1
SARS-CoV-2 genomic sequencing for public health
goals
Interim guidance
8 January 2021
Key messages:
• Global surveillance of SARS-CoV-2 genetic sequences and related metadata contributes to the COVID-19
outbreak response. This contribution includes tracking the spread of SARS-CoV-2 geographically over time
and ensuring that mutations that could potentially influence pathogenicity, transmission or countermeasures
(such as vaccines, therapeutics and diagnostics) are detected and assessed in a timely manner.
• While the cost and complexity of genetic sequencing have dropped significantly over time, effective sequencing
programmes still require substantial investment in terms of staff, equipment, reagents and bioinformatic
infrastructure. Additionally, effective collaboration is needed to ensure that generated data are of good quality
and are used in a meaningful way.
• Countries are encouraged to rapidly deposit SARS-CoV-2 sequences in a public database in order to share them
with the scientific community for public health purposes. Investments in a tiered global sequencing network for
SARS-CoV-2 will contribute to the development of resilient, high-quality global sequencing programmes for
the detection and management of other outbreak pathogens in the future.
Background
Over the last decade, genetic sequence data (GSD) of pathogens have come to play a pivotal role in the detection and
management of infectious disease outbreaks, supporting the development of diagnostics, drugs and vaccines, and
informing the outbreak response (1–11). With the emergence of the novel coronavirus, later named severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2), the importance of GSD has been further underlined. More than
280 000 full genome sequences have been shared via publicly accessible databases within a year of the initial
identification of SARS-CoV-2 (12). Near real-time analysis of data has directly impacted the public health response
(12–16). The public health objectives of SARS-CoV-2 genomic sequencing are listed in Table 1.
The growing understanding of how sequence information can contribute to improved public health is driving global
investments in sequencing facilities and programmes. The falling cost and complexity of generating GSD provides
opportunities for expanding sequencing capacity; however, challenges to widespread implementation remain, and
sequencing capacity and data are not evenly distributed around the world, with an overrepresentation of SARS-CoV-
2 GSD from high-income countries.
Table 1. Public health objectives of SARS-CoV-2 genomic sequencing
Activities that require a limited effort and
once achieved might need either no
sequencing or occasional sequencing for
follow-up
Activities that require sustained sequencing activities over a longer period of time
- Identify SARS-CoV-2 as the causative agent of
disease.
- Develop diagnostics for SARS-CoV-2.
- Support the development of therapies and
vaccines.
- Investigate date of introduction into humans and
investigate SARS-CoV-2 origin (ongoing).
- Reinfection:
• Evaluate and improve understanding of
this phenomenon.
• On the individual level, differentiate
between prolonged infection and
reinfection.
SARS-CoV-2 evolution and its impact
on:
- Change in viral behaviour (phenotypic
change), e.g., transmissibility or
pathogenicity;
- Immunity (from vaccines or natural
infection);
- Diagnostics (i.e., molecular, serology,
antigen assays);
- Therapeutic interventions (e.g.,
monoclonal antibodies).
Monitor viral movement and activity:
- Investigate geographic spread and reintroductions
between populations.
- Investigate outbreaks in specific settings and
populations (e.g., in hospitals).
- Track zoonotic reintroduction in both directions
over the species barrier.
- Monitor environmental and waste water.
- Support classical surveillance by quantifying the
period of transmission and evaluating drivers, and
by estimating the transmission level in the
population.
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
-2Purpose
of this document
This document provides national-level policy-makers and stakeholders with guidance on how to maximize the public
health benefit of SARS-CoV-2 genomic sequencing activities in the short and long term as the pandemic continues
to unfold. Practical considerations for the implementation of a virus genomic sequencing programme and an
overview of the public health objectives of genomic sequencing are covered. This guidance focuses on SARS-CoV-
2 but is applicable to other pathogens of public health concern. It is recommended that countries wishing to build
sequencing capacity for SARS-CoV-2 do so as part of a broader plan to build capacity to detect and monitor other
pathogens of public health concern.
Additional WHO guidance
WHO has developed the implementation guide Genomic sequencing of SARS-CoV-2: a guide to implementation for
maximum impact on public health in collaboration with sequencing experts around the world. This guide provides a
more complete background on SARS-CoV-2 sequencing and is intended for those who are actively involved in
implementing sequencing programmes (17). It provides an in-depth review of the various uses of sequencing and
gives technical advice on pathogen sequencing in the context of SARS-CoV-2. Beyond reading these and other
published documents, laboratories with limited sequencing experience should actively look for opportunities to
collaborate with experienced laboratories and/or join or form laboratory networks with sequencing expertise.
1. Introduction to SARS-CoV-2
SARS-CoV-2 is classified within the genus Betacoronavirus (subgenus Sarbecovirus) of the family Coronaviridae
(18). It is an enveloped, positive-sense, single-stranded ribonucleic acid (RNA) virus with an approximately 30kb
genome (19). Genetic sequencing enables the reading of viral genomes. As each pathogen has a unique genomic
sequence, this method can be used to identify novel pathogens (as in the case of SARS-CoV-2) (20). The SARSCoV-2
genome encodes for non-structural proteins, four structural proteins (spike [S], envelope [E], membrane [M],
nucleocapsid [N]) and several putative accessory proteins (21–23). SARS-CoV-2 host cell entry requires binding of
the viral S protein to the host cell angiotensin-converting enzyme 2 (ACE-2) receptor (24–27). The SARS-CoV-2
spike protein, especially the receptor-binding domain (RBD), is a critical target for natural and vaccine-induced
immunity (28–32). Therefore, diversification of the gene encoding the spike protein could potentially impact vaccine
efficacy, natural immunity and (monoclonal) antibody therapies (33).
When viruses replicate, especially RNA viruses such as SARS-CoV-2, changes (mutations) occur in the genome. If
an acquired mutation does not have an evolutionary disadvantage, it may become fixed in SARS-CoV-2 populations.
The rate of evolutionary change in SARS-CoV-2 is currently estimated to be 1 x 10-3
substitutions per site per year
at the nucleotide level (34). This translates into approximately one substitution in the genome every two weeks (35).
This relatively low rate of evolution limits the time resolution of individual transmission events (35). Studying
SARS-CoV-2 evolution and rapidly identifying substitutions, insertions or deletions that could impact viral
properties (phenotypic change) is an important tool for epidemic monitoring. Among the most obvious yields of such
work is the detection of mutations that are associated with changes in the transmissibility and/or pathogenicity of the
virus, or that could reduce the utility of medical countermeasures (diagnostics, vaccines and therapeutics). Following
virus mutations over time and space can also help to track the spread of the pathogen and support an enhanced
understanding of potential transmission routes and dynamics. Reconstructing the evolutionary history of pathogens
can be achieved through phylogenetic analysis. Phylogenetic and phylodynamic (i.e., how virus phylogenies are
shaped by epidemiological and evolutionary processes) analyses can provide extensive information to support
outbreak response.
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
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2. Establishing an optimal SARS-CoV-2 sequencing approach in the local context
2.1 Context-specific prioritization of sequencing objectives and approach
Although the cost of gene sequencing has fallen significantly over the past decades, sequencing still requires
substantial investment in resources (financial, infrastructure and human). Before initiating a sequencing project, the
critical first step is to determine whether sequencing is truly valuable for reaching a specific objective or whether
there are other more time-effective or cost-effective approaches available. This decision may involve considering
whether virus sequencing alone is sufficient to reach the defined objective or whether it should be included as a
smaller component within a multidisciplinary approach. Epidemiologically focused activities that integrate genomic
data analysts directly into public health investigation and response teams are likely to have a greater immediate
impact than those in which virus genomic analysis exists as a separate or secondary activity.
Where resources to support sequencing are limited, it may be necessary to limit objectives of a sequencing
programme to those activities with high clinical and/or public health potential, which can be sustained. Such a
programme may prioritize the sequencing of SARS-CoV-2 i) from individuals vaccinated for SARS-CoV-2 but who
later become infected with SARS-CoV-2 despite exhibiting an appropriate immune response to the vaccine; ii) in
risk settings, such as where there is close human–animal interaction with a large number of animals that are
susceptible to SARS-CoV-2 infection, or where there are immunocompromised patients with prolonged shedding,
especially when receiving antibody therapy against SARS-CoV-2; iii) when there is an unexpected increase or
change in SARS-CoV-2 transmissibility and/or virulence; iv) when there is suspicion of a change in the performance
of diagnostic (antibody, antigen, molecular assays) methods or therapies; and v) during cluster investigations when
sequencing can support understanding of transmission events and/or evaluate the efficacy of infection control
procedures.
Fig. 1 provides an overview of the basic pillars required for sequencing. If there is no or limited capacity available
in all three pillars, it will likely be necessary to build partnerships with other groups in order to achieve sequencing
goals. Conversely, if there is adequate capacity and resources for one or more pillars, the laboratory could consider
supporting other partners with nascent sequencing programmes. Variable demand on capacity will occur throughout
different phases of an outbreak and might require laboratories to shift from one strategy to another.
Fig. 1. Establishing capacity in the pillars required for sequencing in order to select the best approach for
the setup of a sequencing programme
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
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2.2 Investing in global sustainable sequencing capacity for SARS-CoV-2 and other (emerging/re-emerging)
pathogens of public health concern
Tiered WHO laboratory networks have proven functionality and enable global collaboration, with regional
adaptation of networks to specific national and regional needs (36–39). Building such a strong and resilient global
sequencing network can maximize the public health impact of sequencing for SARS-CoV-2 and emerging/reemerging
pathogens. Currently, the WHO reference laboratories providing confirmatory testing for COVID-19 are
supporting some of these sequencing and analysis needs (40). Several regions have, or are in the process of
developing, sequencing capacities that will be able to join the global network of laboratories/sequencing groups. To
determine the contribution laboratories in the network can make, a global estimation of capacity for each pillar listed
in Fig. 1 can be undertaken. Various pathogen-specific laboratory networks (such as those working on antimicrobial
resistance, MERS-CoV, influenza, measles, rubella, poliovirus and tuberculosis) have invested in sequencing
capacity as part of their surveillance activities (8, 9, 41–43). As the costs of sequencing are still substantial and many
parts of the sequencing workstream can be used for various pathogens or sequencing objectives, national
collaboration to ensure the optimal use of existing capacity is encouraged. Capacity-building programmes should
focus on a stepwise approach to build competencies. The priorities of capacity building should be context-dependent.
For some countries, building wet-laboratory capacity would make sense, whereas in other settings, outsourcing the
actual sequencing and focusing on bioinformatics, data management and interpretation would have a bigger impact.
For effective collaboration, data sharing, standardized protocols for sequencing, joint meetings and training, audits,
proficiency testing (sequencing and analysis) and the development of reference standards for the evaluation of
different procedures will support the further development of high-quality sequencing programmes for SARS-CoV-2
and for the detection of and response to future emerging pathogens. Where samples are shared in a network,
appropriate mechanisms to ship samples under adequate requirements should also be in place.
3. Practical considerations for the implementation of a virus genomic sequencing programme
Here, we provide a general overview of the technical requirements to establish a sequencing programme. For detailed
information regarding SARS-CoV-2 sequencing, refer to the full SARS-CoV-2 sequencing implementation guide
(17).
3.1 Practical considerations when developing a SARS-CoV-2 sequencing programme
The sequencing objectives will determine the design of the sequencing workstream (Table 1). Relevant key questions
to aid this process can be found in Annex I. Annex II contains a checklist with considerations for planning a SARSCoV-2
sequencing programme. Fig. 2 depicts the workstream for SARS-CoV-2 whole genome sequencing. All staff
involved in a sequencing programme should receive the appropriate training and instruction to comply with the
mandated task. Key stakeholders should be identified, consulted and involved at an early stage. Stakeholders to be
engaged when developing sequencing programmes include public health bodies, diagnostic laboratories, sequencing
facilities, analytical groups, and, depending on the setting, infection prevention and control teams or occupational
health services, patient advocacy groups, and other institutions involved in human–animal interface research where
appropriate. Communication channels and pathways that are aligned with the objectives of the programme should
be developed and maintained throughout the project in order to ensure that the sequencing data are used to the greatest
effect. Regular evaluations of the project’s progress and end evaluation are key to ensure that lessons are learned and
improvements are made where needed. Successfully achieving the objectives of a sequencing programme focused
on emerging pathogens requires the involvement of experts in different fields: (i) wet-laboratory sequencing and safe
handling of virus samples; (ii) generation of accurate genomes from raw data; (iii) analysis of genomes to generate
meaningful results that are useful for the outbreak response; and (iv) pathogens. Most experts will be skilled in only
one or two of these areas. For (ii) and (iii), powerful computer resources are required to achieve fast results.
Collaboration between experts with different skillsets and pooling of resources is therefore often key to generate
timely, accurate and effective results that can truly impact public health.
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
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2. Workstream for SARS-CoV-2 whole genome sequencing. Note that successfully implementing this
workstream will involve bilateral communication between experts involved at different stages; for example,
those conducting data interpretation would ideally directly discuss which samples will be chosen from
sequencing with those involved in sample selection and preparation.
3.2 Ethical considerations
It is important to review ethical implications when designing a sequencing programme. Possible risks of social harm
to research participants should be identified, and mitigation strategies should be defined. Any proposed investigations
should be evaluated and approved by an ethical review committee, which takes into account the social value,
scientific validity, participant selection, risk–benefit ratio, informed consent, and ongoing respect for participants
(44–46). Where researchers are not experienced in identifying possible ethical issues surrounding the sequencing of
outbreak pathogens such as SARS-CoV-2, international collaboration and engagement of such expertise is strongly
encouraged (44). Collaboration between researchers across the world should ensure equitable and mutually beneficial
collaborative research partnerships. Local researchers should be encouraged to take leading and active roles
throughout the research process, as they are more likely to understand their health care and research systems and be
able to translate results into policy (44, 45). Ethical considerations for genomic sequence and metadata sharing are
discussed in section 3.7.
3.3 Considerations for sampling strategy and sample preparation
Once goals have been identified, an appropriate sampling strategy needs to be developed with relevant stakeholders.
Details on sampling can be found in the SARS-CoV-2 sequencing implementation guide (17). Ideally, the reasons
for the choice of specimens for sequencing should be recorded in the metadata, as the inclusion of non-random
subsets of samples can affect the reliability of certain genetic analyses such as phylogenetic and phylodynamic
analyses. Practical advice on how to collect clinical samples is covered in the SARS-CoV-2 diagnostic guidance (47).
Before sequencing, it is recommended to enrich the sample for SARS-CoV-2 genetic material relative to other
genetic material. In this step, take care not to contaminate the sample (17, 48, 49). PCR-based approaches are an
inexpensive, rapid and convenient way of increasing the amount of virus genetic material available in a sample prior
to sequencing, for example, the approach designed by the ARTIC Network (51–53). For more technical details and
how to select the optimal method for different settings, we refer to the SARS-CoV-2 sequencing implementation
guide (17). After initial sample preparation to enrich the SARS-CoV-2 genetic material, libraries can typically be
prepared using standard sequencing protocols that are appropriate for any virus.
3.4 Laboratory considerations
Sequencing strategies for SARS-CoV-2 include targeted approaches that rely on knowledge of the genome, and
metagenomic approaches that do not require prior knowledge of the genomic sequence (54, 55). Annex III
summarizes the key advantages and limitations of each commonly used sequencing technology. Before investing in
sequencing capacity, consideration should be given to the requirements of the various technologies in terms of human
resources, staff competencies, laboratory infrastructure, run-time, costs, ease of use, subsequent data processing,
throughput (rate of data production) and sequencing accuracy. The number of samples that need to be analysed will
depend on the sequencing objective. When calculating costs, consider not only the procurement of sequencing
equipment, but also the recurrent costs for reagents, maintenance and service contracts. This guidance does not cover
costs, but an extensive recent overview can be found in (8). Basic infrastructure should be in place to support reliable
sequencing, including reliable Internet connection and electricity supply, appropriate environment (e.g., vibration
and dust free, temperature and humidity logged and regulated required for some platforms), and logged storage of
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
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Appropriate biosafety and biosecurity measures should be implemented. Assessing the costs and basic
infrastructure requirements can help to decide whether the actual sequencing should be done in-house or would be
better outsourced. Technology changes rapidly; consequently, certain techniques will become obsolete or
manufacturers will shift to different machines and/or reagents. Before making large investments, it is recommended
to establish how long the manufacturer will commit to supplying reagents and supporting the maintenance and
troubleshooting of the selected platforms of interest. When planning a programme, the availability of ancillary
reagents and additional equipment to support the sequencing work should also be taken into account (e.g., extraction
methods [either automated or manual], instruments to quantify genetic material, amplification and incubation
instruments, sample purification, and sample and reagent storage). Laboratories that conduct genomic sequencing
should have high-quality SARS-CoV-2 PCR capacity confirmed by internal and external quality assurance. In
addition, for each step in the process, quality indicators should be established and monitored.
3.5 Bioinformatic and computational considerations
Hardware requirements differ depending of the approach taken (for details, we refer to the implementation guide
(17)). The volume of raw data produced depends on the sequencing method (see Annex III) and the number of
samples sequenced (56). The computational power required for data analysis also differs according to the sequencing
objective and method. For example, genome phylogenetics and alignment may require high-performance
computational power, especially where datasets are large. The costs of the computational architecture required to
store and handle these data should be considered when developing a sequencing pipeline. The bioinformatic pipeline
will depend on the pre-sequencing laboratory stages, sequencing platform, and reagents used. For a detailed
description of bioinformatic pipelines, we refer to the implementation guidance (17).
3.6 Considerations for virus naming and nomenclature
A consistent nomenclature has not yet been established for SARS-CoV-2. In the absence of an agreed upon consistent
nomenclature, three main nomenclature strategies are generally used. Lineages or clades can be defined based on
viruses that share a phylogenetically determined common ancestor. Both GISAID and Nextstrain aim to provide a
broad categorization of globally circulating diversity by naming different phylogenetic clades. Rambaut et al.
proposed a dynamic nomenclature for SARS-CoV-2 lineages that focuses on actively circulating virus lineages and
those that spread to new locations (57). Software is available to automatically assign new sequences to a lineage
and/or clade (58–60). With the increasing diversity in SARS-CoV-2 genomes, the demand for a uniform
nomenclature is growing (57, 61, 62). While no consistent nomenclature exists, the best approach would be to list
particular lineages and/or clades using all three of the commonly used systems, or at minimum state explicitly which
nomenclature is being used.
3.7 Genomic sequence and metadata sharing
The rapid sharing of pathogen GSD, together with the relevant anonymized epidemiological and clinical metadata,
will maximize the impact of genomic sequencing in the public health response (63–65). The wide sharing of SARSCoV-2
sequences, as well as diagnostic protocols, sequencing protocols and samples, has been globally beneficial to
achieving worldwide molecular diagnostic capacity (66–68). The scientific/medical community should continue to
build on the global collaboration and timely data sharing during SARS-CoV-2 and future emerging outbreaks. There
are two distinct choices available for SARS-CoV-2 genomic sequence data sharing: “public-domain” and “publicaccess”
(69). Public-domain databases provide access to data without requiring the identity of those accessing and
using the data, for example, the INSDC, operated by DDBJ, EMBL-EBI and NCBI. In public-access databases, such
as GISAID, users must identify themselves to ensure transparent use of the data, permit effective oversight, protect
the rights of the data contributors, make best efforts to collaborate with data providers, and acknowledge their
contribution in published results. The examples mentioned are free of charge and accessible to the public. When
pathogen sequencing projects are developed, it is imperative to determine which, if either, of these choices is most
appropriate and whether other methods to access and share GSD are necessary (44). One of the critical factors to
ensure continued sharing of genetic data is giving due acknowledgement to those who collect clinical samples and
generate virus genomic sequences. Data sources should always be acknowledged where publicly available data are
used, and related publications and pre-print articles should be cited where available.
Sequence data, including consensus sequences, partial consensus sequences and raw sequence data, can be valuably
shared in multiple formats. The quality of the sequence data, including potential contamination with amplicons
produced through PCR, should be carefully evaluated prior to sharing. Laboratories should contact sequence-sharing
platforms to update previously submitted partial sequences if an error is identified and corrected. Sharing of raw
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
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sequencing reads (i.e., all individual sequenced fragments of a virus genome before they are assembled into
one consensus genome) is important because it enables the direct comparison of the effect of different bioinformatic
approaches for consensus genome generation and facilitates the correction of errors if necessary. Given the large
data size of sequenced libraries, the sharing of read-level data may be more challenging in settings that have limited
Internet upload speeds or intermittent connections. Any shared data should protect patient anonymity. To ensure
patient anonymity, raw data containing human reads must be filtered to retain only non-human (i.e., viral) GSD prior
to sharing (43). Sharing of linked metadata, such as date of sample collection or approximate sampling location, is
necessary to enable sequence data to be used in many phylogenetic applications. However, which metadata can be
reasonably shared without compromising patient anonymity should be carefully considered.
Preliminary analyses of GSD are frequently shared through forums, platforms and preprint servers (70–72).
Through their publications, as with all scientific reports, scientists should consider the strengths and weaknesses of
their analyses and how analyses might be interpreted or presented by various audiences prior to peer-review.
Scientists are encouraged to provide a clear interpretation of their findings so that misunderstandings or misuse of
results are minimised.
4. Public health objectives of SARS-CoV-2 genomic sequencing
Summarized below are examples of the key public health objectives of SARS-CoV-2 sequencing; for detailed
descriptions, please refer to the SARS-CoV-2 sequencing implementation guide (17).
4.1 Identification and characterization of SARS-CoV-2 and development of countermeasures
The sharing of the complete genetic sequence of the novel virus in early January 2020 was fundamental to
characterize SARS-CoV-2, enabling the rapid development of diagnostics and supporting the development of
therapies and vaccines (73–80). Genomic sequencing enhances our understanding of the origins and transmission of
novel viruses. By studying the initial SARS-CoV-2 genomes available from Wuhan, People’s Republic of China and
surrounding areas, it was possible to determine the latest possible date of emergence in humans as November–
December 2019 (74, 75, 81, 82). Sampling of a wide range of animals is supporting research around the identification
of the initial animal source and/or potential intermediate hosts (81, 83, 84).
4.2 Monitoring transmission and geographic spread
Phylogenetics is a method for investigating evolutionary relationships between different organisms using their
genetic sequences. It is used in almost every branch of biology and has many important applications in informing
public health responses (17, 85–87). The availability of epidemiological or clinical data related to the sampling of
the virus genomic sequence (often referred to as metadata, e.g., date of sampling, location of patient, clinical
parameters) enhances the interpretation of phylogenetic analyses. Which metadata are required differs according to
the objective of the genomic sequencing. The technical aspects of phylogenetic and phylodynamic analyses, metadata
and common risks of misinterpretation can be found in the SARS-CoV-2 sequencing implementation guide (17).
4.2.1 Investigating geographical spread and reintroductions between populations
Phylogeographic analyses that use virus genomic sequences and information on sampling location are being used to
track SARS-CoV-2 circulation globally (13, 47, 88–90). Phylogeographic reconstructions are often computationally
demanding, and subsampling strategies can help to reduce this computational burden. Inferring virus movement or
country of origin of specific clades/lineages can be valuable, but should be done with caution because several factors
can bias phylogeographic reconstruction. For example, the lack of available SARS-CoV-2 genomes from certain
areas can make it less likely that those areas will be reconstructed as the geographic origin of a lineage/clade.
Genomic sequences may be associated in some databases with the location of virus sampling, instead of with the
suspected location of infection of a patient. Where these locations differ because a patient travelled between the times
of infection and virus sampling, phylogeographic analysis can result in an inaccurate reconstruction of the origin of
specific clades/lineages (91). These results should be interpreted cautiously and not under the assumption that
phylogeographic results represent the true patterns of spatio-temporal (time and space) viral spread.
Methods to infer spatio-temporal spread of an outbreak can also be used to investigate factors that have driven virus
dispersal (92). Identifying drivers of transmission may help to shape new strategies for preventing spread. This
approach has been used, for example, in the Ebola virus disease outbreaks in West Africa (93, 94). For SARS-CoV-
2, several countries have used genomic sequencing to establish the contribution of local transmission compared to
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
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cases, and used this information to help make policy decisions (89, 90, 95–100). The phylodynamic
identification of factors that are important for understanding transmission is often computationally demanding and
requires the curation of extensive data on potential explanatory factors (e.g., human population density, human
mobility). Analyses are therefore often completed weeks or months after virus genomic sequencing. However, even
retrospective analyses are useful to guide interventions for SARS-CoV-2 or potential emerging pathogens.
4.2.2 Evaluating evidence on transmission routes or clusters
Phylogenetic clustering has been used to support cluster and outbreak investigations for SARS-CoV-2. Analyses of
transmission clusters can guide local decisions on whether control measures are needed to prevent future transmission
in identified outbreak settings (101). Given the relatively slow evolutionary rate of SARS-CoV-2 (i.e., one nucleotide
substitution every two weeks), it is expected that many individual transmission events will not be traceable based on
genomic sequence data (35). Phylogenetic clustering of sequences from patients with the same hypothesised source
of exposure would be consistent with (although not strong evidence of) that exposure. By contrast, phylogenetic
separation of virus sequences from patients with the same hypothesised source of exposure would strongly indicate
that the common source of infection has been incorrectly identified.
4.2.3 Quantifying periods of transmission and following the reproduction number over time
Molecular clock phylogenetic approaches can help to estimate the upper and lower limits of time of circulation of
the sampled virus’s genetic lineages in a given population (74, 90, 102–106). This approach can provide more
accurate information on the period of viral transmission than the clinical identification of cases, particularly in the
early or late phases of an outbreak when surveillance is limited. Studying the variation in the genomic sequences
detected can determine whether there is clinically undetected local transmission. In these settings, improved
diagnostic surveillance programmes would need to be implemented where undetected circulation is suspected.
Genomic sequence analysis can also estimate how many individuals are infected by one individual in a given
population (the reproduction number [R0]) and support the assessment of relative changes in outbreak size over time.
This information could be used to evaluate the impact of specific control measures.
4.2.4 Environmental surveillance in wastewater and sludge
For pathogens such as poliovirus, wastewater monitoring is an important tool for tracing the silent circulation of
viruses in a community. This approach provides opportunities to detect circulation (before the initial patients have
been clinically detected), estimate prevalence, and understand the genetic linkage and diversity (107, 108). Several
countries have demonstrated molecular detection of SARS-CoV-2 RNA in wastewater (109–115). Consequently,
environmental surveillance is a promising approach, especially in low prevalence settings, to identify unrecognized
carriers and serve as an “early warning” system for SARS-CoV-2 introduction or changes in prevalence (109, 116,
117).
4.2.5 Investigating potential reinfections
Seasonal coronaviruses can reinfect humans (118). For SARS-CoV-2, cases of reinfection have been documented
(119–124). In this context, the SARS-CoV-2 genomic sequences sampled from the first and subsequent episodes can
be compared to determine whether the renewed detection of SARS-CoV-2 in an individual is a reinfection or the
result of prolonged viral shedding (125, 126). If sequences from each episode have strong genetic distinctions, such
as occurring in different, well-supported lineages/clades, subsequent episodes can be considered to be reinfections.
Concomitant serological investigations are necessary to understand whether reinfection is associated with an
antigenically distinct strain or the lack of a protective immune response from the initial infection. Sequencing can
therefore support the improved understanding of the frequency and potential risk factors for reinfection (125, 126).
4.3 Monitoring the evolution of SARS-CoV-2
4.3.1 Structured evaluation of possibly relevant mutations
Genomic sequencing can be used to identify genetic substitutions that may change viral infection characteristics
(phenotypic change), such as transmissibility or virulence. All viruses acquire genetic changes as they circulate, but
the vast majority of acquired changes do not substantially affect virus behaviour. Nevertheless, rare genetic changes
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
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SARS-CoV-2 may cause relevant phenotypic changes of public health importance. Identifying and demonstrating
the impact of such changes is challenging. In general, it is difficult to confidently establish whether the increase in
relative prevalence of given mutation(s) over time is due to a phenotypic difference. The predominance of a specific
viral clade/lineage in a population, for example, may be due more to the behaviour of the human population infected
than to the behaviour of the virus itself. Most of the time, such patterns are likely to be stochastic. However, if
phylogenetic analysis hints at the potential epidemiological or clinical impact of specific mutations/variants, properly
conducted clinical genomic studies are required in order to evaluate candidate variants that might confer clinically
observed phenotypic changes to the virus. Genetic changes proposed to cause phenotypic changes should be
evaluated using standardized approaches, including protein modelling studies to assess potential impact and in vitro
or in vivo experiments with a mutant virus (clones) with the specific mutations of concern to confirm or reject the
specific properties of the candidate variants. A dedicated WHO Working Group has been established, derived from
the WHO SARS-CoV-2 Reference Laboratory Network. This SARS-CoV-2 Evolution Working Group (SEWG)
focuses on SARS-CoV-2 evolution to provide WHO with the timely identification and evaluation of potentially
relevant mutations, as well as advice for risk mitigation (16, 40, 127).
4.3.2 Monitoring the impact of SARS-CoV-2 evolution on countermeasures
At minimum, global surveillance of SARS-CoV-2 genomes should ideally detect the emergence of SARS-CoV-2
lineages with genetic variants that impact the effectiveness of countermeasures. Monitoring for SARS-CoV-2
genomic changes that might reduce vaccine efficacy should accompany the roll-out of SARS-CoV-2 vaccination
campaigns. Monitoring and investigation into the possible causes of vaccine failure should include genomic
evaluations to assess potential viral escape mutants. Additionally, sequencing can aid in the identification of escape
mutants for monoclonal antibodies (128) and future therapeutics. Genomic monitoring to identify drug resistance
has been used for other pathogens, including influenza, HIV and Mycobacterium tuberculosis (9, 129).
Genomic sequencing can also be used to monitor viral genetic changes that affect molecular diagnostics. Using
multiple targets for SARS-CoV-2 detection, such as a multiplex PCR targeted at two or more regions of the virus
genome, is a cost-effective approach to reduce the chance of false negatives in assays as a result of the virus’s
evolution (47,127). Sequencing of the virus genome or target gene could be undertaken when there is consistent
failure to detect one target or newly observed differences in the sensitivity of assays targeting different regions in
order to identify the possible cause. For additional information, see the interim guidance on the diagnosis of SARSCoV-2
infections (47). Viral mutations can also impact the antigen or serological assay, and genomic sequencing
can help to detect the potential failure of such assays at an early stage (130–132).
4.3.3 SARS-CoV-2 evolution at the human–animal interface
When a virus transmits from one species to another, the virus may adapt to its new host. The ACE-2 receptor that is
targeted by SARS-CoV-2 is similar across humans and a wide variety of animals (mostly mammals) (133, 134).
There is therefore potential for human to animal transmission (anthroponosis). While ACE-2 homology suggests that
other animals may be susceptible to SARS-CoV-2, other proteins critical for viral replication might differ and prevent
infections in these candidate animals. Therefore, appropriate real-world or experimental infection data are needed to
determine the susceptibility of specific animals. Various animals have been shown to be susceptible to SARS-CoV-
2 (15, 127, 135–151), and SARS-CoV-2 is known to be transmissible in certain animal species (e.g., minks and
hamsters). The resistance of some animal species to infection with SARS-CoV-2 has also been demonstrated. Genetic
changes in the sequence that encodes the viral spike protein that binds to the ACE-2 receptors may emerge and
facilitate a jump to new host species. The SARS-CoV-2 spike protein, especially the RBD, is a critical target for
natural and vaccine-induced immunity (28–32). Diversification of genomic regions encoding the spike protein has
already been observed in cases where humans infected with SARS-CoV-2 have infected minks and there has been
secondary zoonotic transmission back to humans (149). Therefore, diversification of the spike gene following the
human–animal exchange of SARS-CoV-2 likely increases the risk of strains emerging that can easily reinfect humans
and may be associated with reduced vaccine efficacy or amenability to monoclonal antibody therapies (33). To
prevent these events from occurring, countries are encouraged to conduct risk assessments on the potential spread to
other species living with or near humans in domestic, rural, agricultural or other zoological settings (127, 152–154).
Risk mitigation strategies need to be established and adequate monitoring is needed to ensure the timely detection of
these events. Monitoring requires resources, and targeted strategies should be adopted where possible. This requires
a One Health strategy in which different disciplines work together, including public, clinical and occupational health,
veterinary and wildlife authorities, and forestry and natural resources management (127, 152, 155–157). This
collaboration should also focus on the development of joint outbreak investigation and infection prevention and
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
-10control
protocols, testing of potentially infected humans and animals, and the sharing of sequence data. Where
anthroponotic infection or secondary zoonotic infection is observed, sequencing of virus genomes can help to
evaluate the possible new risks associated with these events.
Methods
This interim guidance was developed in conjunction with the implementation guide Genomic sequencing of SARSCoV-2:
a guide to implementation for maximum impact on public health. The implementation guide was developed
in consultation with experts with experience in the various fields of genomic sequencing from the Global Laboratory
Alliance of High Threat Pathogens (GLAD-HP), the reference network for confirmatory testing for COVID-19, and
the Global Outbreak Alert and Response Network (GOARN). After initial discussions by a technical writing group
led by a temporary advisor and members of the WHO COVID-19 Laboratory Team, contributions were sought from
other experts within and outside WHO, and two online meetings were held to resolve outstanding questions.
Subsequently, this interim guidance was drafted for national stakeholders, containing a summary of relevant
information from this interim guidance and additional relevant information for this target audience. This interim
guidance was subsequently circulated for input to the experts that supported the drafting of the implementation guide,
the reference network for confirmatory testing for COVID-19, the regional laboratory focal points and other
stakeholders, as listed in the acknowledgements.
Plans for updating
WHO continues to monitor the situation closely for any changes that may affect this interim guidance. Should any
factors change, WHO will issue a further update. Otherwise, this interim guidance will expire one year after the date
of publication.
Contributors
WHO steering group: Celine Barnadas; Sebastian Cognat; Roger Evans; Bruce Allan Gordon; Varja Grabovac;
Rebecca Grant; Francis Inbanathan; Frank Konings; Karen Nahapetyan; Marco Marklewitz; Marie-jo Medina; Kate
Olive Medlicott; Mick Mulders; Mark D Perkins; Magdi Samaan; Oliver Schmoll, Maria Van Kerkhove; Karin von
Eije; Joanna Zwetyenga.
External contributors: Kim Benschop, Netherlands National Institute for Public Health and the Environment (RIVM),
Bilthoven, Netherlands; Antonino di Caro, Instituto Nazionale per le Malattie Infettive Lazzaro Spallanzani, Italy;
Nuno Rodrigues Faria, Imperial College, London and University of Oxford, Oxford, United Kingdom; Tanya
Golubchik, University of Oxford, Oxford, United Kingdom; Keith Hamilton, World Organization for Animal Health
(OIE); Edward Holmes, University of Sydney, Sydney, Australia; Sarah C Hill, Royal Veterinary College, London
and University of Oxford, Oxford, United Kingdom; Erik Karlsson, Institut Pasteur de Cambodge, Cambodia; Meng
Ling Moi, Nagasaki University, Nagasaki, Japan; Leo Poon, Hong Kong University, Hong Kong Special
Administrative Region (SAR), China; James Shepherd, University of Glasgow, Glasgow, United Kingdom; Etienne
Simon-Loriere, Pasteur Institute, Paris, France. And the other experts that contributed to the implementation guide
for SARS-CoV-2 sequencing that served as the basis for this document: Kristian Andersen, Scripps Research, La
Jolla, CA, USA; Julio Croda, Ministry of Health, Rio de Janeiro, Brazil; Túlio de Oliveira, University of KwaZuluNatal,
Durban, South Africa; Simon Dellicour, Free University of Brussels, Brussels, Belgium; Nathan Grubaugh,
Yale University, New Haven, CT, USA; Liana Kafetzopoulou, KU Leuven – University of Leuven, Belgium;
Marion Koopmans, Erasmus MC, Rotterdam, Netherlands; Tommy Lam, University of Hong Kong , Hong Kong
SAR, China; Philippe Lemey, KU Leuven – University of Leuven, Belgium; Tze Minn Mak, National Centre for
Infectious Diseases, Singapore; Marcio Roberto Nunes, Evandro Chagas Institute, Ananindeua, Pará, Brazil; Bas
Oude Munnink, Erasmus MC, Rotterdam, Netherlands; Gustavo Palacios, United States Agency for International
Development, Washington, DC, USA; Steven Pullan, Public Health England, London, United Kingdom; Timothy
Vaughan, Eidgenössische Technische Hochschule Zurich (ETH Zurich), Zurich, Switzerland; Josh Quick, University
of Birmingham, Birmingham, United Kingdom; Andrew Rambaut, University of Edinburgh, Edinburgh, United
Kingdom; Chantal Reusken, RIVM, Bilthoven, Netherlands; Tanja Stadler, Eidgenössische Technische Hochschule
Zurich (ETH Zurich), Switzerland; Marc Suchard, University of California at Los Angeles, Los Angeles, CA, USA;
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
-11Huaiyu
Tian, Beijing Normal University, Beijing, China; Lia van der Hoek, Amsterdam Medical Centre, Amsterdam,
Netherlands; Erik Volz, Imperial College, London, United Kingdom.
Declarations of interest
All contributors submitted declaration of interest documents for review. Contributors who were determined to have
a potential conflict of interest or bias toward specific products were excluded from advising on platform selection.
Funder
Funded by WHO
References
1. Fraser C, Donnelly CA, Cauchemez S, Hanage WP, Van Kerkhove MD, Hollingsworth TD, et al. Pandemic potential of a strain of
influenza A (H1N1): early findings. Science. 2009;324:1557–61. doi: 10.1126/science.1176062.
2. Rambaut A, Holmes E. The early molecular epidemiology of the swine-origin A/H1N1 human influenza pandemic. PLoS Curr.
2009;1:RRN1003. doi: 10.1371/currents.rrn1003.
3. Smith GJD, Vijaykrishna D, Bahl J, Lycett SJ, Worobey M, Pybus OG, et al. Origins and evolutionary genomics of the 2009 swine-origin
H1N1 influenza A epidemic. Nature. 2009;459:1122–5. doi: 10.1038/nature08182.
4. Mena I, Nelson MI, Quezada-Monroy F, Dutta J, Cortes-Fernández R, Lara-Puente JH, et al. Origins of the 2009 H1N1 influenza
pandemic in swine in Mexico. eLife. 2016;5:e16777. doi: 10.7554/eLife.16777.
5. Ladner JT, Wiley MR, Mate S, Dudas G, Prieto K, Lovett S, et al. Evolution and spread of Ebola virus in Liberia, 2014–2015. Cell Host
Microbe. 2015;18:659–69. doi: 10.1016/j.chom.2015.11.008.
6. Stadler T, Kühnert D, Rasmussen DA, Plessis dL. Insights into the early epidemic spread of Ebola in Sierra Leone provided by viral
sequence data. PLoS Curr. 2014;6. doi: 10.1371/currents.outbreaks.02bc6d927ecee7bbd33532ec8ba6a25f.
7. Smits SL, Pas SD, Reusken CB, Haagmans BL, Pertile P, Cancedda C, et al. Genotypic anomaly in Ebola virus strains circulating in
Magazine Wharf area, Freetown, Sierra Leone, 2015. Euro Surveill. 2015;20. doi: 10.2807/1560-7917.ES.2015.20.40.30035.
8. GLASS whole-genome sequencing for surveillance of antimicrobioal resistance. Geneva: World Health Organization; 2020
(https://apps.who.int/iris/handle/10665/334354, accessed 20 November 2020).
9. The use of next-generation sequencing technologies for the detection of mutations associated with drug resistance in
Mycobacterium tuberculosis complex: technical guidance. Geneva: World Health Organization; 2018
(https://apps.who.int/iris/handle/10665/274443, accessed 15 November 2020).
10. Whole genome sequencing for foodborne disease surveillance: landscape paper. Geneva: World Health Organization; 2018
(https://apps.who.int/iris/handle/10665/272430, accessed 25 November 2020).
11. Next-generation sequencing of influenza viruses: general information for national influenza centres. Geneva: World Health
Organization; 2020 (https://www.who.int/influenza/gisrs_laboratory/national_influenza_centres/NGS_guidance_for_NICs.pdf?ua=1,
accessed 20 November 2020).
12. GISAID (https://www.gisaid.org/, accessed 5 January 2021).
13. Genomic epidemiology of novel coronavirus: global subsampling [website]. Nextstrain; 2020 (https://nextstrain.org/ncov/global,
accessed 4 December 2020).
14. Volz E, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunuba Z, et al. Report 5 - phylogenetic analysis of SARS-CoV-2. London: Imperial
College London; 2020 (http://www.imperial.ac.uk/medicine/departments/school-public-health/infectious-diseaseepidemiology/mrc-global-infectious-disease-analysis/covid-19/report-5-phylogenetics-of-sars-cov-2/,
accessed 26 June 2020).
15. Oude Munnink BB, Sikkema RS, Nieuwenhuijse DF, Molenaar RJ, Munger E, Molenkamp R, et al. Transmission of SARS-CoV-2 on mink
farms between humans and mink and back to humans. Science. 2020. doi: 10.1126/science.abe5901.
16. Coronavirus disease (COVID-19): situation report – 185. Geneva: World Health Organization; 2020
(https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200723-covid-19-sitrep-185.pdf?sfvrsn=9395b7bf_2,
accessed 15 November 2020).
17. Genomic sequencing of SARS-CoV-2: a guide to implementation for maximum impact on public health. Geneva: World Health
Organization; 2020. (https://apps.who.int/iris/bitstream/handle/10665/338480/9789240018440-eng.pdf?sequence=1&isAllowed=y,
accessed 8 January 2021)
18. International Committee on Taxonomy of Viruses (ICTV); 2020 (https://talk.ictvonline.org/, accessed 27 July 2020).
19. Gorbalenya ABS, Baric R, de Groot R, Drosten C, Gulyaeva A, Haagmans B, et al. The species Severe acute respiratory syndrome-related
coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol. 2020;5:536–44. doi: 10.1038/s41564-020-0695-z.
20. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med.
2020;382:727–33. doi: 10.1056/NEJMoa2001017.
21. Naqvi AAT, Fatima K, Mohammad T, Fatima U, Singh IK, Singh A, et al. Insights into SARS-CoV-2 genome, structure, evolution,
pathogenesis and therapies: structural genomics approach. Biochim Biophys Acta Mol Basis Dis. 2020;1866:165878. doi:
10.1016/j.bbadis.2020.165878.
22. Yoshimoto FK. The proteins of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2 or n-CoV19), the cause of COVID-19.
Protein J. 2020;39:198–216. doi: 10.1007/s10930-020-09901-4.
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
-12-
23. Kim D, Lee JY, Yang JS, Kim JW, Kim VN, Chang H. The architecture of SARS-CoV-2 transcriptome. Cell. 2020;181:914–21 e10. doi:
10.1016/j.cell.2020.04.011.
24. Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for
virus origins and receptor binding. Lancet. 2020;395:565–74. doi: 10.1016/S0140-6736(20)30251-8.
25. Yan R, Zhang Y, Li Y, Xia L, Guo Y, Zhou Q. Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2. Science.
2020;367:1444–8. doi: 10.1126/science.abb2762.
26. Ni W, Yang X, Yang D, Bao J, Li R, Xiao Y, et al. Role of angiotensin-converting enzyme 2 (ACE2) in COVID-19. Crit Care. 2020;24:422.
doi: 10.1186/s13054-020-03120-0.
27. Hoffmann M, Kleine-Weber H, Schroeder S, Kruger N, Herrler T, Erichsen S, et al. SARS-CoV-2 cell entry depends on ACE2 and
TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell. 2020;181:271–80 e8. doi: 10.1016/j.cell.2020.02.052.
28. Amanat F, Krammer F. SARS-CoV-2 vaccines: status report. Immunity. 2020;52:583–9. doi: 10.1016/j.immuni.2020.03.007.
29. Brouwer PJM, Caniels TG, van der Straten K, Snitselaar JL, Aldon Y, Bangaru S, et al. Potent neutralizing antibodies from COVID-19
patients define multiple targets of vulnerability. Science. 2020;369:643–50. doi: 10.1126/science.abc5902.
30. Tai W, He L, Zhang X, Pu J, Voronin D, Jiang S, et al. Characterization of the receptor-binding domain (RBD) of 2019 novel coronavirus:
implication for development of RBD protein as a viral attachment inhibitor and vaccine. Cell Mol Immunol. 2020;17:613–20. doi:
10.1038/s41423-020-0400-4.
31. Shi R, Shan C, Duan X, Chen Z, Liu P, Song J, et al. A human neutralizing antibody targets the receptor-binding site of SARS-CoV-2.
Nature. 2020;584:120–4. doi: 10.1038/s41586-020-2381-y.
32. Rogers TF, Zhao F, Huang D, Beutler N, Burns A, He WT, et al. Isolation of potent SARS-CoV-2 neutralizing antibodies and protection
from disease in a small animal model. Science. 2020;369:956–63. doi: 10.1126/science.abc7520.
33. Li Q, Wu J, Nie J, Zhang L, Hao H, Liu S, et al. The impact of mutations in SARS-CoV-2 spike on viral infectivity and antigenicity. Cell.
2020;182:1284–94 e9. doi: 10.1016/j.cell.2020.07.012.
34. Candido DS, Claro IM, de Jesus JG, Souza WM, Moreira FRR, Dellicour S, et al. Evolution and epidemic spread of SARS-CoV-2 in Brazil.
Science. 2020;369:1255–60. doi: 10.1126/science.abd2161.
35. van Dorp L, Acman M, Richard D, Shaw LP, Ford CE, Ormond L, et al. Emergence of genomic diversity and recurrent mutations in
SARS-CoV-2. Infect Genet Evol. 2020;83:104351. doi: 10.1016/j.meegid.2020.104351.
36. Xu W, Zhang Y, Wang H, Zhu Z, Mao N, Mulders MN, et al. Global and national laboratory networks support high quality surveillance
for measles and rubella. Int Health. 2017;9:184–9. doi: 10.1093/inthealth/ihx017.
37. Mulders MN, Serhan F, Goodson JL, Icenogle J, Johnson BW, Rota PA. Expansion of surveillance for vaccine-preventable diseases:
building on the global polio laboratory network and the global measles and rubella laboratory network platforms. J Infect Dis.
2017;216:S324–S30. doi: 10.1093/infdis/jix077.
38. Diop OM, Kew OM, de Gourville EM, Pallansch MA. The global polio laboratory network as a platform for the viral vaccinepreventable
and emerging diseases laboratory networks. J Infect Dis. 2017;216:S299–S307. doi: 10.1093/infdis/jix092.
39. Hay AJ, McCauley JW. The WHO Global Influenza Surveillance and Response System (GISRS): a future perspective. Influenza Other
Respir Viruses. 2018;12:551–7. doi: 10.1111/irv.12565.
40. Terms of reference for WHO reference laboratories providing confirmatory testing for COVID-19. Geneva: World Health
Organization; 2020 (https://www.who.int/publications/m/item/terms-of-reference-for-who-reference-laboratories-providingconfirmatory-testing-for-covid-19,
accessed 26 June 2020).
41. RubeNS database for rubella sequences (http://www.who-rubella.org/, accessed 26 June 2020).
42. MeaNS: Measles nucleotide surveillance (http://www.who-measles.org, accessed 26 June 2020).
43. Roy S, LaFramboise WA, Nikiforov YE, Nikiforova MN, Routbort MJ, Pfeifer J, et al. Next-generation sequencing informatics:
challenges and strategies for implementation in a clinical environment. Arch Pathol Lab Med. 2016;140:958–75. doi:
10.5858/arpa.2015-0507-RA.
44. Mutenherwa F, Wassenaar DR, de Oliveira T. Experts' perspectives on key ethical issues associated with HIV phylogenetics as applied
in HIV transmission dynamics research. J Empir Res Hum Res Ethics. 2019;14:61–77. doi: 10.1177/1556264618809608.
45. Emanuel EJ, Wendler D, Grady C. What makes clinical research ethical? JAMA. 2000;283:2701–11. doi: 10.1001/jama.283.20.2701.
46. WHO guidelines on ethical issues in public health surveillance. Geneva: World Health Organization; 2017
(https://www.who.int/ethics/publications/public-health-surveillance/en/, accessed 15 November 2020).
47. Diagnostic testing for SARS-CoV-2: interim guidance. 11 September 2020. Geneva: World Health Organization; 2020
(https://www.who.int/publications/i/item/diagnostic-testing-for-sars-cov-2, accessed 6 December 2020).
48. MacCannell D. SARS-CoV-2 sequencing (https://github.com/CDCgov/SARS-CoV-2_Sequencing, accessed 1 November 2020).
49. Cesare MD. Probe-based target enrichment of SARS-CoV-2 [Protocol]. Univeristy of Oxford; 2020. doi:
10.17504/protocols.io.bd5di826.
50. Vogels CBF, Brito AF, Wyllie AL, Fauver JR, Ott IM, Kalinich CC, et al. Analytical sensitivity and efficiency comparisons of SARS-CoV-2
RT–qPCR primer–probe sets. Nat Microbiol. 2020:5:1299–1305. doi: 10.1038/s41564-020-0761-6.
51. Quick J, Grubaugh ND, Pullan ST, Claro IM, Smith AD, Gangavarapu K, et al. Multiplex PCR method for minION and Illumina
sequencing of Zika and other virus genomes directly from clinical samples. Nat Protoc. 2017;12:1261–76. doi:
10.1038/nprot.2017.066.
52. Grubaugh ND, Gangavarapu K, Quick J, Matteson NL, De Jesus JG, Main BJ, et al. An amplicon-based sequencing framework for
accurately measuring intrahost virus diversity using PrimalSeq and iVar. Genome Biol. 2019;20:8. doi: 10.1186/s13059-018-1618-7.
53. Matteson N, Grubaugh N, Gangavarapu K, Quick J, Loman N, Andersen K. PrimalSeq: Generation of tiled virus amplicons for MiSeq
sequencing [Protocol]. 2020. doi: 10.17504/protocols.io.bez7jf9n.
54. Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. Shotgun metagenomics, from sampling to analysis. Nat Biotechnol.
2017;35:833–44. doi: 10.1038/nbt.3935.
55. Bragg L, Tyson GW. Metagenomics using next-generation sequencing. Methods Mol Biol. 2014;1096:183–201. doi: 10.1007/978-1-
62703-712-9_15.
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
-13-
56. Xiao M, Liu X, Ji J, Li M, Li J, Yang L, et al. Multiple approaches for massively parallel sequencing of SARS-CoV-2 genomes directly from
clinical samples. Genome Med. 2020;12:57. doi: 10.1186/s13073-020-00751-4.
57. Rambaut A, Holmes EC, O’Toole Á, Hill V, McCrone JT, Ruis C, et al. A dynamic nomenclature proposal for SARS-CoV-2 lineages to
assist genomic epidemiology. Nat Microbiol. 2020;5:1403–7. doi: 10.1038/s41564-020-0770-5.
58. Singer J, Gifford R, Cotten M, Robertson D. CoV-GLUE: A web application for tracking SARS-CoV-2 genomic variation. Preprints.
2020:2020060225. doi: 10.20944/preprints202006.0225.v1.
59. CoVsurver: mutation analysis of hCoV-19. GISAID (https://www.gisaid.org/epiflu-applications/covsurver-mutations-app/, accessed
11 December 2020).
60. Pangolin COVID-19 lineage assigner (https://pangolin.cog-uk.io/, accessed 11 December 2020).
61. Fauquet CM, Fargette D. International Committee on Taxonomy of Viruses and the 3,142 unassigned species. Virol J. 2005;2:64. doi:
10.1186/1743-422X-2-64.
62. Alm E, Broberg EK, Connor T, Hodcroft EB, Komissarov AB, Maurer-Stroh S, et al. Geographical and temporal distribution of SARSCoV-2
clades in the WHO European Region, January to June 2020. Euro Surveill. 2020;25. doi: 10.2807/1560-
7917.ES.2020.25.32.2001410.
63. Policy statement on data sharing by WHO in the context of public health emergencies (as of 13 April 2016). Geneva: World Health
Organization Geneva; 2016 (https://apps.who.int/iris/handle/10665/254440, accessed 25 November 2020).
64. Pandemic influenza preparedness framework, for the sharing of influenza viruses and access to vaccines and other benefits. Geneva:
World Health Organization; 2011 (https://apps.who.int/gb/pip/pdf_files/pandemic-influenza-preparedness-en.pdf, accessed 20
November 2020).
65. Executive Board, 140th session, provisional agenda item 7.5 Review of the pandemic influenza preparedness framework, report by
the Director-General. Geneva: World Health Organization; 2016 (https://apps.who.int/gb/ebwha/pdf_files/EB140/B140_16en.pdf?ua=1,
accessed 15 November 2020).
66. Laboratory testing strategy recommendations for COVID-19. Geneva: World Health Organization; 2020
(https://apps.who.int/iris/handle/10665/331509, accessed 6 December 2020).
67. Guidance for laboratories shipping specimens to WHO reference laboratories that provide confirmatory testing for COVID-19 virus.
Geneva: World Health Organization; 2020 (https://apps.who.int/iris/handle/10665/331639, accessed 4 December 2020).
68. Molecular assays to diagnose COVID-19: summary table of available protocols. Geneva: World Health Organization; 2020
(https://www.who.int/who-documents-detail/molecular-assays-to-diagnose-covid-19-summary-table-of-available-protocols,
accessed 4 December 2020).
69. Fact sheet: genetic sequence data and databases. Geneva: World Health Organization; 2018
(https://www.who.int/influenza/pip/GSD_EN_V2_10Sep2018.pdf?ua=1, accessed 11 December 2020).
70. medRxiv: The Preprint Server for Health Sciences (https://www.medrxiv.org/, accessed 1 November 2020).
71. bioRxiv: The Preprint Server for Biology (https://www.biorxiv.org/, accessed 1 November 2020).
72. Virological (https://virological.org/, accessed 1 November 2020).
73. Wu F, Zhao S, Yu B, Chen Y-M, Wang W, Song Z-G, et al. A new coronavirus associated with human respiratory disease in China.
Nature. 2020;579:265–9. doi: 10.1038/s41586-020-2008-3.
74. Lu J, du Plessis L, Liu Z, Hill V, Kang M, Lin H, et al. Genomic epidemiology of SARS-CoV-2 in Guangdong Province, China. Cell.
2020;181:997–1003.e9. doi: 10.1016/j.cell.2020.04.023.
75. Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with a new coronavirus of probable bat
origin. Nature. 2020;579:270–3. doi: 10.1038/s41586-020-2012-7.
76. Corman VM, Landt O, Kaiser M, Molenkamp R, Meijer A, Chu DK, et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time
RT-PCR. Euro Surveill. 2020;25. doi: 10.2807/1560-7917.ES.2020.25.3.2000045.
77. Detection of 2019 novel coronavirus (2019-nCoV) in suspected human cases by RT-PCR. Hong Kong University Medical School; 2020
(https://www.who.int/docs/default-source/coronaviruse/peiris-protocol-16-1-20.pdf?sfvrsn=af1aac73_4, accessed 1 December
2020).
78. Melén K, Kakkola L, He F, Airenne K, Vapalahti O, Karlberg H, et al. Production, purification and immunogenicity of recombinant Ebola
virus proteins: a comparison of Freund's adjuvant and adjuvant system 03. J Virol Methods. 2017;242:35–45. doi:
10.1016/j.jviromet.2016.12.014.
79. Draft landscape of COVID-19 candidate vaccines. Geneva: World Health Organization
(https://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines, accessed 26 June 2020).
80. Ren Y, Zhou Z, Liu J, Lin L, Li S, Wang H, et al. A strategy for searching antigenic regions in the SARS-CoV spike protein. Genomics
Proteomics Bioinformatics. 2003;1:207–15. doi: 10.1016/s1672-0229(03)01026-x.
81. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected
pneumonia. N Engl J Med. 2020;382:1199–207. doi: 10.1056/NEJMoa2001316.
82. Andersen K. Clock and TMRCA based on 27 genomes. Scripps Research; 2020 (https://virological.org/t/clock-and-tmrca-based-on-27genomes/347,
accessed 26 June 2020).
83. Report of the WHO–China Joint Mission on coronavirus disease 2019 (COVID-19). Geneva: World Health Organization; 2020
(https://www.who.int/publications-detail-redirect/report-of-the-who-china-joint-mission-on-coronavirus-disease-2019-(covid-19),
accessed 15 July 2020).
84. Cui J, Li F, Shi Z-L. Origin and evolution of pathogenic coronaviruses. Nat Rev Microbiol. 2019;17:181–92. doi: 10.1038/s41579-018-
0118-9.
85. Grenfell BT, Pybus OG, Gog JR, Wood JLN, Daly JM, Mumford JA, et al. Unifying the epidemiological and evolutionary dynamics of
pathogens. Science. 2004;303:327–32. doi: 10.1126/science.1090727.
86. Volz EM, Koelle K, Bedford T. Viral phylodynamics. PLoS Comput Biol. 2013;9. doi: 10.1371/journal.pcbi.1002947.
87. Pybus OG, Rambaut A. Evolutionary analysis of the dynamics of viral infectious disease. Nat Rev Genet. 2009;10:540–50. doi:
10.1038/nrg2583.
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
-14-
88. Lai A, Bergna A, Caucci S, Clementi N, Vicenti I, Dragoni F, et al. Molecular tracing of SARS-CoV-2 in Italy in the first three months of
the epidemic. Viruses. 2020;12. doi: 10.3390/v12080798.
89. Fauver JR, Petrone ME, Hodcroft EB, Shioda K, Ehrlich HY, Watts AG, et al. Coast-to-coast spread of SARS-CoV-2 during the early
epidemic in the United States. Cell. 2020;181:990–6.e5. doi: 10.1016/j.cell.2020.04.021.
90. Candido DdS, Claro IM, Jesus dJG, Souza dWM, Moreira FRR, Dellicour S, et al. Evolution and epidemic spread of SARS-CoV-2 in Brazil.
Science. 2020;369:1255–60. doi: 10.1101/2020.06.11.20128249.
91. Lemey P, Hong S, Hill V, Baele G, Poletto C, Colizza V, et al. Accommodating individual travel history, global mobility, and unsampled
diversity in phylogeography: a SARS-CoV-2 case study. bioRxiv. 2020. doi: 10.1101/2020.06.22.165464.
92. Lemey P, Rambaut A, Bedford T, Faria N, Bielejec F, Baele G, et al. Unifying viral genetics and human transportation data to predict
the global transmission dynamics of human influenza H3N2. PLoS Pathog. 2014;10:e1003932. doi: 10.1371/journal.ppat.1003932.
93. Dudas G, Carvalho LM, Bedford T, Tatem AJ, Baele G, Faria NR, et al. Virus genomes reveal factors that spread and sustained the
Ebola epidemic. Nature. 2017;544:309–15. doi: 10.1038/nature22040.
94. Dellicour S, Baele G, Dudas G, Faria NR, Pybus OG, Suchard MA, et al. Phylodynamic assessment of intervention strategies for the
West African Ebola virus outbreak. Nat Commun. 2018;9:1–9. doi: 10.1038/s41467-018-03763-2.
95. Lemey P, Rambaut A, Drummond AJ, Suchard MA. Bayesian phylogeography finds its roots. PLoS Comput Biol. 2009;5:e1000520. doi:
10.1371/journal.pcbi.1000520.
96. Lemey P, Rambaut A, Welch JJ, Suchard MA. Phylogeography takes a relaxed random walk in continuous space and time. Mol Biol
Evol. 2010;27:1877–85. doi: 10.1093/molbev/msq067.
97. Bloomquist EW, Lemey P, Suchard MA. Three roads diverged? Routes to phylogeographic inference. Trends Ecol Evol. 2010;25:626–
32. doi: 10.1016/j.tree.2010.08.010.
98. Faria NR, Suchard MA, Rambaut A, Lemey P. Towards a quantitative understanding of viral phylogeography. Curr Opin Virol.
2011;1:423–9. doi: 10.1016/j.coviro.2011.10.003.
99. Lemey P, Hong S, Hill V, Baele G, Poletto C, Colizza V, et al. Accommodating individual travel history, global mobility, and unsampled
diversity in phylogeography: A SARS-CoV-2 case study. bioRxiv. 2020:165464. doi: 10.1101/2020.06.22.165464.
100. Reusken CB, Buiting A, Bleeker-Rovers C, Diederen B, Hooiveld M, Friesema I, et al. Rapid assessment of regional SARS-CoV-2
community transmission through a convenience sample of healthcare workers, the Netherlands, March 2020. Euro Surveill. 2020;25.
doi: 10.2807/1560-7917.ES.2020.25.12.2000334.
101. Worby CJ, Lipsitch M, Hanage WP. Shared genomic variants: identification of transmission routes using pathogen deep-sequence
data. Am J Epidemiol. 2017;186:1209–16. doi: 10.1093/aje/kwx182.
102. Duchene S, Featherstone L, Haritopoulou-Sinanidou M, Rambaut A, Lemey P, Baele G. Temporal signal and the phylodynamic
threshold of SARS-CoV-2. bioRxiv. 2020:077735. doi: 10.1101/2020.05.04.077735.
103. Volz E, Fu H, Wang H, Xi X, Chen W, Liu D, et al. Genomic epidemiology of a densely sampled COVID19 outbreak in China. medRxiv.
2020:20033365. doi: 10.1101/2020.03.09.20033365.
104. Bedford T, Greninger AL, Roychoudhury P, Starita LM, Famulare M, Huang M-L, et al. Cryptic transmission of SARS-CoV-2 in
Washington State. Science. 2020;370:571–5. doi: 10.1101/2020.04.02.20051417.
105. Zehender G, Lai A, Bergna A, Meroni L, Riva A, Balotta C, et al. Genomic characterization and phylogenetic analysis of SARS-CoV-2 in
Italy. J Med Virol. 2020;92:1637–40. doi: 10.1002/jmv.25794.
106. Worobey MA-O, Pekar JA-O, Larsen BA-O, Nelson MA-O, Hill V, Joy JB, et al. The emergence of SARS-CoV-2 in Europe and North
America. Science. 2020;370:564–70. doi: 10.1126/science.abc8169.
107. Asghar H, Diop OM, Weldegebriel G, Malik F, Shetty S, El Bassioni L, et al. Environmental surveillance for polioviruses in the Global
Polio Eradication Initiative. J Infect Dis. 2014;210 Suppl 1:S294–303. doi: 10.1093/infdis/jiu384.
108. Paul JR, Trask JD, Gard S. Ii. Poliomyelitic virus in urban sewage. J Exp Med. 1940;71:765–77. doi: 10.1084/jem.71.6.765.
109. Nemudryi A, Nemudraia A, Wiegand T, Surya K, Buyukyoruk M, Cicha C, et al. Temporal detection and phylogenetic assessment of
SARS-CoV-2 in municipal wastewater. Cell Rep Med. 2020;1:100098. doi: 10.1016/j.xcrm.2020.100098.
110. Wu F, Zhang J, Xiao A, Gu X, Lee WL, Armas F, et al. SARS-CoV-2 titers in wastewater are higher than expected from clinically
confirmed cases. mSystems. 2020;5. doi: 10.1128/mSystems.00614-20.
111. Ahmed W, Angel N, Edson J, Bibby K, Bivins A, O'Brien JW, et al. First confirmed detection of SARS-CoV-2 in untreated wastewater in
Australia: a proof of concept for the wastewater surveillance of COVID-19 in the community. Sci Total Environ. 2020;728:138764. doi:
10.1016/j.scitotenv.2020.138764.
112. Wurtzer SMV, Mouchel JM, Maday Y, Teyssou R, Richard E, Almayrac JL, Moulin L. Evaluation of lockdown impact on SARS-CoV-2
dynamics through viral genome quantification in Paris wastewaters. medRxiv. 2020. doi: 10.1101/2020.04.12.20062679.
113. La Rosa G, Iaconelli M, Mancini P, Bonanno Ferraro G, Veneri C, Bonadonna L, et al. First detection of SARS-CoV-2 in untreated
wastewaters in Italy. Sci Total Environ. 2020;736:139652. doi: 10.1016/j.scitotenv.2020.139652.
114. Gertjan Medema LH, Goffe Elsinga, Ronald Italiaander, Anke Brouwer. Presence of SARS-coronavirus-2 RNA in sewage and
correlation with reported COVID-19 prevalence in the early stage of the epidemic in the Netherlands. Environ Sci Technol Lett. 2020.
doi: 10.1021/acs.estlett.0c00357.
115. Lodder W, de Roda Husman AM. SARS-CoV-2 in wastewater: potential health risk, but also data source. Lancet Gastroenterol
Hepatol. 2020;5:533–4. doi: 10.1016/S2468-1253(20)30087-X.
116. Scientific brief: status of environmental surveillance for SARS-CoV-2 virus. Geneva: World Health Organization; 2020
(https://www.who.int/publications/i/item/WHO-2019-nCoV-sci-brief-environmentalSampling-2020-1, accessed 12 December 2020).
117. Rapid expert consultation on environmental surveillance of SARS-CoV-2 in wastewater: summary report. Geneva: World Health
Organization; 2020 (https://www.euro.who.int/en/health-topics/environment-and-health/water-and-
sanitation/publications/2020/rapid-expert-consultation-on-environmental-surveillance-of-sars-cov-2-in-wastewater-summaryreport-2020,
accessed 12 December 2020).
118. Edridge AWD, Kaczorowska JM, Hoste ACR, Bakker M, Klein M, Jebbink MF, et al. Coronavirus protective immunity is short lasting.
medRxiv. 2020. doi: 10.1101/2020.05.11.20086439.
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
-15-
119. To KK, Hung IF, Ip JD, Chu AW, Chan WM, Tam AR, et al. COVID-19 re-infection by a phylogenetically distinct SARS-coronavirus-2
strain confirmed by whole genome sequencing. Clin Infect Dis. 2020. doi: 10.1093/cid/ciaa1275.
120. Goldman JD, Wang K, Roltgen K, Nielsen SCA, Roach JC, Naccache SN, et al. Reinfection with SARS-CoV-2 and failure of humoral
immunity: a case report. medRxiv. 2020. doi: 10.1101/2020.09.22.20192443.
121. Gupta V, Bhoyar RC, Jain A, Srivastava S, Upadhayay R, Imran M, et al. Asymptomatic reinfection in two healthcare workers from
India with genetically distinct SARS-CoV-2. Clin Infect Dis. 2020. doi: 10.1093/cid/ciaa1451.
122. Mulder M, van der Vegt D, Oude Munnink BB, GeurtsvanKessel CH, van de Bovenkamp J, Sikkema RS, et al. Reinfection of SARS-CoV-
2 in an immunocompromised patient: a case report. Clin Infect Dis. 2020. doi: 10.1093/cid/ciaa1538.
123. Tillett RL, Sevinsky JR, Hartley PD, Kerwin H, Crawford N, Gorzalski A, et al. Genomic evidence for reinfection with SARS-CoV-2: a case
study. Lancet Infect Dis. 2020. doi: 10.1016/S1473-3099(20)30764-7.
124. Van Elslande J, Vermeersch P, Vandervoort K, Wawina-Bokalanga T, Vanmechelen B, Wollants E, et al. Symptomatic SARS-CoV-2
reinfection by a phylogenetically distinct strain. Clin Infect Dis. 2020. doi: 10.1093/cid/ciaa1330.
125. Common investigation protocol for investigating suspected SARS-CoV-2 reinfection. Atlanta: United States Centers for Disease
Control and Prevention; 2020 (https://www.cdc.gov/coronavirus/2019-ncov/php/reinfection.html, accessed 1 November 2020).
126. Reinfection with SARS-CoV-2: considerations for public health response. Stockholm: European Centre for Disease Prevention and
Control; 2020 (https://www.ecdc.europa.eu/sites/default/files/documents/Re-infection-and-viral-shedding-threat-assessmentbrief.pdf,
accessed 1 November 2020).
127. Emergencies preparedness, response: SARS-CoV-2 variants. Disease outbreak news. Geneva: World Health Organization; 31
December 2020 (https://www.who.int/csr/don/31-december-2020-sars-cov2-variants/en/, accessed 31 December 2020)
128. Baum A, Fulton BO, Wloga E, Copin R, Pascal KE, Russo V, et al. Antibody cocktail to SARS-CoV-2 spike protein prevents rapid
mutational escape seen with individual antibodies. Science. 2020;369:1014–8. doi: 10.1126/science.abd0831.
129. Inzaule SC, Hamers RL, Paredes R, Yang C, Schuurman R, Rinke de Wit TF. The evolving landscape of HIV drug resistance diagnostics
for expanding testing in resource-limited settings. AIDS Rev. 2017;19:219–30 (https://www.ncbi.nlm.nih.gov/pubmed/28182618,
accessed 15 November 2020).
130. Sepulveda N, Phelan J, Diez-Benavente E, Campino S, Clark TG, Hopkins H, et al. Global analysis of Plasmodium falciparum histidinerich
protein-2 (pfhrp2) and pfhrp3 gene deletions using whole-genome sequencing data and meta-analysis. Infect Genet Evol.
2018;62:211–9. doi: 10.1016/j.meegid.2018.04.039.
131. Cremer J, Hofstraat SHI, van Heiningen F, Veldhuijzen IK, van Benthem BHB, Benschop KSM. Genetic variation of Hepatitis B surface
antigen among acute and chronic Hepatitis B virus infections in the Netherlands. J Med Virol. 2018;90:1576–85. doi:
10.1002/jmv.25232.
132. Hollinger FB. Hepatitis B virus genetic diversity and its impact on diagnostic assays. J Viral Hepat. 2007;14 Suppl 1:11–5. doi:
10.1111/j.1365-2893.2007.00910.x.
133. Lam SD, Bordin N, Waman VP, Scholes HM, Ashford P, Sen N, et al. SARS-CoV-2 spike protein predicted to form complexes with host
receptor protein orthologues from a broad range of mammals. Sci Rep. 2020;10:16471. doi: 10.1038/s41598-020-71936-5.
134. Damas J, Hughes GM, Keough KC, Painter CA, Persky NS, Corbo M, et al. Broad host range of SARS-CoV-2 predicted by comparative
and structural analysis of ACE2 in vertebrates. Proc Natl Acad Sci U S A. 2020;117:22311–22. doi: 10.1073/pnas.2010146117.
135. Freuling CM, Breithaupt A, Müller T, Sehl J, Balkema-Buschmann A, Rissmann M, et al. Susceptibility of raccoon dogs for experimental
SARS-CoV-2. bioRxiv. 2020. doi: 10.1101/2020.08.19.256800v1.
136. Schlottau K, Rissmann M, Graaf A, Schon J, Sehl J, Wylezich C, et al. SARS-CoV-2 in fruit bats, ferrets, pigs, and chickens: an
experimental transmission study. Lancet Microbe. 2020;1:e218–e25. doi: 10.1016/S2666-5247(20)30089-6.
137. Shi J, Wen Z, Zhong G, Yang H, Wang C, Huang B, et al. Susceptibility of ferrets, cats, dogs, and other domesticated animals to SARScoronavirus
2. Science. 2020;368:1016–20. doi: 10.1126/science.abb7015.
138. Kim YI, Kim SG, Kim SM, Kim EH, Park SJ, Yu KM, et al. Infection and rapid transmission of SARS-CoV-2 in ferrets. Cell Host Microbe.
2020;27:704–9 e2. doi: 10.1016/j.chom.2020.03.023.
139. Halfmann PJ, Hatta M, Chiba S, Maemura T, Fan S, Takeda M, et al. Transmission of SARS-CoV-2 in domestic cats. N Engl J Med.
2020;383:592–4. doi: 10.1056/NEJMc2013400.
140. Ruiz-Arrondo I, Portillo A, Palomar AM, Santibanez S, Santibanez P, Cervera C, et al. Detection of SARS-CoV-2 in pets living with
COVID-19 owners diagnosed during the COVID-19 lockdown in Spain: a case of an asymptomatic cat with SARS-CoV-2 in Europe.
Transbound Emerg Dis. 2020. doi: 10.1111/tbed.13803.
141. Richard M, Kok A, de Meulder D, Bestebroer TM, Lamers MM, Okba NMA, et al. SARS-CoV-2 is transmitted via contact and via the air
between ferrets. Nat Commun. 2020;11:3496. doi: 10.1038/s41467-020-17367-2.
142. Sia SF, Yan LM, Chin AWH, Fung K, Choy KT, Wong AYL, et al. Pathogenesis and transmission of SARS-CoV-2 in golden hamsters.
Nature. 2020. doi: 10.1038/s41586-020-2342-5.
143. Munster VJ, Feldmann F, Williamson BN, van Doremalen N, Pérez-Pérez L, Schulz J, et al. Respiratory disease and virus shedding in
rhesus macaques inoculated with SARS-CoV-2. medRxiv. 2020. doi: 10.1101/2020.03.21.001628v1.
144. Zhao Y, Wang J, Kuang D, Xu J, Yang M, Ma C, et al. Susceptibility of tree shrew to SARS-CoV-2 infection. Sci Rep. 2020;10:16007. doi:
10.1038/s41598-020-72563-w.
145. Woolsey C, Borisevich V, Prasad AN, Agans KN, Deer DJ, Dobias NS, et al. Establishment of an African green monkey model for COVID-
19. bioRxiv. 2020. doi: 10.1101/2020.05.17.100289.
146. Lu S, Zhao Y, Yu W, Yang Y, Gao J, Wang J, et al. Comparison of nonhuman primates identified the suitable model for COVID-19. Signal
Transduct Target Ther. 2020;5:157. doi: 10.1038/s41392-020-00269-6.
147. Sit THC, Brackman CJ, Ip SM, Tam KWS, Law PYT, To EMW, et al. Infection of dogs with SARS-CoV-2. Nature. 2020. doi:
10.1038/s41586-020-2334-5.
148. Newman A, Smith D, Ghai RR, Wallace RM, Torchetti MK, Loiacono C, et al. First reported cases of SARS-CoV-2 infection in companion
animals - New York, March–April 2020. MMWR Morb Mortal Wkly Rep. 2020;69:710–3. doi: 10.15585/mmwr.mm6923e3.
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
-16-
149. Oreshkova N, Molenaar RJ, Vreman S, Harders F, Oude Munnink BB, Hakze-van der Honing RW, et al. SARS-CoV-2 infection in farmed
minks, the Netherlands, April and May 2020. Euro Surveill. 2020;25. doi: 10.2807/1560-7917.ES.2020.25.23.2001005.
150. Cahan E. COVID-19 hits U.S. mink farms after ripping through Europe. Science Magazine. 2020
(https://www.sciencemag.org/news/2020/08/covid-19-hits-us-mink-farms-after-ripping-through-europe, accessed 13 November
2020).
151. Abdel-Moneim AS, Abdelwhab EM. Evidence for SARS-CoV-2 infection of animal hosts. Pathogens. 2020;9. doi:
10.3390/pathogens9070529.
152. Exposure of humans or animals to SARS-CoV-2 from wild, livestock, companion and aquatic animals. Rome: Food and Agriculture
Organization of the United Nations; 2020 (http://www.fao.org/3/ca9959en/ca9959en.pdf, accessed 1 December 2020).
153. Guidelines to mitigate the impact of the COVID-19 pandemic on livestock production and animal health. Rome: Food and Agriculture
Organization of the United Nations; 2020 (http://www.fao.org/3/ca9177en/CA9177EN.pdf, accessed 1 December 2020).
154. Guidance on working with farmed animals of species susceptible to infection with SARS-CoV-2. Paris: World Organisation for Animal
Health (OIE); 2020 (https://www.oie.int/fileadmin/Home/MM/Draft_OIE_Guidance_farmed_animals_cleanMS05.11.pdf, accessed 8
December 2020).
155. El Zowalaty ME, Jarhult JD. From SARS to COVID-19: a previously unknown SARS-related coronavirus (SARS-CoV-2) of pandemic
potential infecting humans: call for a One Health approach. One Health. 2020;9:100124. doi: 10.1016/j.onehlt.2020.100124.
156. Leroy EM, Ar Gouilh M, Brugere-Picoux J. The risk of SARS-CoV-2 transmission to pets and other wild and domestic animals strongly
mandates a One-Health strategy to control the COVID-19 pandemic. One Health. 2020;10:100133. doi:
10.1016/j.onehlt.2020.100133.
157. Guidelines for working with free-ranging wild mammals in the era of the COVID-19 pandemic. Paris: World Organisation for Animal
Health (OIE); 2020 (https://www.oie.int/fileadmin/Home/eng/Our_scientific_expertise/docs/pdf/COV-19/A_WHSG_and_OIE_COVID-
19_Guidelines.pdf, accessed 10 December 2020).
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
-17Annex
I: Key questions to consider before
initiating a sequencing programme
(1) What are the expected outputs of the sequencing programme?
(2) Which samples should be sequenced to achieve the expected outputs identified in step 1?
Which metadata or additional data sources are critical?
(3) Who are the key stakeholders and what are their responsibilities? How can they be
effectively engaged?
(4) How can samples and information be transferred rapidly and appropriately between
stakeholders, as required?
(5) Is the project designed in accordance with local, national and international laws and ethical
guidelines?
(6) Are adequate funding, equipment and human resources available to deliver all stages of
specimen retrieval, wet-laboratory sequencing, bioinformatic, phylodynamic and other
analyses, data sharing, and communication of timely results to appropriate stakeholders?
(7) How can goals be achieved without disrupting other areas of laboratory work, such as
clinical diagnostics, and avoiding duplication of effort?
(8) How will the programme be evaluated for cost-effectiveness and impact?
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
-18Annex
II: Checklist for setting up a SARS-CoV-2
sequencing programme
Aims
Define the expected aims of the sequencing programme; what information will sequencing be likely to provide that is
additional to or more cost-effective than existing approaches?
Stakeholder identification and engagement
Identify key stakeholders.
Discuss the programme aims with senior representatives of stakeholder groups and define the responsibilities of each
group.
Consider sharing educational materials about the potential and requirements of SARS-CoV-2 sequencing with
stakeholders.
Identify the links needed between key stakeholders to enable rapid movement of samples, requests for information
and use of results.
Ensure that clear, appropriate links between stakeholders are established.
Technical considerations
Determine the level of genomic sampling required to achieve the desired goals, in discussion with senior members of
case-identification and analytical teams.
Identify the metadata required to achieve the desired goals, in discussion with senior members of case-identification
and analytical teams.
Choose appropriate sample and library preparation protocols.
Choose appropriate bioinformatic protocols.
Choose appropriate analytical protocols.
Logistical considerations
Consider where sequencing and analysis will be conducted (e.g., an existing diagnostic laboratory or external
commercial or academic laboratory).
Identify appropriate sources of funding that will be adequate to support laboratory sequencing, data storage and data
analysis.
Ensure that sufficient reagents and computational resources are available and can be sustainably obtained as required.
Ensure that there are sufficient and appropriate human resources to deliver the programme at every stage.
Ensure that sample integrity can be maintained at all steps throughout the pipeline via cold-chain or other measures.
Ensure adequate collection and storage of metadata and correct association with biological samples.
Consider the possible additional pressure that sequencing will place on existing arms of the public health response, and
seek ways to alleviate this.
For large-scale sequencing programmes, identify how to streamline the sharing of data and samples between
participating groups (e.g., the feasibility of using a single-sample identification and identical metadata formats).
Ensuring a safe and ethical environment
Conduct appropriate ethical reviews for the generation, use and storage of sequence data and associated metadata.
Conduct risk assessments of sequencing activities to ensure appropriate biosafety at all stages.
Conduct risk assessments of sequencing activities to ensure appropriate biosecurity, if relevant under national and
regional law.
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
-19Consider
the impact on human resources, including the reallocation of staff or hiring of additional staff to maintain the
individual workload at reasonable levels.
Ensure that staff can commute to work and be in the workplace safely and in accordance with national guidelines on
preventing transmission during the COVID-19 outbreak.
Define strategies for maintaining the sequencing programme if key staff members become ill or must self-isolate.
Data sharing
Ensure that all stakeholders are in agreement as to which sequences and metadata will be shared publicly, via which
platforms and when.
Ensure that all stakeholders are in agreement as to whether any metadata are to be restricted to a limited number of
local users and devise strategies for securely sharing those data.
Ensure data sharing complies with national and international regulatory frameworks.
Evaluation
Ensure regular opportunities for evaluating the sequencing programme, including successes and continuing challenges.
Ensure that a monitoring and evaluation framework is implemented to assess performance of the sequencing programme
both technically (quality, etc.) and in terms of the programme’s success in meeting its objectives.
SARS-CoV-2 genomic sequencing for public health goals: Interim guidance
-20Annex
III: Commonly used platforms for SARSCoV-2
sequencing analysis and their
characteristics
a
This listing of the various instruments is to provide an overview of the most commonly used tools for SARS-CoV-2 genomic sequencing and does not imply
WHO endorsement of these products.
b Different cost estimations can be found in (8).
WHO continues to monitor the situation closely for any changes that may affect this interim guidance. Should any factors
change, WHO will issue a further update. Otherwise, this interim guidance document will expire 2 years after the date of
publication.
© World Health Organization 2021. Some rights reserved. This work is available under the CC BY-NC-SA 3.0 IGO licence.
WHO reference number: WHO/2019-nCoV/genomic_sequencing/2021.1
Instrumenta Advantages Limitations Instrument run-
time
Sequencing
throughput
Relative cost
comparison
Sanger
sequencing
Widely accessible
Easy to use
Cost-effective sequencing
if few targets required
Very low throughput
Amplicons (often no more than
1000 bp) must be individually
amplified and sequenced
Expensive for full genomes
Inappropriate for metagenomics
Typically a few
hours
100 kB-2 Mb
per single run
Relatively low
cost for a few
targets
Illumina (e.g.,
iSeq, MiniSeq,
MiSeq, NextSeq,
HiSeq, NovaSeq)
Very high sequencing
yields possible
Very high accuracy
iSeq is portable
Methods for handling data
are well established
With the exception of Illumina iSeq,
expensive to purchase and maintain
compared to some other platforms
Maximum read length 2 x 300 bp
10–55 h,
depending on the
instrument
1.2–6000 Gb,
depending on
instrument
High
maintenance and
start-up costs
Moderate
running costs
Oxford Nanopore
Technologies
(Flongle, MinION,
GridION,
PromethION)
Portable, direct
sequencing
Real-time data
Low start-up and
maintenance costs
Can stop sequencing as
soon as sufficient data are
achieved
Very long read lengths
achievable (exceeding the
full length of the SARSCoV-2
genome)
Challenges with homopolymers
Error rate per read is ~5% (R9.4
flowcells) so use of appropriate
pipelines is critical to obtain highaccuracy
consensus sequences
Currently unsuitable for determining
intra-host variation unless replicate
sequencing is used (52)
Reads available
immediately
Can be
monitored and
run for up to
several days as
required
Ranging from
< 2 Gb for
Flongle flow
cell to 220 Gb
for
PromethION
flow cell
Up to 48 flow
cells can be
used on
PromethION
No maintenance
and relatively
low start-up
costs
Moderate
running costs
Ion Torrent
Fast turnaround once
sequencing starts
Challenges with homopolymers
Expensive to purchase
Maximum typical read lengths around
400 bp
2 h–1 day,
depending on
chip and device
30 Mb–50 Gb
depending on
device and
chips
Moderate costs