Population immunity predicts evolutionary trajectories of SARS-CoV-2.

The large-scale evolution of the SARS-CoV-2 virus has been marked by rapid turnover of genetic clades. New variants show intrinsic changes, notably increased transmissibility, and antigenic changes that reduce cross-immunity induced by previous infections or vaccinations. How this functional variation shapes global evolution has remained unclear. Here, we establish a predictive fitness model for SARS-CoV-2 that integrates antigenic and intrinsic selection. The model is informed by tracking of time-resolved sequence data, epidemiological records, and cross-neutralization data of viral variants. Our inference shows that immune pressure, including contributions of vaccinations and previous infections, has become the dominant force driving the recent evolution of SARS-CoV-2. The fitness model can serve continued surveillance in two ways. First, it successfully predicts the short-term evolution of circulating strains and flags emerging variants likely to displace the previously predominant variant. Second, it predicts likely antigenic profiles of successful escape variants prior to their emergence.

Coming to America: Genomic surveillance and how B.1.1.7 arrived in the US.

In this issue of Cell, Washington et al. and Alpert et al. demonstrate the value of genomic surveillance when studying the introduction of the B.1.1.7 variant to the US and illustrate the challenge that results from the lack of good sampling strategies.

Public health actions to control new SARS-CoV-2 variants.

Recent reports suggest that some SARS-CoV-2 genetic variants, such as B.1.1.7, might be more transmissible and are quickly spreading around the world. As the emergence of more transmissible variants could exacerbate the pandemic, we provide public health guidance for increased surveillance and measures to reduce community transmission.

Genomic Epidemiology of SARS-CoV-2 in Guangdong Province, China.

Coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2 infection and was first reported in central China in December 2019. Extensive molecular surveillance in Guangdong, China's most populous province, during early 2020 resulted in 1,388 reported RNA-positive cases from 1.6 million tests. In order to understand the molecular epidemiology and genetic diversity of SARS-CoV-2 in China, we generated 53 genomes from infected individuals in Guangdong using a combination of metagenomic sequencing and tiling amplicon approaches. Combined epidemiological and phylogenetic analyses indicate multiple independent introductions to Guangdong, although phylogenetic clustering is uncertain because of low virus genetic variation early in the pandemic. Our results illustrate how the timing, size, and duration of putative local transmission chains were constrained by national travel restrictions and by the province's large-scale intensive surveillance and intervention measures. Despite these successes, COVID-19 surveillance in Guangdong is still required, because the number of cases imported from other countries has increased.

Coast-to-Coast Spread of SARS-CoV-2 during the Early Epidemic in the United States.

The novel coronavirus SARS-CoV-2 was first detected in the Pacific Northwest region of the United States in January 2020, with subsequent COVID-19 outbreaks detected in all 50 states by early March. To uncover the sources of SARS-CoV-2 introductions and patterns of spread within the United States, we sequenced nine viral genomes from early reported COVID-19 patients in Connecticut. Our phylogenetic analysis places the majority of these genomes with viruses sequenced from Washington state. By coupling our genomic data with domestic and international travel patterns, we show that early SARS-CoV-2 transmission in Connecticut was likely driven by domestic introductions. Moreover, the risk of domestic importation to Connecticut exceeded that of international importation by mid-March regardless of our estimated effects of federal travel restrictions. This study provides evidence of widespread sustained transmission of SARS-CoV-2 within the United States and highlights the critical need for local surveillance.

Tracking Changes in SARS-CoV-2 Spike: Evidence that D614G Increases Infectivity of the COVID-19 Virus.

A SARS-CoV-2 variant carrying the Spike protein amino acid change D614G has become the most prevalent form in the global pandemic. Dynamic tracking of variant frequencies revealed a recurrent pattern of G614 increase at multiple geographic levels: national, regional, and municipal. The shift occurred even in local epidemics where the original D614 form was well established prior to introduction of the G614 variant. The consistency of this pattern was highly statistically significant, suggesting that the G614 variant may have a fitness advantage. We found that the G614 variant grows to a higher titer as pseudotyped virions. In infected individuals, G614 is associated with lower RT-PCR cycle thresholds, suggestive of higher upper respiratory tract viral loads, but not with increased disease severity. These findings illuminate changes important for a mechanistic understanding of the virus and support continuing surveillance of Spike mutations to aid with development of immunological interventions.

Roots, Not Parachutes: Research Collaborations Combat Outbreaks.

Recent infectious disease epidemics illustrate how health systems failures anywhere can create disease vulnerabilities everywhere. We must therefore prioritize investments in health care infrastructure in outbreak-prone regions of the world. We describe how "rooted" research collaborations can establish capacity for pathogen surveillance and facilitate rapid outbreak responses.

Genomic reconstruction of the SARS-CoV-2 epidemic in England.

The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus leads to new variants that warrant timely epidemiological characterization. Here we use the dense genomic surveillance data generated by the COVID-19 Genomics UK Consortium to reconstruct the dynamics of 71 different lineages in each of 315 English local authorities between September 2020 and June 2021. This analysis reveals a series of subepidemics that peaked in early autumn 2020, followed by a jump in transmissibility of the B.1.1.7/Alpha lineage. The Alpha variant grew when other lineages declined during the second national lockdown and regionally tiered restrictions between November and December 2020. A third more stringent national lockdown suppressed the Alpha variant and eliminated nearly all other lineages in early 2021. Yet a series of variants (most of which contained the spike E484K mutation) defied these trends and persisted at moderately increasing proportions. However, by accounting for sustained introductions, we found that the transmissibility of these variants is unlikely to have exceeded the transmissibility of the Alpha variant. Finally, B.1.617.2/Delta was repeatedly introduced in England and grew rapidly in early summer 2021, constituting approximately 98% of sampled SARS-CoV-2 genomes on 26 June 2021.

Repeated cross-sectional sero-monitoring of SARS-CoV-2 in New York City.

In late 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first detected in China and has since caused a pandemic of coronavirus disease 2019 (COVID-19). The first case of COVID-19 in New York City was officially confirmed on 1 March 2020 followed by a severe local epidemic1. Here, to understand seroprevalence dynamics, we conduct a retrospective, repeated cross-sectional analysis of anti-SARS-CoV-2 spike antibodies in weekly intervals from the beginning of February to July 2020 using more than 10,000 plasma samples from patients at Mount Sinai Hospital in New York City. We describe the dynamics of seroprevalence in an 'urgent care' group, which is enriched in cases of COVID-19 during the epidemic, and a 'routine care' group, which more closely represents the general population. Seroprevalence increased at different rates in both groups; seropositive samples were found as early as mid-February, and levelled out at slightly above 20% in both groups after the epidemic wave subsided by the end of May. From May to July, seroprevalence remained stable, suggesting lasting antibody levels in the population. Our data suggest that SARS-CoV-2 was introduced in New York City earlier than previously documented and describe the dynamics of seroconversion over the full course of the first wave of the pandemic in a major metropolitan area.

Reconstruction of the full transmission dynamics of COVID-19 in Wuhan.

As countries in the world review interventions for containing the pandemic of coronavirus disease 2019 (COVID-19), important lessons can be drawn from the study of the full transmission dynamics of its causative agent-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)- in Wuhan (China), where vigorous non-pharmaceutical interventions have suppressed the local outbreak of this disease1. Here we use a modelling approach to reconstruct the full-spectrum dynamics of COVID-19 in Wuhan between 1 January and 8 March 2020 across 5 periods defined by events and interventions, on the basis of 32,583 laboratory-confirmed cases1. Accounting for presymptomatic infectiousness2, time-varying ascertainment rates, transmission rates and population movements3, we identify two key features of the outbreak: high covertness and high transmissibility. We estimate 87% (lower bound, 53%) of the infections before 8 March 2020 were unascertained (potentially including asymptomatic and mildly symptomatic individuals); and a basic reproduction number (R0) of 3.54 (95% credible interval 3.40-3.67) in the early outbreak, much higher than that of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS)4,5. We observe that multipronged interventions had considerable positive effects on controlling the outbreak, decreasing the reproduction number to 0.28 (95% credible interval 0.23-0.33) and-by projection-reducing the total infections in Wuhan by 96.0% as of 8 March 2020. We also explore the probability of resurgence following the lifting of all interventions after 14 consecutive days of no ascertained infections; we estimate this probability at 0.32 and 0.06 on the basis of models with 87% and 53% unascertained cases, respectively-highlighting the risk posed by substantial covert infections when changing control measures. These results have important implications when considering strategies of continuing surveillance and interventions to eventually contain outbreaks of COVID-19.

"Smart markets": harnessing the potential of new technologies for endemic and emerging infectious disease surveillance in traditional food markets.

Emerging and endemic zoonotic diseases continue to threaten human and animal health, our social fabric, and the global economy. Zoonoses frequently emerge from congregate interfaces where multiple animal species and humans coexist, including farms and markets. Traditional food markets are widespread across the globe and create an interface where domestic and wild animals interact among themselves and with humans, increasing the risk of pathogen spillover. Despite decades of evidence linking markets to disease outbreaks across the world, there remains a striking lack of pathogen surveillance programs that can relay timely, cost-effective, and actionable information to decision-makers to protect human and animal health. However, the strategic incorporation of environmental surveillance systems in markets coupled with novel pathogen detection strategies can create an early warning system capable of alerting us to the risk of outbreaks before they happen. Here, we explore the concept of "smart" markets that utilize continuous surveillance systems to monitor the emergence of zoonotic pathogens with spillover potential.IMPORTANCEFast detection and rapid intervention are crucial to mitigate risks of pathogen emergence, spillover and spread-every second counts. However, comprehensive, active, longitudinal surveillance systems at high-risk interfaces that provide real-time data for action remain lacking. This paper proposes "smart market" systems harnessing cutting-edge tools and a range of sampling techniques, including wastewater and air collection, multiplex assays, and metagenomic sequencing. Coupled with robust response pathways, these systems could better enable Early Warning and bolster prevention efforts.

Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide.

Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor its evolution, inform the public, and assist governments in decision-making. Here, we present a globally applicable method, integrated in a daily updated dashboard that provides an estimate of the trend in the evolution of the number of cases and deaths from reported data of more than 200 countries and territories, as well as 7-d forecasts. One of the significant difficulties in managing a quickly propagating epidemic is that the details of the dynamic needed to forecast its evolution are obscured by the delays in the identification of cases and deaths and by irregular reporting. Our forecasting methodology substantially relies on estimating the underlying trend in the observed time series using robust seasonal trend decomposition techniques. This allows us to obtain forecasts with simple yet effective extrapolation methods in linear or log scale. We present the results of an assessment of our forecasting methodology and discuss its application to the production of global and regional risk maps.

Wildlife susceptibility to infectious diseases at global scales.

Disease transmission prediction across wildlife is crucial for risk assessment of emerging infectious diseases. Susceptibility of host species to pathogens is influenced by the geographic, environmental, and phylogenetic context of the specific system under study. We used machine learning to analyze how such variables influence pathogen incidence for multihost pathogen assemblages, including one of direct transmission (coronaviruses and bats) and two vector-borne systems (West Nile Virus [WNV] and birds, and malaria and birds). Here we show that this methodology is able to provide reliable global spatial susceptibility predictions for the studied host-pathogen systems, even when using a small amount of incidence information (i.e., [Formula: see text] of information in a database). We found that avian malaria was mostly affected by environmental factors and by an interaction between phylogeny and geography, and WNV susceptibility was mostly influenced by phylogeny and by the interaction between geographic and environmental distances, whereas coronavirus susceptibility was mostly affected by geography. This approach will help to direct surveillance and field efforts providing cost-effective decisions on where to invest limited resources.

Large Clusters of Invasive emm49 Group A Streptococcus Identified Within Arizona Health Care Facilities Through Statewide Genomic Surveillance System, 2019-2021.

A statewide genomic surveillance system for invasive Group A Streptococcus was implemented in Arizona in June 2019, resulting in 1046 isolates being submitted for genomic analysis to characterize emm types and identify transmission clusters. Eleven of the 32 identified distinct emm types comprised >80% of samples, with 29.7% of all isolates being typed as emm49 (and its genetic derivative emm151). Phylogenetic analysis initially identified an emm49 genomic cluster of 4 isolates that rapidly expanded over subsequent months (June 2019 to February 2020). Public health investigations identified epidemiologic links with 3 different long-term care facilities, resulting in specific interventions. Unbiased genomic surveillance allowed for identification and response to clusters that would have otherwise remained undetected.

Development and implementation of a core genome multilocus sequence typing scheme for Yersinia enterocolitica: a tool for surveillance and outbreak detection.

Yersinia enterocolitica (Y. enterocolitica) is the most frequent etiological agent of yersiniosis and has been responsible for several national outbreaks in Norway and elsewhere. A standardized high-resolution method, such as core genome Multilocus Sequence Typing (cgMLST), is needed for pathogen traceability at the national and international levels. In this study, we developed and implemented a cgMLST scheme for Y. enterocolitica. We designed a cgMLST scheme in SeqSphere + using high-quality genomes from different Y. enterocolitica biotype sublineages. The scheme was validated if more than 95% of targets were found across all tested Y. enterocolitica: 563 Norwegian genomes collected between 2012 and 2022 and 327 genomes from public data sets. We applied the scheme to known outbreaks to establish a threshold for identifying major complex types (CTs) based on the number of allelic differences. The final cgMLST scheme included 2,582 genes with a median of 97.9% (interquartile range 97.6%-98.8%) targets found across all tested genomes. Analysis of outbreaks identified all outbreak strains using single linkage clustering at four allelic differences. This threshold identified 311 unique CTs in Norway, of which CT18, CT12, and CT5 were identified as the most frequently associated with outbreaks. The cgMLST scheme showed a very good performance in typing Y. enterocolitica using diverse data sources and was able to identify outbreak clusters. We recommend the implementation of this scheme nationally and internationally to facilitate Y. enterocolitica surveillance and improve outbreak response in national and cross-border outbreaks.

A simple and sensitive test for Candida auris colonization, surveillance, and infection control suitable for near patient use.

Candida auris is a multidrug-resistant fungal pathogen with a propensity to colonize humans and persist on environmental surfaces. C. auris invasive fungal disease is being increasingly identified in acute and long-term care settings. We have developed a prototype cartridge-based C. auris surveillance assay (CaurisSurV cartridge; "research use only") that includes integrated sample processing and nucleic acid amplification to detect C. auris from surveillance skin swabs in the GeneXpert instrument and is designed for point-of-care use. The assay limit of detection (LoD) in the skin swab matrix was 10.5 and 14.8 CFU/mL for non-aggregative (AR0388) and aggregative (AR0382) strains of C. auris, respectively. All five known clades of C. auris were detected at 2-3-5× (31.5-52.5 CFU/mL) the LoD. The assay was validated using a total of 85 clinical swab samples banked at two different institutions (University of California Los Angeles, CA and Wadsworth Center, NY). Compared to culture, sensitivity was 96.8% (30/31) and 100% (10/10) in the UCLA and Wadsworth cohorts, respectively, providing a combined sensitivity of 97.5% (40/41), and compared to PCR, the combined sensitivity was 92% (46/50). Specificity was 100% with both clinical (C. auris negative matrix, N = 31) and analytical (non-C. auris strains, N = 32) samples. An additional blinded study with N = 60 samples from Wadsworth Center, NY yielded 97% (29/30) sensitivity and 100% (28/28) specificity. We have developed a completely integrated, sensitive, specific, and 58-min prototype test, which can be used for routine surveillance of C. auris and might help prevent colonization and outbreaks in acute and chronic healthcare settings. IMPORTANCE: This study has the potential to offer a better solution to healthcare providers at hospitals and long-term care facilities in their ongoing efforts for effective and timely control of Candida auris infection and hence quicker response for any potential future outbreaks.

Getting rapid diagnostic test data into the appropriate hands by leveraging pharmacy staff and a clinical surveillance platform: a case study from a US community hospital.

OBJECTIVES: To outline the procedural implementation and optimization of rapid diagnostic test (RDT) results for bloodstream infections (BSIs) and to evaluate the combination of RDTs with real-time antimicrobial stewardship team (AST) support plus clinical surveillance platform (CSP) software on time to appropriate therapy in BSIs at a single health system. METHODS: Blood culture reporting and communication were reported for four time periods: (i) a pre-BCID [BioFire® FilmArray® Blood Culture Identification (BCID) Panel] implementation period that consisted of literature review and blood culture notification procedure revision; (ii) a BCID implementation period that consisted of BCID implementation, real-time results notification via CSP, and creation of a treatment algorithm; (iii) a post-BCID implementation period; and (iv) a BCID2 implementation period. Time to appropriate therapy metrics was reported for the BCID2 time period. RESULTS: The mean time from BCID2 result to administration of effective antibiotics was 1.2 h (range 0-7.9 h) and time to optimal therapy was 7.6 h (range 0-113.8 h) during the BCID2 Panel implementation period. When comparing time to optimal antibiotic administration among patients growing ceftriaxone-resistant Enterobacterales, the BCID2 Panel group (mean 2.8 h) was significantly faster than the post-BCID Panel group (17.7 h; P = 0.0041). CONCLUSIONS: Challenges exist in communicating results to the appropriate personnel on the healthcare team who have the knowledge to act on these data and prescribe targeted therapy against the pathogen(s) identified. In this report, we outline the procedures for telephonic communication and CSP support that were implemented at our health system to distribute RDT data to individuals capable of assessing results, enabling timely optimization of antimicrobial therapy.

Detection of drug-resistant malaria in resource-limited settings: efficient and high-throughput surveillance of artemisinin and partner drug resistance.

OBJECTIVES: Artemisinin-resistant Plasmodium falciparum malaria is currently spreading globally, including in Africa. Artemisinin resistance also leads to resistance to partner drugs used in artemisinin-based combination therapies. Sequencing of kelch13, which is associated with artemisinin resistance, culture-based partner drug susceptibility tests, and ELISA-based growth measurement are conventionally used to monitor resistance; however, their application is challenging in resource-limited settings. METHODS: An experimental package for field studies with minimum human/material requirements was developed. RESULTS: First, qPCR-based SNP assay was applied in artemisinin resistance screening, which can detect mutations within 1 h and facilitate sample selection for subsequent processes. It had 100% sensitivity and specificity compared with DNA sequencing in the detection of the two common artemisinin resistance mutations in Uganda, C469Y and A675V. Moreover, in the partner drug susceptibility test, the cultured samples were dry-preserved on a 96-well filter paper plate and shipped to the central laboratory. Parasite growth was measured by ELISA using redissolved samples. It well reproduced the results of direct ELISA, reducing significant workload in the field (Pearson correlation coefficient: 0.984; 95% CI: 0.975-0.990). CONCLUSIONS: Large-scale and sustainable monitoring is required urgently to track rapidly spreading drug-resistant malaria. In malaria-endemic areas, where research resources are often limited, simplicity and feasibility of the procedure is especially important. Our approach combines a qPCR-based rapid test, which is also applicable to point-of-care diagnosis of artemisinin resistance and centralized analysis of ex vivo culture. The approach could improve efficiency of field experiments and accelerate global drug resistance surveillance.

Global epidemiology and antimicrobial resistance of Enterobacterales harbouring genes encoding OXA-48-like carbapenemases: insights from the results of the Antimicrobial Testing Leadership and Surveillance (ATLAS) programme 2018-2021.

OBJECTIVES: The recent emergence of carbapenem-resistant Enterobacterales poses a major and escalating threat to global public health. This study aimed to analyse the global distribution and antimicrobial resistance of Enterobacterales harbouring variant OXA-48-like carbapenemase-related genes. METHODS: Enterobacterales isolates were collected from the Antimicrobial Testing Leadership and Surveillance (ATLAS) programme during 2018-2021. Comprehensive antimicrobial susceptibility testing and ß-lactamase gene detection were also conducted, along with statistical analysis of the collected data. RESULTS: Among the 72 244 isolates, 1934 Enterobacterales isolates were identified to harbour blaOXA-48-like genes, predominantly Klebsiella spp. (86.9%). High rates of multidrug resistance were observed, with only ceftazidime/avibactam and tigecycline showing favourable susceptibility. A discrepancy between the genotype and phenotype of carbapenem resistance was evident: 16.8% (233 out of 1384) of the Enterobacterales isolates with blaOXA-48-like genes exhibited susceptibility to meropenem. Specifically, 37.4% (64/95) of Escherichia coli strains with blaOXA-48-like genes displayed meropenem susceptibility, while the corresponding percentages for Klebsiella pneumoniae and Enterobacter cloacae complex were 25.2% (160/1184) and 0% (0/36), respectively (P < 0.05). Geographical analysis revealed that the highest prevalence of blaOXA-48-like genes occurred in Asia, the Middle East and Eastern Europe. The proportion of K. pneumoniae isolates harbouring blaOXA-232 increased from 23.9% in 2018 to 56.0% in 2021. By contrast, the proportion of blaOXA-48 decreased among K. pneumoniae isolates during 2018-2021. CONCLUSIONS: This study underscores the widespread and increasing prevalence of blaOXA-48-like genes in Enterobacterales and emphasizes the need for enhanced surveillance, improved diagnostic methods and tailored antibiotic stewardship to combat the spread of these resistant pathogens.

Linking epidemiological and genomic data in cases of enteric fever in England to inform clinical management and public health action.

OBJECTIVES: To explore the feasibility of linking data from enhanced surveillance patient questionnaires from each enteric fever case in England with genome sequencing data, including antimicrobial resistance (AMR) profiles, from the corresponding isolate of typhoidal salmonellae. METHODS: After linking data we interrogated the merged dataset and assessed the utility of passive surveillance data to match and monitor antimicrobial treatment regimens in enteric fever patients with the AMR profiles of the infectious agent. RESULTS: A high proportion of cases were given antibiotics (n = 1230/1415; 86.9%); half of the cases stated the class of antibiotic they were given (n = 630/1239) and half were prescribed cephalosporins (n = 316/630). Reported treatment with a combination of antibiotics increased with symptom severity. Nearly half of isolates (n = 644/1415; 45.5%) had mutations conferring resistance to ciprofloxacin. Based on genome-derived AMR profiles, typhoidal salmonellae isolates inferred to be susceptible to the recommended first-line antimicrobials were twice as likely to be isolated from individuals residing in the least deprived areas compared with the most deprived (n = 26/169; 15.4% versus n = 32/442; 7.2%). CONCLUSIONS: Due to the high proportion of missing data obtained from patient interviews, we recommend a more transparent and systematic approach to recording the antibiotic prescription details by healthcare professionals in primary and secondary care. A more robust approach to data capture at this point in the care pathway would enable us to audit inconsistencies in the prescribing algorithms across England and ensure equitable treatment across all sections of society. Integrating prescribing data with the genome-derived AMR profiles of the causative agent at the individual patient level provides an opportunity to monitor the impact of treatment on clinical outcomes, and to promote best practice in real time.