Genomic epidemiology of SARS-CoV-2 in Sudan: A retrospective analysis 2020-2022 (preprint)

SARS-CoV-2 was first detected in Sudan on 13th March 2020. Here, we describe the genomic epidemiology of SARS-CoV-2 in Sudan between May 2020 and April 2022 to understand the introduction and transmission of SARS-CoV-2 variants in the country. A total of 667 SARS-CoV-2 positive samples were successfully sequenced using the nCoV-19 Artic protocol on the Oxford Nanopore Technology ([≥]70% genome completeness). The genomes were compared with a select contemporaneous global dataset to determine genetic relatedness and estimate import/export events. The genomes were classified into 37 Pango lineages within the ancestral strain (107 isolates across 13 Pango lineages), Eta variant of interest (VOI) (78 isolates in 1 lineage), Alpha variant of concern (VOC) (10 isolates in 2 lineages), Beta VOC (26 isolates in 1 lineage), Delta VOC (171 isolates across 8 lineages) and Omicron VOC (242 isolates across 12 lineages). We estimated a total of 144 introductions of the observed variants from different countries across the globe. Multiple introductions of the Eta VOI, Beta VOC and Omicron VOC were observed in Sudan mainly from Europe and Africa. These findings suggest a need for continuous genomic surveillance of SARS-CoV-2 to monitor their introduction and spread consequently inform public health measures to combat SARS-CoV-2 transmission.

Temporal changes in the positivity rate of common enteric viruses among paediatric admissions in coastal Kenya, in the period spanning the COVID-19 pandemic, 2019-2022 (preprint)

Abstract Background The non-pharmaceutical interventions (NPIs) implemented to curb the spread of SARS_CoV_2 early in the COVID_19 pandemic years, disrupted the activity of other respiratory viruses. There is limited data from low and middle income countries (LMICs) to determine whether COVID_19 NPIs also impacted the epidemiology of enteric viruses. We investigated the changes in infection patterns of common enteric viruses among hospitalised children who presented with diarrhoea to a referral hospital in coastal Kenya, in the period spanning the COVID_19 pandemic. Methods A total of 870 stool samples from children under 13 years of age admitted to Kilifi County Hospital between January 2019, and December 2022 were screened for rotavirus group A (RVA), norovirus genogroup II (GII), astrovirus, sapovirus, and adenovirus type F40/41 using realtime reverse transcription polymerase chain reaction. The proportions positive across the four years were compared using the chi-squared test statistic. Results One or more of the five virus targets were detected in 282 (32.4%) cases. A reduction in the positivity rate of RVA cases was observed from 2019 (12.1%, 95% confidence interval (CI) 8.7% to 16.2%) to 2020 (1.7%, 95% CI 0.2% to 6.0%; p < 0.001). However, in the 2022, RVA positivity rate rebounded to 23.5% (95% CI 18.2% to 29.4%). For norovirus GII, the positivity rate fluctuated over the four years with its highest positivity rate observed in 2020 (16.2%; 95% C.I, 10.0% to 24.1%). No astrovirus cases were detected in 2020 and 2021, but the positivity rate in 2022 was similar to that in 2019 (3.1% (95% CI 1.5% to 5.7%) vs 3.3% (95% CI 1.4% to 6.5%)). A higher case fatality rate was observed in 2021 (9.0%) compared to the 2019 (3.2%), 2020 (6.8%) and 2022 (2.1%) (p <0.001). Conclusion Our study finds that in 2020 the transmission of common enteric viruses, especially RVA and astrovirus, in Kilifi Kenya may have been disrupted due to the COVID_19 NPIs. After 2020, local enteric virus transmission patterns appeared to return to prepandemic levels coinciding with the removal of most of the government COVID_19 NPIs.

A new Omicron lineage with Spike Y451H mutation that dominated a new COVID-19 wave in Kilifi, Coastal Kenya: March-May 2023 (preprint)

We report a newly emerged SARS-CoV-2 Omicron lineage, named FY.4, that has two unique mutations; spike:Y451H and ORF3a:P42L. FY.4 emergence has coincided with increased SARS-CoV-2 cases in coastal Kenya, April-May 2023. We demonstrate the value of continued SARS-CoV-2 genomic surveillance in the post-acute pandemic era in understanding new COVID-19 outbreaks.

Serum IgG and mucosal IgA antibodies from pre-pandemic samples collected in Kilifi, Kenya, neutralize SARS-CoV-2 in vitro

Objectives Many regions of Africa have experienced lower COVID-19 morbidity and mortality compared to Europe. Pre-existing humoral responses to endemic human coronaviruses (HCoV) may cross-protect against SARS-CoV-2. We investigated neutralizing capacity of SARS-CoV-2 spike reactive and non-reactive IgG and IgA antibodies in pre-pandemic samples. Methods To investigate the presence of pre-existing immunity, we performed ELISA using spike antigens from reference SARS-CoV-2, HCoV HKU1, OC43, NL63 and 229E using pre-pandemic samples from Kilifi in coastal Kenya. Additionally, we performed neutralization assays using pseudotyped reference SARS-CoV-2 to determine functionality of the identified reactive antibodies. Results We demonstrate presence of HCoV serum IgG and mucosal IgA antibodies which cross-react with the SARS-CoV-2 spike. We show pseudotyped reference SARS-CoV-2 neutralization by pre-pandemic serum with a mean ID50 of 1:251, which is ten-fold less than that of pooled convalescent sera from COVID-19 patients but still within predicted protection levels. The pre-pandemic naso-oropharyngeal fluid neutralized pseudo-SARS-CoV-2 at a mean ID50 of 1:5.9 in the neutralization assay. Conclusion Our data provide evidence for pre-existing functional humoral responses to SARS-CoV-2 in Kilifi, coastal Kenya and adds to data showing pre-existing immunity for COVID-19 from other regions.

Genomic epidemiology of SARS-CoV-2 within households in coastal Kenya: a case ascertained cohort study (preprint)

Background Analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic sequence data from household infections should aid its detailed epidemiological understanding. Using viral genomic sequence data, we investigated household SARS-CoV-2 transmission and evolution in coastal Kenya households. Methods We conducted a case-ascertained cohort study between December 2020 and February 2022 whereby 573 members of 158 households were prospectively monitored for SARS-CoV-2 infection. Households were invited to participate if a member tested SARS-CoV-2 positive or was a contact of a confirmed case. Follow-up visits collected a nasopharyngeal/oropharyngeal (NP/OP) swab on days 1, 4 and 7 for RT-PCR diagnosis. If any of these were positive, further swabs were collected on days 10, 14, 21 and 28. Positive samples with an RT-PCR cycle threshold of <33.0 were subjected to whole genome sequencing followed by phylogenetic analysis. Ancestral state reconstruction was used to determine if multiple viruses had entered households. Results Of 2,091 NP/OP swabs that were collected, 375 (17.9%) tested SARS-CoV-2 positive. Viral genome sequences (>80% coverage) were obtained from 208 (55%) positive samples obtained from 61 study households. These genomes fell within 11 Pango lineages and four variants of concern (Alpha, Beta, Delta and Omicron). We estimated 163 putative transmission events involving members of the sequenced households, 40 (25%) of which were intra-household transmission events while 123 (75%) were infections that likely occurred outside the households. Multiple virus introductions (up-to-5) were observed in 28 (47%) households with the 1-month follow-up period. Conclusions We show that a considerable proportion of SARS-CoV-2 infections in coastal Kenya occurred outside the household setting. Multiple virus introductions frequently occurred into households within the same infection wave in contrast to observations from high income settings, where single introduction appears to be the norm. Our findings suggests that control of SARS-CoV-2 transmission by household member isolation may be impractical in this setting.

The genomic epidemiology of SARS-CoV-2 variants of concern in Kenya (preprint)

The emergence and establishment of SARS CoV 2 variants of concern presented a major global public health crisis across the world. There were six waves of SARS CoV 2 cases in Kenya that corresponded with the introduction and eventual dominance of the major SARS-COV-2 variants of concern, excepting the first 2 waves that were both wildtype virus. We estimate that more than 1000 SARS CoV 2 introductions occurred in the two-year epidemic period (March 2020 to September 2022) and a total of 930 introductions were associated with variants of concern namely Beta (n=78), Alpha(n=108), Delta(n=239) and Omicron (n=505). A total of 29 introductions were associated with A.23.1 variant that circulated in high frequencies in Uganda and Rwanda. The actual number of introductions is likely to be higher than these conservative estimates due to limited genomic sequencing. Our data suggested that cryptic transmission was usually underway prior to the first real-time identification of a new variant, and that multiple introductions were responsible. Following emergence of each VOC and subsequent introduction, transmission patterns were associated with hotspots of transmission in Coast, Nairobi and Western Kenya and follows established land and air transport corridors. Understanding the introduction and dispersal of major circulating variants and identifying the sources of new introductions is important to inform public health control strategies within Kenya and the larger East-African region. Border control and case finding reactive to new variants is unlikely to be a successful control strategy.

Epidemiological impact and cost-effectiveness analysis of COVID-19 vaccination in Kenya (preprint)

ABSTRACT Background Few studies have assessed the benefits of COVID-19 vaccines in settings where most of the population had been exposed to SARS-CoV-2 infection. Methods We conducted a cost-effectiveness analysis of COVID-19 vaccine in Kenya from a societal perspective over a 1.5-year time frame. An age-structured transmission model assumed at least 80% of the population to have prior natural immunity when an immune escape variant was introduced. We examine the effect of slow (18 months) or rapid (6 months) vaccine roll-out with vaccine coverage of 30%, 50% or 70% of the adult (> 18 years) population prioritizing roll-out in over 50-year olds (80% uptake in all scenarios). Cost data were obtained from primary analyses. We assumed vaccine procurement at $7 per dose and vaccine delivery costs of $3.90-$6.11 per dose. The cost-effectiveness threshold was USD 919. Findings Slow roll-out at 30% coverage largely targets over 50-year-olds and resulted in 54% fewer deaths (8,132(7,914 to 8,373)) than no vaccination and was cost-saving (ICER=US$-1,343 (-1,345 to - 1,341) per DALY averted). Increasing coverage to 50% and 70%, further reduced deaths by 12% (810 (757 to 872) and 5% (282 (251 to 317) but was not cost-effective, using Kenya’s cost-effectiveness threshold ($ 919.11). Rapid roll-out with 30% coverage averted 63% more deaths and was more cost-saving (ICER=$-1,607 (-1,609 to -1,604) per DALY averted) compared to slow roll-out at the same coverage level, but 50% and 70% coverage scenarios were not cost-effective. Interpretation With prior exposure partially protecting much of the Kenyan population, vaccination of young adults may no longer be cost-effective. KEY QUESTIONS What is already known? The COVID-19 pandemic has led to a substantial number of cases and deaths in low-and middle-income countries. COVID-19 vaccines are considered the main strategy of curtailing the pandemic. However, many African nations are still at the early phase of vaccination. Evidence on the cost-effectiveness of COVID-19 vaccines are useful in estimating value for money and illustrate opportunity costs. However, there is a need to balance these economic outcomes against the potential impact of vaccination. What are the new findings? In Kenya, a targeted vaccination strategy that prioritizes those of an older age and is deployed at a rapid rollout speed achieves greater marginal health impacts and is better value for money. Given the existing high-level population protection to COVID-19 due to prior exposure, vaccination of younger adults is less cost-effective in Kenya. What do the new findings imply? Rapid deployment of vaccines during a pandemic averts more cases, hospitalisations, and deaths and is more cost-effective. Against a context of constrained fiscal space for health, it is likely more prudent for Kenya to target those at severe risk of disease and possibly other vulnerable populations rather than to the whole population.

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance. (preprint)

Investment in Africa over the past year with regards to SARS-CoV-2 genotyping has led to a massive increase in the number of sequences, exceeding 100,000 genomes generated to track the pandemic on the continent. Our results show an increase in the number of African countries able to sequence within their own borders, coupled with a decrease in sequencing turnaround time. Findings from this genomic surveillance underscores the heterogeneous nature of the pandemic but we observe repeated dissemination of SARS-CoV-2 variants within the continent. Sustained investment for genomic surveillance in Africa is needed as the virus continues to evolve, particularly in the low vaccination landscape. These investments are very crucial for preparedness and response for future pathogen outbreaks.

Genomic Epidemiology of SARS-CoV-2 in Seychelles, 2020-2021 (preprint)

By 31st December 2021, Seychelles, an archipelago of 115 islands in the Indian Ocean, had confirmed 24,788 cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first SARS-CoV-2 cases in Seychelles were reported on 14th March 2020, but cases remained low until January 2021, when a surge of SARS-CoV-2 cases was observed on the islands. Here, we investigated the potential drivers of the surge by genomic analysis 1,056 SARS-CoV-2 positive samples collected in Seychelles between 14th March 2020 and 31st December 2021. The Seychelles genomes were classified into 32 Pango lineages, 1,042 of which fell within four variants of concern i.e., Alpha, Beta, Delta and Omicron. Sporadic of SARS-CoV-2 detected in Seychelles in 2020 were mainly of lineage B.1 (Europe origin) but this lineage was rapidly replaced by Beta variant starting January 2021, and which was also subsequently replaced by the Delta variant in May 2021 that dominated till November 2021 when Omicron cases were identified. Using ancestral state reconstruction approach, we estimated at least 78 independent SARS-CoV-2 introduction events into Seychelles during the study period. Majority of viral introductions into Seychelles occurred in 2021, despite substantial COVID-19 restrictions in place during this period. We conclude that the surge of SARS-CoV-2 cases in Seychelles in January 2021 was primarily due to introduction of the more transmissible SARS-CoV-2 variants into the islands.

Detection of SARS-CoV-2 variant 501Y.V2 in Comoros Islands in January 2021.

Background. Genomic data is key in understanding the spread and evolution of SARS-CoV-2 pandemic and informing the design and evaluation of interventions. However, SARS-CoV-2 genomic data remains scarce across Africa, with no reports yet from the Indian Ocean islands. Methods. We genome sequenced six SARS-CoV-2 positive samples from the first major infection wave in the Union of Comoros in January 2021 and undertook detailed phylogenetic analysis. Results. All the recovered six genomes classified within the 501Y.V2 variant of concern (also known as lineage B.1.351) and appeared to be from 2 sub-clusters with the most recent common ancestor dated 30 th Oct-2020 (95% Credibility Interval: 06 th Sep-2020 to 10 th Dec-2020). Comparison of the Comoros genomes with those of 501Y.V2 variant of concern from other countries deposited into the GISAID database revealed their close association with viruses identified in France and Mayotte (part of the Comoros archipelago and a France, Overseas Department). Conclusions. The recovered genomes, albeit few, confirmed local transmission following probably multiple introductions of the SARS-CoV-2 501Y.V2 variant of concern during the Comoros's first major COVID-19 wave. These findings demonstrate the importance of genomic surveillance and have implications for ongoing control strategies on the islands.

A computational framework for modelling infectious disease policy based on age and household structure with applications to the COVID-19 pandemic (preprint)

The widespread, and in many countries unprecedented, use of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic has highlighted the need for mathematical models which can estimate the impact of these measures while accounting for the highly heterogeneous risk profile of COVID-19. Models accounting either for age structure or the household structure necessary to explicitly model many NPIs are commonly used in infectious disease modelling, but models incorporating both levels of structure present substantial computational and mathematical challenges due to their high dimensionality. Here we present a modelling framework for the spread of an epidemic that includes explicit representation of age structure and household structure. Our model is formulated in terms of tractable systems of ordinary differential equations for which we provide an open-source Python implementation. Such tractability leads to significant benefits for model calibration, exhaustive evaluation of possible parameter values, and interpretability of results. We demonstrate the flexibility of our model through four policy case studies, where we quantify the likely benefits of the following measures which were either considered or implemented in the UK during the current COVID-19 pandemic: control of within- and between-household mixing through NPIs; formation of support bubbles during lockdown periods; out-of-household isolation (OOHI); and temporary relaxation of NPIs during holiday periods. Our ordinary differential equation formulation and associated analysis demonstrate that multiple dimensions of risk stratification and social structure can be incorporated into infectious disease models without sacrificing mathematical tractability. This model and its software implementation expand the range of tools available to infectious disease policy analysts.

Genomic surveillance reveals the spread patterns of SARS-CoV-2 in coastal Kenya during the first two waves (preprint)

The transmission networks of SARS-CoV-2 in sub-Saharan Africa remain poorly understood. We analyzed 684 genomes from samples collected across six counties in coastal Kenya during the first two waves (March 2020 - February 2021). Up to 32 Pango lineages were detected in the local sample with six accounting for 88.0% of the sequenced infections: B.1 (60.4%), B.1.1 (8.9%), B.1.549 (7.9%), B.1.530 (6.4%), N.8 (4.4%) and A (3.1%). In a contemporaneous global sample, 571 lineages were identified, 247 for Africa and 88 for East Africa. We detected 262 location transition events comprising: 64 viral imports into Coastal Kenya; 26 viral exports from coastal Kenya; and 172 inter-county import/export events. Most international viral imports (61%) and exports (88%) occurred through Mombasa, a key coastal touristic and commercial center; and many occurred prior to June 2020, when stringent local COVID-19 restriction measures were enforced. After this period, local transmission dominated, and distinct local phylogenies were seen. Our analysis supports moving control strategies from a focus on international travel to local transmission.

COVID-19 Transmission Dynamics Underlying Epidemic Waves in Kenya (preprint)

Policy decisions on COVID-19 interventions should be informed by a local, regional and national understanding of SARS-CoV-2 transmission. Epidemic waves may result when restrictions are lifted or poorly adhered to, variants with new phenotypic properties successfully invade, or when infection spreads to susceptible sub-populations. Three COVID-19 epidemic waves have been observed in Kenya. Using a mechanistic mathematical model we explain the first two distinct waves by differences in contact rates in high and low social-economic groups, and the third wave by the introduction of a new higher-transmissibility variant. Reopening schools led to a minor increase in transmission between the second and third waves. Our predictions of current population exposure in Kenya (∼75% June 1st) have implications for a fourth wave and future control strategies. One Sentence Summary COVID-19 spread in Kenya is explained by mixing heterogeneity and a variant less constrained by high population exposure

Improving SARS-CoV-2 cumulative incidence estimation through mixture modelling of antibody levels (preprint)

As countries decide on vaccination strategies and how to ease movement restrictions, estimates of cumulative incidence of SARS-CoV-2 infection are essential in quantifying the extent to which populations remain susceptible to COVID-19. Cumulative incidence is usually estimated from seroprevalence data, where seropositives are defined by an arbitrary threshold antibody level, and adjusted for sensitivity and specificity at that threshold. This does not account for antibody waning nor for lower antibody levels in asymptomatic or mildly symptomatic cases. Mixture modelling can estimate cumulative incidence from antibody-level distributions without requiring adjustment for sensitivity and specificity. To illustrate the bias in standard threshold-based seroprevalence estimates, we compared both approaches using data from several Kenyan serosurveys. Compared to the mixture model estimate, threshold analysis underestimated cumulative incidence by 31% (IQR: 11 to 41) on average. Until more discriminating assays are available, mixture modelling offers an approach to reduce bias in estimates of cumulative incidence.

Seroprevalence of Antibodies to SARS-CoV-2 among Health Care Workers in Kenya (preprint)

BackgroundFew studies have assessed the seroprevalence of antibodies against SARS-CoV-2 among Health Care Workers (HCWs) in Africa. We report findings from a survey among HCWs in three counties in Kenya. MethodsWe recruited 684 HCWs from Kilifi (rural), Busia (rural) and Nairobi (urban) counties. The serosurvey was conducted between 30th July 2020 and 4th December 2020. We tested for IgG antibodies to SARS-CoV-2 spike protein using ELISA. Assay sensitivity and specificity were 93% (95% CI 88-96%) and 99% (95% CI 98-99.5%), respectively. We adjusted prevalence estimates using Bayesian modeling to account for assay performance. ResultsCrude overall seroprevalence was 19.7% (135/684). After adjustment for assay performance seroprevalence was 20.8% (95% CI 17.5-24.4%). Seroprevalence varied significantly (p<0.001) by site: 43.8% (CI 35.8-52.2%) in Nairobi, 12.6% (CI 8.8-17.1%) in Busia and 11.5% (CI 7.2-17.6%) in Kilifi. In a multivariable model controlling for age, sex and site, professional cadre was not associated with differences in seroprevalence. ConclusionThese initial data demonstrate a high seroprevalence of antibodies to SARS-CoV-2 among HCWs in Kenya. There was significant variation in seroprevalence by region, but not by cadre.

Spatially resolved simulations of the spread of COVID-19 in European countries (preprint)

We explore the spatial and temporal spread of the novel SARS-CoV-2 virus under containment measures in three European countries based on fits to data of the early outbreak. Using data from Spain and Italy, we estimate an age dependent infection fatality ratio for SARS-CoV-2, as well as risks of hospitalization and intensive care admission. We use them in a model that simulates the dynamics of the virus using an age structured, spatially detailed agent based approach, that explicitly incorporates governamental interventions, changes in mobility and contact patterns occurred during the COVID-19 outbreak in each country.Our simulations reproduce several of the features of its spatio-temporal spread in the three countries studied. They show that containment measures combined with high density are responsible for the containment of cases within densely populated areas, and that spread to less densely populated areas occurred during the late stages of the first wave. The capability to reproduce observed features of the spatio-temporal dynamics of SARS-CoV-2 makes this model a potential candidate for forecasting the dynamics of SARS-CoV-2 in other settings, and we recommend its application in low and lower-middle countries which remain understudied.

Infection patterns of endemic human coronaviruses in rural households in coastal Kenya (preprint)

Introduction The natural history and transmission patterns of endemic human coronaviruses are of increased interest following the emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Methods In rural Kenya 483 individuals from 47 households were followed for six months (2009-10) with nasopharyngeal swabs collected twice weekly regardless of symptoms. A total of 16,918 swabs were tested for human coronavirus (hCoV) OC43, NL63 and 229E and other respiratory viruses using polymerase chain reaction. Results From 346 (71.6%) household members 629 hCoV infection episodes were defined with 36.3% being symptomatic: varying by hCoV type and decreasing with age. Symptomatic episodes (aHR=0.6 (95% CI:0.5-0.8) or those with elevated peak viral load (medium aHR=0.4 (0.3-0.6); high aHR=0.31 (0.2-0.4)) had longer viral shedding compared to their respective counterparts. Homologous reinfections were observed in 99 (19.9%) of 497 first infections. School-age children (55%) were the most common index cases with those having medium (aOR=5.3 (2.3 – 12.0)) or high (8.1 (2.9 - 22.5)) peak viral load most often generating secondary cases. Conclusion Household coronavirus infection was common, frequently asymptomatic and mostly introduced by school-age children. Secondary transmission was influenced by viral load of index cases. Homologous-type reinfection was common. These data may be insightful for SARS-CoV-2.

Tracking the introduction and spread of SARS-CoV-2 in coastal Kenya (preprint)

We generated 274 SARS-CoV-2 genomes from samples collected during the early phase of the Kenyan pandemic. Phylogenetic analysis identified 8 global lineages and at least 76 independent SARS-CoV-2 introductions into Kenyan coast. The dominant B.1 lineage (European origin) accounted for 82.1% of the cases. Lineages A, B and B.4 were detected from screened individuals at the Kenya-Tanzania border or returning travellers but did not lead to established transmission. Though multiple lineages were introduced in coastal Kenya within three months following the initial confirmed case, none showed extensive local expansion other than cases characterised by lineage B.1, which accounted for 45 of the 76 introductions. We conclude that the international points of entry were important conduits of SARS-CoV-2 importations. We speculate that early public health responses prevented many introductions leading to established transmission, but nevertheless a few undetected introductions were sufficient to give rise to an established epidemic.

Tracking the introduction and spread of SARS-CoV-2 in coastal Kenya (preprint)

We generated 274 SARS-CoV-2 genomes from samples collected during the early phase of the Kenyan pandemic. Phylogenetic analysis identified 8 global lineages and at least 76 independent SARS-CoV-2 introductions into Kenyan coast. The dominant B.1 lineage (European origin) accounted for 82.1% of the cases. Lineages A, B and B.4 were detected from screened individuals at the Kenya-Tanzania border or returning travellers but did not lead to established transmission. Though multiple lineages were introduced in coastal Kenya within three months following the initial confirmed case, none showed extensive local expansion other than cases characterised by lineage B.1, which accounted for 45 of the 76 introductions. We conclude that the international points of entry were important conduits of SARS-CoV-2 importations. We speculate that early public health responses prevented many introductions leading to established transmission, but nevertheless a few undetected introductions were sufficient to give rise to an established epidemic.

MAJORA: Continuous integration supporting decentralised sequencing for SARS-CoV-2 genomic surveillance (preprint)

Genomic epidemiology has become an increasingly common tool for epidemic response. Recent technological advances have made it possible to sequence genomes rapidly enough to inform outbreak response, and cheaply enough to justify dense sampling of even large epidemics. With increased availability of sequencing it is possible for agile networks of sequencing facilities to collaborate on the sequencing and analysis of epidemic genomic data. In response to the ongoing SARS-CoV-2 pandemic in the United Kingdom, the COVID-19 Genomics UK (COG-UK) consortium was formed with the aim of rapidly sequencing SARS-CoV-2 genomes as part of a national-scale genomic surveillance strategy. The network consists of universities, academic institutes, regional sequencing centres and the four UK Public Health Agencies. We describe the development and deployment of Majora, an encompassing digital infrastructure to address the challenge of collecting and integrating both genomic sequencing data and sample-associated metadata produced across the COG-UK network. The system was designed and implemented pragmatically to stand up capacity rapidly in a pandemic caused by a novel virus. This approach has underpinned the success of COG-UK, which has rapidly become the leading contributor of SARS-CoV-2 genomes to international databases and has generated over 60,000 sequences to date.