Revealing fine-scale spatiotemporal differences in SARS-CoV-2 introduction and spread

Model predicted human mobility explains COVID-19 transmission in urban space without behavioral data

The SARS-CoV-2 virus is primarily transmitted through in-person interactions, and so its growth in urban space is a complex function of human mobility behaviors that cannot be adequately explained by standard epidemiological models. Recent studies leveraged fine-grained urban mobility data to accurately model the viral spread, but such data pose privacy concerns and are often difficult to collect, especially in low- and middle-income countries (LMICs). Here, we show that the metapopulation epidemiological model incorporated with a simple gravity mobility model can be sufficient to capture most of the complex epidemic dynamics in urban space, largely reducing the need for empirical mobility data. Extensive experiments on 30 cities in the United States, India and Brazil show that our model consistently reproduces complex, distinctive COVID-19 growth curves with high accuracy. It also provides a theoretical explanation of the emergence of urban "superspreading", where a few high-risk neighborhoods account for most subsequent infections. Furthermore, with the aid of the proposed framework, we can inform mobility-aware travel restrictions to achieve a better balance between social cost and disease prevention, which facilitates sustainable epidemic control and supports the gradual transition to a post-pandemic world.

SARS-CoV-2 introductions to the island of Ireland: a phylogenetic and geospatiotemporal study of infection dynamics

BACKGROUND

Ireland's COVID-19 response combined extensive SARS-CoV-2 testing to estimate incidence, with whole genome sequencing (WGS) for genome surveillance. As an island with two political jurisdictions-Northern Ireland (NI) and Republic of Ireland (RoI)-and access to detailed passenger travel data, Ireland provides a unique setting to study virus introductions and evaluate public health measures. Using a substantial Irish genomic dataset alongside global data from GISAID, this study aimed to trace the introduction and spread of SARS-CoV-2 across the island.

METHODS

We recursively searched for 29,518 SARS-CoV-2 genome sequences collected in Ireland from March 2020 to June 2022 within the global SARS-CoV-2 phylogenetic tree and identified clusters based on shared last common non-Irish ancestors. A maximum parsimony approach was used to assign a likely country of origin to each cluster. The geographic locations and collection dates of the samples in each introduction cluster were used to map the spread of the virus across Ireland. Downsampling was used to model the impact of varying levels of sequencing and normalisation for population permitted comparison between jurisdictions.

RESULTS

Six periods spanning the early introductions and the emergence of Alpha, Delta, and Omicron variants were studied in detail. Among 4439 SARS-CoV-2 introductions to Ireland, 2535 originated in England, with additional cases largely from the rest of Great Britain, United States of America, and Northwestern Europe. Introduction clusters ranged in size from a single to thousands of cases. Introductions were concentrated in the densely populated Dublin and Belfast areas, with many clusters spreading islandwide. Genetic phylogeny was able to effectively trace localised transmission patterns. Introduction rates were similar in NI and RoI for most variants, except for Delta, which was more frequently introduced to NI.

CONCLUSIONS

Tracking individual introduction events enables detailed modelling of virus spread patterns and clearer assessment of the effectiveness of control measures. Stricter travel restrictions in RoI likely reduced Delta introductions but not infection rates, which were similar across jurisdictions. Local and global sequencing levels influence the information available from phylogenomic analyses and we describe an approach to assess the ability of a chosen WGS level to detect virus introductions.

Integrating Contact Tracing Data to Enhance Outbreak Phylodynamic Inference: A Deep Learning Approach

Phylodynamics is central to understanding infectious disease dynamics through the integration of genomic and epidemiological data. Despite advancements, including the application of deep learning to overcome computational limitations, significant challenges persist due to data inadequacies and statistical unidentifiability of key parameters. These issues are particularly pronounced in poorly resolved phylogenies, commonly observed in outbreaks such as SARS-CoV-2. In this study, we conducted a thorough evaluation of PhyloDeep, a deep learning inference tool for phylodynamics, assessing its performance on poorly resolved phylogenies. Our findings reveal the limited predictive accuracy of PhyloDeep (and other state-of-the-art approaches) in these scenarios. However, models trained on poorly resolved, realistically simulated trees demonstrate improved predictive power, despite not being infallible, especially in scenarios with superspreading dynamics, whose parameters are challenging to capture accurately. Notably, we observe markedly improved performance through the integration of minimal contact tracing data, which refines poorly resolved trees. Applying this approach to a sample of SARS-CoV-2 sequences partially matched to contact tracing from Hong Kong yields informative estimates of superspreading potential, extending beyond the scope of contact tracing data alone. Our findings demonstrate the potential for enhancing phylodynamic analysis through complementary data integration, ultimately increasing the precision of epidemiological predictions crucial for public health decision-making and outbreak control.

Local-scale phylodynamics reveal differential community impact of SARS-CoV-2 in a metropolitan US county

SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.

Modeling target-density-based cull strategies to contain foot-and-mouth disease outbreaks

Total ring depopulation is sometimes used as a management strategy for emerging infectious diseases in livestock, which raises ethical concerns regarding the potential slaughter of large numbers of healthy animals. We evaluated a farm-density-based ring culling strategy to control foot-and-mouth disease (FMD) in the United Kingdom (UK), which may allow for some farms within rings around infected premises (IPs) to escape depopulation. We simulated this reduced farm density, or "target density", strategy using a spatially-explicit, stochastic, state-transition algorithm. We modeled FMD spread in four counties in the UK that have different farm demographics, using 740,000 simulations in a full-factorial analysis of epidemic impact measures (i.e., culled animals, culled farms, and epidemic length) and cull strategy parameters (i.e., target farm density, daily farm cull capacity, and cull radius). All of the cull strategy parameters listed above were drivers of epidemic impact. Our simulated target density strategy was usually more effective at combatting FMD compared with traditional total ring depopulation when considering mean culled animals and culled farms and was especially effective when daily farm cull capacity was low. The differences in epidemic impact measures among the counties are likely driven by farm demography, especially differences in cattle and farm density. To prevent over-culling and the associated economic, organizational, ethical, and psychological impacts, the target density strategy may be worth considering in decision-making processes for future control of FMD and other diseases.

Genomic epidemiology reveals the dominance of Hennepin County in the transmission of SARS-CoV-2 in Minnesota from 2020 to 2022

IMPORTANCE

We analyzed over 22,000 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes of patient samples tested at Mayo Clinic Laboratories during a 2-year period in the COVID-19 pandemic, which included Alpha, Delta, and Omicron variants of concern to examine the roles and relationships of Minnesota virus transmission. We found that Hennepin County, the most populous county, drove the transmission of SARS-CoV-2 viruses in the state after including the formation of earlier clades including 20A, 20C, and 20G, as well as variants of concern Alpha and Delta. We also found that Hennepin County was the source for most of the county-to-county introductions after an initial predicted introduction with the virus in early 2020 from an international source, while other counties acted as transmission "sinks." In addition, major policies, such as the end of the lockdown period in 2020 or the end of all restrictions in 2021, did not appear to have an impact on virus diversity across individual counties.

Metagenomic sequencing detects human respiratory and enteric viruses in air samples collected from congregate settings

Innovative methods for evaluating virus risk and spread, independent of test-seeking behavior, are needed to improve routine public health surveillance, outbreak response, and pandemic preparedness. Throughout the COVID-19 pandemic, environmental surveillance strategies, including wastewater andair sampling, have been used alongside widespread individual-based SARS-CoV-2 testing programs to provide population-level data. These environmental surveillance strategies have predominantly relied on pathogen-specific detection methods to monitor viruses through space and time. However, this provides a limited picture of the virome present in an environmental sample, leaving us blind to most circulating viruses. In this study, we explore whether pathogen-agnostic deep sequencing can expand the utility of air sampling to detect many human viruses. We show that sequence-independent single-primer amplification sequencing of nucleic acids from air samples can detect common and unexpected human respiratory and enteric viruses, including influenza virus type A and C, respiratory syncytial virus, human coronaviruses, rhinovirus, SARS-CoV-2, rotavirus, mamastrovirus, and astrovirus.

Online Phylogenetics with matOptimize Produces Equivalent Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies than de novo and Maximum-Likelihood Implementations

Phylogenetics has been foundational to SARS-CoV-2 research and public health policy, assisting in genomic surveillance, contact tracing, and assessing emergence and spread of new variants. However, phylogenetic analyses of SARS-CoV-2 have often relied on tools designed for de novo phylogenetic inference, in which all data are collected before any analysis is performed and the phylogeny is inferred once from scratch. SARS-CoV-2 data sets do not fit this mold. There are currently over 14 million sequenced SARS-CoV-2 genomes in online databases, with tens of thousands of new genomes added every day. Continuous data collection, combined with the public health relevance of SARS-CoV-2, invites an "online" approach to phylogenetics, in which new samples are added to existing phylogenetic trees every day. The extremely dense sampling of SARS-CoV-2 genomes also invites a comparison between likelihood and parsimony approaches to phylogenetic inference. Maximum likelihood (ML) and pseudo-ML methods may be more accurate when there are multiple changes at a single site on a single branch, but this accuracy comes at a large computational cost, and the dense sampling of SARS-CoV-2 genomes means that these instances will be extremely rare because each internal branch is expected to be extremely short. Therefore, it may be that approaches based on maximum parsimony (MP) are sufficiently accurate for reconstructing phylogenies of SARS-CoV-2, and their simplicity means that they can be applied to much larger data sets. Here, we evaluate the performance of de novo and online phylogenetic approaches, as well as ML, pseudo-ML, and MP frameworks for inferring large and dense SARS-CoV-2 phylogenies. Overall, we find that online phylogenetics produces similar phylogenetic trees to de novo analyses for SARS-CoV-2, and that MP optimization with UShER and matOptimize produces equivalent SARS-CoV-2 phylogenies to some of the most popular ML and pseudo-ML inference tools. MP optimization with UShER and matOptimize is thousands of times faster than presently available implementations of ML and online phylogenetics is faster than de novo inference. Our results therefore suggest that parsimony-based methods like UShER and matOptimize represent an accurate and more practical alternative to established ML implementations for large SARS-CoV-2 phylogenies and could be successfully applied to other similar data sets with particularly dense sampling and short branch lengths.

Metagenomic sequencing detects human respiratory and enteric viruses in air samples collected from congregate settings

Innovative methods for evaluating virus risk and spread, independent of test-seeking behavior, are needed to improve routine public health surveillance, outbreak response, and pandemic preparedness. Throughout the COVID-19 pandemic, environmental surveillance strategies, including wastewater and air sampling, have been used alongside widespread individual-based SARS-CoV-2 testing programs to provide population-level data. These environmental surveillance strategies have predominantly relied on pathogen-specific detection methods to monitor viruses through space and time. However, this provides a limited picture of the virome present in an environmental sample, leaving us blind to most circulating viruses. In this study, we explore whether pathogen-agnostic deep sequencing can expand the utility of air sampling to detect many human viruses. We show that sequence-independent single-primer amplification sequencing of nucleic acids from air samples can detect common and unexpected human respiratory and enteric viruses, including influenza virus type A and C, respiratory syncytial virus, human coronaviruses, rhinovirus, SARS-CoV-2, rotavirus, mamastrovirus, and astrovirus.

Genomic Surveillance Reveals the Rapid Expansion of the XBB Lineage among Circulating SARS-CoV-2 Omicron Lineages in Southeastern Wisconsin, USA

SARS-CoV-2 caused a life-threatening COVID-19 pandemic outbreak worldwide. The Southeastern Region of Wisconsin, USA (SERW) includes large urban Milwaukee and six suburban counties, namely Kenosha, Ozaukee, Racine, Walworth, Washington and Waukesha. Due to the lack of detailed SARS-CoV-2 genomic surveillance in the suburban populations of the SERW, whole-genome sequencing was employed to investigate circulating SARS-CoV-2 lineages and characterize dominant XBB lineages among this SERW population from November 2021 to April 2023. For an unbiased data analysis, we combined our 6709 SARS-CoV-2 sequences with 1520 sequences from the same geographical region submitted by other laboratories. Our study shows that SARS-CoV-2 genomes were distributed into 357 lineages/sublineages belonging to 13 clades, of which 88.8% were from Omicron. We document dominant sublineages XBB.1.5 and surging XBB.1.16 and XBB.1.9.1 with a few additional functional mutations in Spike, which are known to contribute to higher viral reproduction, enhanced transmission and immune evasion. Mutational profile assessment of XBB.1.5 Spike identifies 38 defining mutations with high prevalence occurring in 49.8-99.6% of the sequences studied, of which 32 mutations were in three functional domains. Phylogenetic and genetic relatedness between XBB.1.5 sequences reveal potential virus transmission occurring within households and within and between Southeastern Wisconsin counties. A comprehensive phylogeny of XBB.1.5 with global sub-dataset sequences confirms the wide spread of genetically similar SARS-CoV-2 strains within the same geographical area. Altogether, this study identified proportions of circulating Omicron variants and genetic characterization of XBB.1.5 in the SERW population, which helped state and national public health agencies to make compelling mitigation efforts to reduce COVID-19 transmission in the communities and monitor emerging lineages for their impact on diagnostics, treatments and vaccines.

Genomic epidemiology reveals the dominance of Hennepin County in transmission of SARS-CoV-2 in Minnesota from 2020-2022

SARS-CoV-2 has had an unprecedented impact on human health and highlights the need for genomic epidemiology studies to increase our understanding of virus evolution and spread, and to inform policy decisions. We sequenced viral genomes from over 22,000 patient samples tested at Mayo Clinic Laboratories between 2020-2022 and use Bayesian phylodynamics to describe county and regional spread in Minnesota. The earliest introduction into Minnesota was to Hennepin County from a domestic source around January 22, 2020; six weeks before the first confirmed case in the state. This led to the virus spreading to Northern Minnesota, and eventually, the rest of the state. International introductions were most abundant in Hennepin (home to the Minneapolis/St. Paul International (MSP) airport) totaling 45 (out of 107) over the two-year period. Southern Minnesota counties were most common for domestic introductions with 19 (out of 64), potentially driven by bordering states such as Iowa and Wisconsin as well as Illinois which is nearby. Hennepin also was, by far, the most dominant source of in-state transmissions to other Minnesota locations (n=772) over the two-year period. We also analyzed the diversity of the location source of SARS-CoV-2 viruses in each county and noted the timing of state-wide policies as well as trends in clinical cases. Neither the number of clinical cases or major policy decisions, such as the end of the lockdown period in 2020 or the end of all restrictions in 2021, appeared to have impact on virus diversity across each individual county.

TransCode: Uncovering COVID-19 transmission patterns via deep learning

BACKGROUND

The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale, especially in densely populated regions. In this study, we aim to discover such fine-scale transmission patterns via deep learning.

METHODS

We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors. First, in Hong Kong, China, we construct the mobility trajectories of confirmed cases using their visiting records. Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution. Integrating the spatial and temporal information, we represent the TransCode via spatiotemporal transmission networks. Further, we propose a deep transfer learning model to adapt the TransCode of Hong Kong, China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises: New York City, San Francisco, Toronto, London, Berlin, and Tokyo, where fine-scale data are limited. All the data used in this study are publicly available.

RESULTS

The TransCode of Hong Kong, China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns (e.g., the imported and exported transmission intensities) at the district and constituency levels during different COVID-19 outbreaks waves. By adapting the TransCode of Hong Kong, China to other data-limited densely populated metropolises, the proposed method outperforms other representative methods by more than 10% in terms of the prediction accuracy of the disease dynamics (i.e., the trend of case numbers), and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level.

CONCLUSIONS

The fine-scale transmission patterns due to the metapopulation level mobility (e.g., travel across different districts) and contact behaviors (e.g., gathering in social-economic centers) are one of the main contributors to the rapid spread of the virus. Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions.

Local-Scale phylodynamics reveal differential community impact of SARS-CoV-2 in metropolitan US county

SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.

The methodologies to assess the effectiveness of non-pharmaceutical interventions during COVID-19: a systematic review

Non-pharmaceutical interventions, such as school closures and stay-at-home orders, have been implemented around the world to control the spread of SARS-CoV-2. Their effectiveness in improving health-related outcomes has been the subject of numerous empirical studies. However, these studies show fairly large variation among methodologies in use, reflecting the absence of an established methodological framework. On the one hand, variation in methodologies may be desirable to assess the robustness of results; on the other hand, a lack of common standards can impede comparability among studies. To establish a comprehensive overview over the methodologies in use, we conducted a systematic review of studies assessing the effectiveness of non-pharmaceutical interventions between January 1, 2020 and January 12, 2021 (n = 248). We identified substantial variation in methodologies with respect to study setting, outcome, intervention, methodological approach, and effectiveness assessment. On this basis, we point to shortcomings of existing studies and make recommendations for the design of future studies.

Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic

Determining the transmissibility, prevalence and patterns of movement of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections is central to our understanding of the impact of the pandemic and to the design of effective control strategies. Phylogenies (evolutionary trees) have provided key insights into the international spread of SARS-CoV-2 and enabled investigation of individual outbreaks and transmission chains in specific settings. Phylodynamic approaches combine evolutionary, demographic and epidemiological concepts and have helped track virus genetic changes, identify emerging variants and inform public health strategy. Here, we review and synthesize studies that illustrate how phylogenetic and phylodynamic techniques were applied during the first year of the pandemic, and summarize their contributions to our understanding of SARS-CoV-2 transmission and control.

A Single Early Introduction Governed Viral Diversity in the Second Wave of SARS-CoV-2 Epidemic in Hungary

Retrospective evaluation of past waves of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic is key for designing optimal interventions against future waves and novel pandemics. Here, we report on analysing genome sequences of SARS-CoV-2 from the first two waves of the epidemic in 2020 in Hungary, mirroring a suppression and a mitigation strategy, respectively. Our analysis reveals that the two waves markedly differed in viral diversity and transmission patterns. Specifically, unlike in several European areas or in the USA, we have found no evidence for early introduction and cryptic transmission of the virus in the first wave of the pandemic in Hungary. Despite the introduction of multiple viral lineages, extensive community spread was prevented by a timely national lockdown in March 2020. In sharp contrast, the majority of the cases in the much larger second wave can be linked to a single transmission lineage of the pan-European B.1.160 variant. This lineage was introduced unexpectedly early, followed by a 2-month-long cryptic transmission before a soar of detected cases in September 2020. Epidemic analysis has revealed that the dominance of this lineage in the second wave was not associated with an intrinsic transmission advantage. This finding is further supported by the rapid replacement of B.1.160 by the alpha variant (B.1.1.7) that launched the third wave of the epidemic in February 2021. Overall, these results illustrate how the founder effect in combination with the cryptic transmission, instead of repeated international introductions or higher transmissibility, can govern viral diversity.

Online Phylogenetics using Parsimony Produces Slightly Better Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies than de novo and Maximum-Likelihood Approaches

Phylogenetics has been foundational to SARS-CoV-2 research and public health policy, assisting in genomic surveillance, contact tracing, and assessing emergence and spread of new variants. However, phylogenetic analyses of SARS-CoV-2 have often relied on tools designed for de novo phylogenetic inference, in which all data are collected before any analysis is performed and the phylogeny is inferred once from scratch. SARS-CoV-2 datasets do not fit this mould. There are currently over 10 million sequenced SARS-CoV-2 genomes in online databases, with tens of thousands of new genomes added every day. Continuous data collection, combined with the public health relevance of SARS-CoV-2, invites an "online" approach to phylogenetics, in which new samples are added to existing phylogenetic trees every day. The extremely dense sampling of SARS-CoV-2 genomes also invites a comparison between likelihood and parsimony approaches to phylogenetic inference. Maximum likelihood (ML) methods are more accurate when there are multiple changes at a single site on a single branch, but this accuracy comes at a large computational cost, and the dense sampling of SARS-CoV-2 genomes means that these instances will be extremely rare because each internal branch is expected to be extremely short. Therefore, it may be that approaches based on maximum parsimony (MP) are sufficiently accurate for reconstructing phylogenies of SARS-CoV-2, and their simplicity means that they can be applied to much larger datasets. Here, we evaluate the performance of de novo and online phylogenetic approaches, and ML and MP frameworks, for inferring large and dense SARS-CoV-2 phylogenies. Overall, we find that online phylogenetics produces similar phylogenetic trees to de novo analyses for SARS-CoV-2, and that MP optimizations produce more accurate SARS-CoV-2 phylogenies than do ML optimizations. Since MP is thousands of times faster than presently available implementations of ML and online phylogenetics is faster than de novo , we therefore propose that, in the context of comprehensive genomic epidemiology of SARS-CoV-2, MP online phylogenetics approaches should be favored.

Dynamics of SARS-CoV-2 Alpha (B.1.1.7) variant spread: The wastewater surveillance approach

Wastewater based epidemiology (WBE) offers an overview of the SARS-CoV-2 variants circulating among the population thereby serving as a proper surveillance method. The variant of concern (VOC) Alpha was first identified in September 2020 in the United Kingdom, and rapidly became dominant across Europe. Our objective was to elucidate the Alpha VOC outcompetition rate and identify mutations in the spike glycoprotein (S) gene, indicative of the circulation of the Alpha VOC and/or other variants in the population through wastewater analysis. In the period covered by this study (November 2020-April 2021), forteen wastewater treatment plants (WWTPs) were weekly sampled. The total number of SARS-CoV-2 genome copies per L (GC/L) was determined with a Real-Time qPCR, targeting the N gene. Surveillance of the Alpha VOC circulation was ascertained using a duplex RT-qPCR, targeting and discriminating the S gene. Our results showed that in a period of 6 weeks the Alpha VOC was present in all the studied WWTPs, and became dominant in 11 weeks on average. The outcompetition rates of the Alpha VOC were estimated, and their relationship with different parameters statistically analyzed. The rapid spread of the Alpha VOC was influenced by its initial input and by the previous circulation of SARS-COV-2 in the population. This latter point could be explained by its higher transmissibility, particularly advantadgeous when a certain degree of herd immunity exists. Moreover, the presence of signature mutations of SARS-COV-2 variants were established by deep-sequencing of the complete S gene. The circulation of the Alpha VOC in the area under study was confirmed, and additionally two combinations of mutations in the S glycoprotein (T73A and D253N, and S477N and A522S) that could affect antibody binding were identified.

Statistical Challenges in Tracking the Evolution of SARS-CoV-2

Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.

Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria

The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves.

Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale leading to a tremendous amount of viral genome sequencing data. To assist in tracing infection pathways and design preventive strategies, a deep understanding of the viral genetic diversity landscape is needed. We present here a set of genomic surveillance tools from population genetics which can be used to better understand the evolution of this virus in humans. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic. We analyzed 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets. This approach enables real-time lineage identification, a clear description of the relationship between variants of concern, and efficient detection of recurrent mutations. Furthermore, time series change of Tajima's D by haplotype provides a powerful metric of lineage expansion. Finally, principal component analysis (PCA) highlights key steps in variant emergence and facilitates the visualization of genomic variation in the context of SARS-CoV-2 diversity. The computational framework presented here is simple to implement and insightful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of populations of humans and other organisms.

Unrecognized introductions of SARS-CoV-2 into the US state of Georgia shaped the early epidemic

In early 2020, as diagnostic and surveillance responses for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ramped up, attention focused primarily on returning international travelers. Here, we build on existing studies characterizing early patterns of SARS-CoV-2 spread within the USA by analyzing detailed clinical, molecular, and viral genomic data from the state of Georgia through March 2020. We find evidence for multiple early introductions into Georgia, despite relatively sparse sampling. Most sampled sequences likely stemmed from a single or small number of introductions from Asia three weeks prior to the state's first detected infection. Our analysis of sequences from domestic travelers demonstrates widespread circulation of closely related viruses in multiple US states by the end of March 2020. Our findings indicate that the exclusive focus on identifying SARS-CoV-2 in returning international travelers early in the pandemic may have led to a failure to recognize locally circulating infections for several weeks and point toward a critical need for implementing rapid, broadly targeted surveillance efforts for future pandemics.

Spatio-temporal dynamics of intra-host variability in SARS-CoV-2 genomes

During the course of the COVID-19 pandemic, large-scale genome sequencing of SARS-CoV-2 has been useful in tracking its spread and in identifying variants of concern (VOC). Viral and host factors could contribute to variability within a host that can be captured in next-generation sequencing reads as intra-host single nucleotide variations (iSNVs). Analysing 1347 samples collected till June 2020, we recorded 16 410 iSNV sites throughout the SARS-CoV-2 genome. We found ∼42% of the iSNV sites to be reported as SNVs by 30 September 2020 in consensus sequences submitted to GISAID, which increased to ∼80% by 30th June 2021. Following this, analysis of another set of 1774 samples sequenced in India between November 2020 and May 2021 revealed that majority of the Delta (B.1.617.2) and Kappa (B.1.617.1) lineage-defining variations appeared as iSNVs before getting fixed in the population. Besides, mutations in RdRp as well as RNA-editing by APOBEC and ADAR deaminases seem to contribute to the differential prevalence of iSNVs in hosts. We also observe hyper-variability at functionally critical residues in Spike protein that could alter the antigenicity and may contribute to immune escape. Thus, tracking and functional annotation of iSNVs in ongoing genome surveillance programs could be important for early identification of potential variants of concern and actionable interventions.

The emergence, genomic diversity and global spread of SARS-CoV-2

Since the first cases of COVID-19 were documented in Wuhan, China in 2019, the world has witnessed a devastating global pandemic, with more than 238 million cases, nearly 5 million fatalities and the daily number of people infected increasing rapidly. Here we describe the currently available data on the emergence of the SARS-CoV-2 virus, the causative agent of COVID-19, outline the early viral spread in Wuhan and its transmission patterns in China and across the rest of the world, and highlight how genomic surveillance, together with other data such as those on human mobility, has helped to trace the spread and genetic variation of the virus and has also comprised a key element for the control of the pandemic. We pay particular attention to characterizing and describing the international spread of the major variants of concern of SARS-CoV-2 that were first identified in late 2020 and demonstrate that virus evolution has entered a new phase. More broadly, we highlight our currently limited understanding of coronavirus diversity in nature, the rapid spread of the virus and its variants in such an increasingly connected world, the reduced protection of vaccines, and the urgent need for coordinated global surveillance using genomic techniques. In summary, we provide important information for the prevention and control of both the ongoing COVID-19 pandemic and any new diseases that will inevitably emerge in the human population in future generations.

Influenza viruses and coronaviruses: Knowns, unknowns, and common research challenges

The development of safe and effective vaccines in a record time after the emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a remarkable achievement, partly based on the experience gained from multiple viral outbreaks in the past decades. However, the Coronavirus Disease 2019 (COVID-19) crisis also revealed weaknesses in the global pandemic response and large gaps that remain in our knowledge of the biology of coronaviruses (CoVs) and influenza viruses, the 2 major respiratory viruses with pandemic potential. Here, we review current knowns and unknowns of influenza viruses and CoVs, and we highlight common research challenges they pose in 3 areas: the mechanisms of viral emergence and adaptation to humans, the physiological and molecular determinants of disease severity, and the development of control strategies. We outline multidisciplinary approaches and technological innovations that need to be harnessed in order to improve preparedeness to the next pandemic.

SARS-CoV-2 Genomic Surveillance Reveals Little Spread From a Large University Campus to the Surrounding Community

BACKGROUND

Coronavirus disease 2019 (COVID-19) has had high incidence rates at institutions of higher education (IHE) in the United States, but the transmission dynamics in these settings are poorly understood. It remains unclear to what extent IHE-associated outbreaks have contributed to transmission in nearby communities.

METHODS

We implemented high-density prospective genomic surveillance to investigate these dynamics at the University of Michigan and the surrounding community during the Fall 2020 semester (August 16-November 24). We sequenced complete severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from 1659 individuals, including 468 students, representing 20% of cases in students and 25% of total cases in Washtenaw County over the study interval.

RESULTS

Phylogenetic analysis identified >200 introductions into the student population, most of which were not related to other student cases. There were 2 prolonged student transmission clusters, of 115 and 73 individuals, that spanned multiple on-campus residences. Remarkably, <5% of nonstudent genomes were descended from student clusters, and viral descendants of student cases were rare during a subsequent wave of infections in the community.

CONCLUSIONS

The largest outbreaks among students at the University of Michigan did not significantly contribute to the rise in community cases in Fall 2020. These results provide valuable insights into SARS-CoV-2 transmission dynamics at the regional level.

Interventions to Disrupt Coronavirus Disease Transmission at a University, Wisconsin, USA, August-October 2020

University settings have demonstrated potential for coronavirus disease (COVID-19) outbreaks; they combine congregate living, substantial social activity, and a young population predisposed to mild illness. Using genomic and epidemiologic data, we describe a COVID-19 outbreak at the University of Wisconsin-Madison, Madison, Wisconsin, USA. During August-October 2020, a total of 3,485 students, including 856/6,162 students living in dormitories, tested positive. Case counts began rising during move-in week, August 25-31, 2020, then rose rapidly during September 1-11, 2020. The university initiated multiple prevention efforts, including quarantining 2 dormitories; a subsequent decline in cases was observed. Genomic surveillance of cases from Dane County, in which the university is located, did not find evidence of transmission from a large cluster of cases in the 2 quarantined dorms during the outbreak. Coordinated implementation of prevention measures can reduce COVID-19 spread in university settings and may limit spillover to the surrounding community.

Viral Sequencing to Investigate Sources of SARS-CoV-2 Infection in US Healthcare Personnel

BACKGROUND

Healthcare personnel (HCP) are at increased risk of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We posit that current infection control guidelines generally protect HCP from SARS-CoV-2 infection in a healthcare setting.

METHODS

In this retrospective case series, we used viral genomics to investigate the likely source of SARS-CoV-2 infection in HCP at a major academic medical institution in the Upper Midwest of the United States between 25 March and 27 December 2020. We obtained limited epidemiological data through informal interviews and review of the electronic health record and combined this information with healthcare-associated viral sequences and viral sequences collected in the broader community to infer the most likely source of infection in HCP.

RESULTS

We investigated SARS-CoV-2 infection clusters involving 95 HCP and 137 possible patient contact sequences. The majority of HCP infections could not be linked to a patient or coworker (55 of 95 [57.9%]) and were genetically similar to viruses circulating concurrently in the community. We found that 10.5% of HCP infections (10 of 95) could be traced to a coworker. Strikingly, only 4.2% (4 of 95) could be traced to a patient source.

CONCLUSIONS

Infections among HCP add further strain to the healthcare system and put patients, HCP, and communities at risk. We found no evidence for healthcare-associated transmission in the majority of HCP infections evaluated. Although we cannot rule out the possibility of cryptic healthcare-associated transmission, it appears that HCP most commonly become infected with SARS-CoV-2 via community exposure. This emphasizes the ongoing importance of mask wearing, physical distancing, robust testing programs, and rapid distribution of vaccines.

Acute SARS-CoV-2 infections harbor limited within-host diversity and transmit via tight transmission bottlenecks

The emergence of divergent SARS-CoV-2 lineages has raised concern that novel variants eliciting immune escape or the ability to displace circulating lineages could emerge within individual hosts. Though growing evidence suggests that novel variants arise during prolonged infections, most infections are acute. Understanding how efficiently variants emerge and transmit among acutely-infected hosts is therefore critical for predicting the pace of long-term SARS-CoV-2 evolution. To characterize how within-host diversity is generated and propagated, we combine extensive laboratory and bioinformatic controls with metrics of within- and between-host diversity to 133 SARS-CoV-2 genomes from acutely-infected individuals. We find that within-host diversity is low and transmission bottlenecks are narrow, with very few viruses founding most infections. Within-host variants are rarely transmitted, even among individuals within the same household, and are rarely detected along phylogenetically linked infections in the broader community. These findings suggest that most variation generated within-host is lost during transmission.

Severe Acute Respiratory Syndrome Coronavirus 2 Transmission in Intercollegiate Athletics Not Fully Mitigated With Daily Antigen Testing

Background

High-frequency, rapid-turnaround severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing continues to be proposed as a way of efficiently identifying and mitigating transmission in congregate settings. However, 2 SARS-CoV-2 outbreaks occurred among intercollegiate university athletic programs during the fall 2020 semester, despite mandatory directly observed daily antigen testing.

Methods

During the fall 2020 semester, athletes and staff in both programs were tested daily using Quidel’s Sofia SARS Antigen Fluorescent Immunoassay, with positive antigen results requiring confirmatory testing with real-time reverse-transcription polymerase chain reaction. We used genomic sequencing to investigate transmission dynamics in these 2 outbreaks.

Results

In the first outbreak, 32 confirmed cases occurred within a university athletics program after the index patient attended a meeting while infectious, despite a negative antigen test on the day of the meeting. Among isolates sequenced from that outbreak, 24 (92%) of 26 were closely related, suggesting sustained transmission following an initial introduction event. In the second outbreak, 12 confirmed cases occurred among athletes from 2 university programs that faced each other in an athletic competition, despite receipt of negative antigen test results on the day of the competition. Sequences from both teams were closely related and distinct from viruses circulating in the community for team 1, suggesting transmission during intercollegiate competition in the community for team 2.

Conclusions

These findings suggest that antigen testing alone, even when mandated and directly observed, may not be sufficient as an intervention to prevent SARS-CoV-2 outbreaks in congregate settings, and they highlight the importance of vaccination to prevent SARS-CoV-2 outbreak in congregate settings.

Genomic epidemiology of SARS-CoV-2 transmission lineages in Ecuador

Characterisation of SARS-CoV-2 genetic diversity through space and time can reveal trends in virus importation and domestic circulation, and permit the exploration of questions regarding the early transmission dynamics. Here we present a detailed description of SARS-CoV-2 genomic epidemiology in Ecuador, one of the hardest hit countries during the early stages of the COVID-19 pandemic. We generate and analyse 160 whole genome sequences sampled from all provinces of Ecuador in 2020. Molecular clock and phylgeographic analysis of these sequences in the context of global SARS-CoV-2 diversity enable us to identify and characterise individual transmission lineages within Ecuador, explore their spatiotemporal distributions, and consider their introduction and domestic circulation. Our results reveal a pattern of multiple international importations across the country, with apparent differences between key provinces. Transmission lineages were mostly introduced before the implementation of non-pharmaceutical interventions (NPIs), with differential degrees of persistence and national dissemination.

SARS-CoV-2 transmission in intercollegiate athletics not fully mitigated with daily antigen testing

Background

High frequency, rapid turnaround SARS-CoV-2 testing continues to be proposed as a way of efficiently identifying and mitigating transmission in congregate settings. However, two SARS-CoV-2 outbreaks occurred among intercollegiate university athletic programs during the fall 2020 semester despite mandatory directly observed daily antigen testing.

Methods

During the fall 2020 semester, athletes and staff in both programs were tested daily using Quidel's Sofia SARS Antigen Fluorescent Immunoassay (FIA), with positive antigen results requiring confirmatory testing with real-time reverse transcription polymerase chain reaction (RT-PCR). We used genomic sequencing to investigate transmission dynamics in these two outbreaks.

Results

In Outbreak 1, 32 confirmed cases occurred within a university athletics program after the index patient attended a meeting while infectious despite a negative antigen test on the day of the meeting. Among isolates sequenced from Outbreak 1, 24 (92%) of 26 were closely related, suggesting sustained transmission following an initial introduction event. In Outbreak 2, 12 confirmed cases occurred among athletes from two university programs that faced each other in an athletic competition despite receiving negative antigen test results on the day of the competition. Sequences from both teams were closely related and unique from strains circulating in the community, suggesting transmission during intercollegiate competition.

Conclusions

These findings suggest that antigen testing alone, even when mandated and directly observed, may not be sufficient as an intervention to prevent SARS-CoV-2 outbreaks in congregate settings, and highlights the importance of supplementing serial antigen testing with appropriate mitigation strategies to prevent SARS-CoV-2 outbreak in congregate settings.

Summary

High frequency, rapid turnaround SARS-CoV-2 testing continues to be proposed as a way of efficiently identifying and mitigating transmission in congregate settings. However, here we describe two SARS-CoV-2 outbreaks occurred among intercollegiate university athletic programs during the fall 2020 semester.

Genomic epidemiology of SARS-CoV-2 transmission lineages in Ecuador

Characterisation of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic diversity through space and time can reveal trends in virus importation and domestic circulation and permit the exploration of questions regarding the early transmission dynamics. Here, we present a detailed description of SARS-CoV-2 genomic epidemiology in Ecuador, one of the hardest hit countries during the early stages of the coronavirus-19 pandemic. We generated and analysed 160 whole genome sequences sampled from all provinces of Ecuador in 2020. Molecular clock and phylogeographic analysis of these sequences in the context of global SARS-CoV-2 diversity enable us to identify and characterise individual transmission lineages within Ecuador, explore their spatiotemporal distributions, and consider their introduction and domestic circulation. Our results reveal a pattern of multiple international importations across the country, with apparent differences between key provinces. Transmission lineages were mostly introduced before the implementation of non-pharmaceutical interventions, with differential degrees of persistence and national dissemination.