Improving surveillance of emerging RNA viruses

Effective surveillance of emerging RNA viruses is essential for public health andepidemiological research. Without identifying incident infections in an actionable time frame, the prevention of future infections becomes nearly impossible. This was exemplified at the beginning of the COVID-19 pandemic in the United States, during which time only a small fraction of infected individuals was detected through surveillance, and the virus promptly spread across the country. Similarly, parameterizing informative epidemiological models requires accurate data. In the absence of effective surveillance and/or a clear understanding of where gaps in the system exist, efforts to better understand the dynamics of circulating viruses become misguided or futile. Major challenges to improving surveillance methods are (1) the inherent interdisciplinarynature of the field and (2) limited available resources. Molecular drivers including host-pathogen interactions and pathogen evolution influence the burden of disease and the changing effectiveness of diagnostic tools used to detect new infections. For example, syndromic surveillance systems used in dengue-endemic regions disproportionately detect individuals experiencing secondary dengue infections. Statistical methods may be useful for inferring information that would otherwise be unobservable in such cases, and obtaining community buyin is essential for ensuring the success of any surveillance program. To further investigate these challenges, we characterize surveillance gaps in arbovirus surveillance systems in the Dominican Republic and propose some solutions for closing these gaps in Chapter 1. Because implementing these solutions becomes increasingly difficult in resource-limited settings, we next develop systems that utilize low-cost components. We first validate the use of saliva as a diagnostic medium for SARS-CoV-2 testing. Compared to nasopharyngeal swabs, saliva collection is less invasive and is less affected by clinical supply-chain bottlenecks, which mitigates fluctuations in the cost of consumables. In Chapter 2, we evaluate the safety and effectiveness of unobserved saliva self-collection and find that participants were able to provide saliva samples for diagnostic testing without difficulty. By increasing access to diagnostic testing, we aim to build sustainable surveillance systems and promote health equity. The expanding scale of available genomic data, particularly in response to the COVID-19 pandemic, has also helped address some surveillance gaps and provided important insights into the emergence and spread of novel RNA viruses. When a viral population is sampled at sufficient density, it is possible to answer key epidemiological questions such as when and from where the virus was introduced into a community. In Chapter 3, we use phylogenetics and genomic epidemiology to demonstrate that the initial COVID-19 outbreak in Connecticut was likely caused by domestic spread of SARS-CoV-2. This discovery highlighted surveillance gaps in the early COVID-19 response in the United States, which assumed that incident cases were associated with international travel at that time. Since then, the duration of the COVID-19 pandemic has prompted more complex questions, especially in response to the emergence of more transmissible variants. Thus, in Chapter 4, we develop a framework that combines genomic and traditional epidemiological data and compares the relative fitness of co-circulating SARS-CoV-2 variants. We specifically designed this framework for routine implementation by minimizing the requisite computational demands and run time. In this dissertation, we identify and address key challenges associated with theimprovement of surveillance systems for RNA viruses of public health significance. Specifically, we use a case study in the Dominican Republic to demonstrate the risks associated with relying on syndromic surveillance and surveillance systems closely tied to initial outbreaks of emerging viruses. We then propose practical solutions for closing surveillance gaps using low-cost and genomic methods. In sum, we argue that sustainable rather than reactionary surveillance systems promote public health, and we provide feasible methods for facilitating this transition

Sequencing SARS-CoV-2 Genomes From Saliva

Genomic sequencing is crucial to understanding the epidemiology and evolution of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Often, genomic studies rely on remnant diagnostic material, typically nasopharyngeal (NP) swabs, as input into whole-genome SARS-CoV-2 next-generation sequencing pipelines. Saliva has proven to be a safe and stable specimen for the detection of SARS-CoV-2 RNA via traditional diagnostic assays; however, saliva is not commonly used for SARS-CoV-2 sequencing. Using the ARTIC Network amplicon-generation approach with sequencing on the Oxford Nanopore MinION, we demonstrate that sequencing SARS-CoV-2 from saliva produces genomes comparable to those from NP swabs, and that RNA extraction is necessary to generate complete genomes from saliva. In this study, we show that saliva is a useful specimen type for genomic studies of SARS-CoV-2

A 39.8kb flavi-like virus uses a novel strategy for overcoming the RNA virus error threshold

It is commonly held that there is a fundamental relationship between genome size and error rate, manifest as a notional “error threshold” that sets an upper limit on genome sizes. The genome sizes of RNA viruses, which have intrinsically high mutation rates due to a lack of mechanisms for error correction, must therefore be small to avoid accumulating an excessive number of deleterious mutations that will ultimately lead to population extinction. The proposed exceptions to this evolutionary rule are RNA viruses from the order Nidovirales (such as coronaviruses) that encode an error correcting exonuclease, enabling them to reach genome lengths greater than 40kb. The recent discovery of large genome flavi-like viruses (Flaviviridae), which comprise genomes up to 27kb in length yet seemingly do not encode exonuclease domains, has led to the proposal that a proofreading mechanism is required to facilitate the expansion of RNA virus genomes above 30kb. Herein, we describe a 39.8kb flavi-like virus identified in a Haliclona sponge metatranscriptome that does not encode an exonuclease. Structural analysis revealed that this virus may have instead captured bacterial domains associated with nucleic acid metabolism that have not been previously found in RNA viruses. Phylogenetic analysis placed this virus as a divergent pesti-like lineage, such that we have provisionally termed it Maximus pesti-like virus. This virus represents the first instance of a flavi-like virus achieving a genome size comparable to that of the Nidovirales and demonstrates that RNA viruses have evolved multiple solutions to overcome the error threshold

Nonsystematic Reporting Biases of the SARS-CoV-2 Variant Mu Could Impact Our Understanding of the Epidemiological Dynamics of Emerging Variants

Developing a timely and effective response to emerging SARS-CoV-2 variants of concern (VOCs) is of paramount public health importance. Global health surveillance does not rely on genomic data alone to identify concerning variants when they emerge. Instead, methods that utilize genomic data to estimate the epidemiological dynamics of emerging lineages have the potential to serve as an early warning system. However, these methods assume that genomic data are uniformly reported across circulating lineages. In this study, we analyze differences in reporting delays among SARS-CoV-2 VOCs as a plausible explanation for the timing of the global response to the former VOC Mu. Mu likely emerged in South America in mid-2020, where its circulation was largely confined. In this study, we demonstrate that Mu was designated as a VOC ∼1 year after it emerged and find that the reporting of genomic data for Mu differed significantly than that of other VOCs within countries, states, and individual laboratories. Our findings suggest that nonsystematic biases in the reporting of genomic data may have delayed the global response to Mu. Until they are resolved, the surveillance gaps that affected the global response to Mu could impede the rapid and accurate assessment of future emerging variants

Lineage Abundance Estimation for SARS-CoV-2 in Wastewater Using Transcriptome Quantification Techniques

Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in mutant prevalence in situations where clinical sequencing is unavailable

Accelerated SARS-CoV-2 Intrahost Evolution Leading to Distinct Genotypes During Chronic Infection

The chronic infection hypothesis for novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant emergence is increasingly gaining credence following the appearance of Omicron. Here, we investigate intrahost evolution and genetic diversity of lineage B.1.517 during a SARS-CoV-2 chronic infection lasting for 471 days (and still ongoing) with consistently recovered infectious virus and high viral genome copies. During the infection, we find an accelerated virus evolutionary rate translating to 35 nucleotide substitutions per year, approximately 2-fold higher than the global SARS-CoV-2 evolutionary rate. This intrahost evolution results in the emergence and persistence of at least three genetically distinct genotypes, suggesting the establishment of spatially structured viral populations continually reseeding different genotypes into the nasopharynx. Finally, we track the temporal dynamics of genetic diversity to identify advantageous mutations and highlight hallmark changes for chronic infection. Our findings demonstrate that untreated chronic infections accelerate SARS-CoV-2 evolution, providing an opportunity for the emergence of genetically divergent variants

Multiplex qPCR Discriminates Variants of Concern to Enhance Global Surveillance of SARS-CoV-2

With the emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants that may increase transmissibility and/or cause escape from immune responses, there is an urgent need for the targeted surveillance of circulating lineages. It was found that the B.1.1.7 (also 501Y.V1) variant, first detected in the United Kingdom, could be serendipitously detected by the Thermo Fisher TaqPath COVID-19 PCR assay because a key deletion in these viruses, spike Δ69-70, would cause a spike gene target failure (SGTF) result. However, a SGTF result is not definitive for B.1.1.7, and this assay cannot detect other variants of concern (VOC) that lack spike Δ69-70, such as B.1.351 (also 501Y.V2), detected in South Africa, and P.1 (also 501Y.V3), recently detected in Brazil. We identified a deletion in the ORF1a gene (ORF1a Δ3675-3677) in all 3 variants, which has not yet been widely detected in other SARS-CoV-2 lineages. Using ORF1a Δ3675-3677 as the primary target and spike Δ69-70 to differentiate, we designed and validated an open-source PCR assay to detect SARS-CoV-2 VOC. Our assay can be rapidly deployed in laboratories around the world to enhance surveillance for the local emergence and spread of B.1.1.7, B.1.351, and P.1

Lying in Wait: The Resurgence of Dengue Virus After the Zika Epidemic in Brazil

After the Zika virus (ZIKV) epidemic in the Americas in 2016, both Zika and dengue incidence declined to record lows in many countries in 2017-2018, but in 2019 dengue resurged in Brazil, causing ~2.1 million cases. In this study we use epidemiological, climatological and genomic data to investigate dengue dynamics in recent years in Brazil. First, we estimate dengue virus force of infection (FOI) and model mosquito-borne transmission suitability since the early 2000s. Our estimates reveal that DENV transmission was low in 2017-2018, despite conditions being suitable for viral spread. Our study also shows a marked decline in dengue susceptibility between 2002 and 2019, which could explain the synchronous decline of dengue in the country, partially as a result of protective immunity from prior ZIKV and/or DENV infections. Furthermore, we performed phylogeographic analyses using 69 newly sequenced genomes of dengue virus serotype 1 and 2 from Brazil, and found that the outbreaks in 2018-2019 were caused by local DENV lineages that persisted for 5-10 years, circulating cryptically before and after the Zika epidemic. We hypothesize that DENV lineages may circulate at low transmission levels for many years, until local conditions are suitable for higher transmission, when they cause major outbreaks

Comparative Transmissibility of SARS-CoV-2 Variants Delta and Alpha in New England, USA

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant quickly rose to dominance in mid-2021, displacing other variants, including Alpha. Studies using data from the United Kingdom and India estimated that Delta was 40-80% more transmissible than Alpha, allowing Delta to become the globally dominant variant. However, it was unclear if the ostensible difference in relative transmissibility was due mostly to innate properties of Delta\u27s infectiousness or differences in the study populations. To investigate, we formed a partnership with SARS-CoV-2 genomic surveillance programs from all six New England US states. By comparing logistic growth rates, we found that Delta emerged 37-163% faster than Alpha in early 2021 (37% Massachusetts, 75% New Hampshire, 95% Maine, 98% Rhode Island, 151% Connecticut, and 163% Vermont). We next computed variant-specific effective reproductive numbers and estimated that Delta was 58-120% more transmissible than Alpha across New England (58% New Hampshire, 68% Massachusetts, 76% Connecticut, 85% Rhode Island, 98% Maine, and 120% Vermont). Finally, using RT-PCR data, we estimated that Delta infections generate on average ∼6 times more viral RNA copies per mL than Alpha infections. Overall, our evidence indicates that Delta\u27s enhanced transmissibility could be attributed to its innate ability to increase infectiousness, but its epidemiological dynamics may vary depending on the underlying immunity and behavior of distinct populations

Combining genomic and epidemiological data to compare the transmissibility of SARS-CoV-2 variants Alpha and Iota.

SARS-CoV-2 variants shaped the second year of the COVID-19 pandemic and the discourse around effective control measures. Evaluating the threat posed by a new variant is essential for adapting response efforts when community transmission is detected. In this study, we compare the dynamics of two variants, Alpha and Iota, by integrating genomic surveillance data to estimate the effective reproduction number (Rt) of the variants. We use Connecticut, United States, in which Alpha and Iota co-circulated in 2021. We find that the Rt of these variants were up to 50% larger than that of other variants. We then use phylogeography to show that while both variants were introduced into Connecticut at comparable frequencies, clades that resulted from introductions of Alpha were larger than those resulting from Iota introductions. By monitoring the dynamics of individual variants throughout our study period, we demonstrate the importance of routine surveillance in the response to COVID-19