of phylogeographic especially This has been an with SARS-CoV-2 as genomes from a of for the and the global dataset and may to transmission of virus M. et of SARS-CoV-2 in Europe and 2020; PubMed Google et transmission of SARS-CoV-2 in 2020; PubMed Google Scholar]. discrete a is in the data can at data to from where a viral lineage has can that the is locations in The is as countries with samples have the of introductions local sampling can the is relatively with large in the not to the of and continuous sampling can to the not including the origin of the and the et and in continuous on a 2021; PubMed Google Scholar]. While sampling and can help with this can by the of data to M. et for preparedness and for from the early epidemic in 2021; PubMed Scopus Google Scholar]. Plessis et used international and data to a this with a phylogeographic analysis to lineage that the epidemic was by introductions that from other countries well to the [15.du Plessis L. et al.Establishment and lineage dynamics of the SARS-CoV-2 epidemic in the UK.Science. 2021; 371: 708-712Crossref PubMed Scopus (112) Google Scholar]. to in the et phylogenetic and data integration 2018; PubMed Google Scholar], the and of are can in the of among locations and include new locations in the phylogeographic the tree and of the of et and in phylogeographic of 2020; PubMed Scopus Google Scholar]. This may in in of as for which the in the sequence data is This can used origins have sequences at as in an analysis of SARS-CoV-2 introductions in three countries genome introductions from et al.Genomic sequencing of SARS-CoV-2 in evolution and 2021; (Published online Scholar]. 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For SARS-CoV-2 into the due at to spread to multiple regions [15.du Plessis L. et al.Establishment and lineage dynamics of the SARS-CoV-2 epidemic in the UK.Science. 2021; 371: 708-712Crossref PubMed Scopus (112) Google Scholar]. transmission with sequence locations and data further uncover the spatial scale over which transmission occurs and the factors driving For including community (Figure M. of on reveal community PubMed Scopus Google Scholar]) in may help to identify the scale at which interventions are are through the analysis of data and may from (Figure insights may the scale over which interventions to to transmission and in transmission can have on the dynamics of an infectious For diseases with a of may to control by the locations or to et of SARS-CoV-2 2020; PubMed Scopus Google Scholar]. this in transmission can by data with genomic is to genomic data of the of genomic and data include of of SARS-CoV-2 transmission that on to the of cases in the in from a et al.Genomic epidemiology reveals transmission and dynamics of SARS-CoV-2 in 2020; PubMed Scopus Google Scholar], and from a et in the of in an genomic epidemiological study.Lancet Public Health. 2021; Full Text Full Text PDF PubMed Scopus Google Scholar], of which the of early and the demographic of a at is in the on public health factors as and can associated with of disease et origins of health for 2014; Full Text Full Text PDF PubMed Scopus Google Scholar]. the of coronavirus for in where an in the with the with the et and related in 2021; PubMed Scopus Google Scholar]. of SARS-CoV-2 and of with M. et and of 2020; PubMed Scopus Google Scholar]. factors a role in the transmission and the spread of an a into a fully epidemic is the of cases the and its as a During this often with data on and factors. However, as the transmission of new introductions into different of et and the of influenza epidemics in 2018; PubMed Scopus Google Scholar]. this of is in J. et reveals the role of and in the first of in 2021; (Published online June Scopus Google Scholar]. to epidemiological the of cases can into the of in an for diseases that are the spread of many infections has been to N.R. et virus in the epidemiological and 2016; PubMed Scopus Google Scholar, et role of the in the and of global PubMed Scopus Google Scholar, et and the global spread of PubMed Scopus Google Scholar, et the of on PubMed Scopus Google Scholar, et of on the of epidemics in 2015; PubMed Scopus Google Scholar, et of and control on the epidemic in 2020; PubMed Scopus Google Scholar], the integration of genomic data and (Figure in help to reveal the role of local of epidemiological as can become robust for sampling in the genomic a demographic factors an important role in disease For a of can the time of infections between and multiple into to may the of an especially during the early of an infectious disease or lineage et of and control on the epidemic in 2020; PubMed Scopus Google E. et of SARS-CoV-2 lineage in 2021; PubMed Scopus (312) Google Scholar]. large-scale genomic data associated with metadata (e.g., is to the role of different in the spread of an For has become during the SARS-CoV-2 pandemic that and diseases across different geographic scales etc.) M. et that epidemics in the United 2021; PubMed Google et of introductions and by genomic and 2021; (Published online Google Scholar]. a a pathogen in an in host can the dynamics of this For there a in where there was major outbreak of of a of or multiple due to with other et dynamics and of spatial to the of syndrome in PubMed Scopus Google Scholar]. in outbreaks between regions can enable of the disease B.T. Impact of on the spatial and of PubMed Scopus Google Scholar]. This is to due to spatial in of and vaccine The of and genomic data in to demographic of cases and of different regions can insights into which and are driving epidemics at different time et al.Transmission and of HIV in a phylogenetic study.Lancet 2017; Full Text Full Text PDF PubMed Scopus Google Scholar]. A of genomic epidemiology is that the integration of different data sources (Figure has the of data integration into for pathogens et of for Microbiol. 2021; 29: Full Text Full Text PDF PubMed Scopus Google Scholar]. that by including further to its to viral genomic data include and demographic as well as the locations where the sequences are for sequencing to and the of genomic epidemiology to new sources of genomic and data are For that are in sampling a new to community This is especially in where many infections are this has been used to in surveillance through a epidemic of poliovirus cases of acute been et of the outbreak in based on of surveillance 2018; PubMed Scopus Google Scholar]. sampling has been used to SARS-CoV-2 in uncover and was to not the in a community in a of was in samples et sequencing of SARS-CoV-2 2021; PubMed Scopus Google Scholar]. where cases are can through the of For the lineage a that in in a used This the analysis of the of cases that for to sequence the which a and of data to to a transmission in the E. et of SARS-CoV-2 lineage in 2021; PubMed Scopus (312) Google Scholar]. This especially important in that the relatively sequencing that has been for SARS-CoV-2 in the for in et SARS-CoV-2 lineage insights from and late to 2021; Scopus Google Scholar]. the have been to between the major SARS-CoV-2 of by that with to in et of to global surveillance of 2021; PubMed Scopus (62) Google Scholar]. The integration of data into genomic epidemiology increases the of data for of data in is data which can in a to epidemiological et disease phylodynamics with 2021; Scopus (3) Google Scholar]. This is especially the sequencing over time as can help to sampling of that have used data sources to as to or with genomic While this is the how to integrate with genomic and interventions to the spatial spread of infectious diseases are during disease to use to the spread of a virus in space J. et and forecasting the and international spread of the outbreak in a modelling study.Lancet. 2020; Full Text Full Text PDF PubMed Scopus Google Scholar] are by the of data early in an is of the during the early of an outbreak to have of containing et control interventions can outbreak from Public Health. 2017; PubMed Scopus Google Scholar, et the of Ebola control in 2015; PubMed Scopus Google Scholar, The of public health on the influenza pandemic in PubMed Scopus Google Scholar]. to epidemiological Genomic on the other from a relatively of data to provide insights into of virus between different regions [5.Dudas G. et al.Virus genomes reveal factors that spread and sustained the Ebola epidemic.Nature. 2017; 544: 309-315Crossref PubMed Scopus (204) Google Scholar]. on the the time between of sampling and of of sequence its can many The at which data is and is of the for genomic data for rapid of spread of an infectious disease E. et of genomic surveillance reveals how the SARS-CoV-2 pandemic in 2021; (Published online Google Scholar]. For for SARS-CoV-2 in an of from to of a sequence from the variant of and an of for a the and from While genomic data now for rapid of transmission as new data are et in with application to epidemic 2020; PubMed Scopus Google et on enables for the SARS-CoV-2 2021; PubMed Scopus Google Scholar], their power has to a that of disease spread as new data in and to the time in epidemic The are data and A and of this forecasting was during the of in the E. et of SARS-CoV-2 lineage in 2021; PubMed Scopus (312) Google et genomic of an SARS-CoV-2 lineage in the by a novel of 2020; (Published online Scholar]. investment into or genomic surveillance is to robust from genomic data (e.g., as for in S.K. et surveillance for response in 2020; Full Text Full Text PDF PubMed Scopus Google may not in all and in sampling may especially on an international scale E. et of genomic surveillance reveals how the SARS-CoV-2 pandemic in 2021; (Published online Google Scholar]. to the of from data and how that to optimal sampling frameworks et and in continuous on a 2021; PubMed Google Scholar]. 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