Surveillance for variants of SARS-CoV-2 to inform risk assessments.

Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, numerous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have emerged, some leading to large increases in infections, hospitalizations and deaths globally. The virus's impact on public health depends on many factors, including the emergence of new viral variants and their global spread. Consequently, the early detection and surveillance of variants and characterization of their clinical effects are vital for assessing their health risk. The unprecedented capacity for viral genomic sequencing and data sharing built globally during the pandemic has enabled new variants to be rapidly detected and assessed. This article describes the main variants circulating globally between January 2020 and June 2023, the genetic features driving variant evolution, and the epidemiological impact of these variants across countries and regions. Second, we report how integrating genetic variant surveillance with epidemiological data and event-based surveillance, through a network of World Health Organization partners, supported risk assessment and helped provide guidance on pandemic responses. In addition, given the evolutionary characteristics of circulating variants and the immune status of populations, we propose future directions for the sustainable genomic surveillance of SARS-CoV-2 variants, both nationally and internationally: (i) optimizing variant surveillance by including environmental monitoring; (ii) coordinating laboratory assessment of variant evolution and phenotype; (iii) linking data on circulating variants with clinical data; and (iv) expanding genomic surveillance to additional pathogens. Experience during the COVID-19 pandemic has shown that genomic surveillance of pathogens can provide essential, timely and evidence-based information for public health decision-making.

Depuis le début de la pandémie de coronavirus survenue en 2019 (COVID-19), de nombreux variants du coronavirus 2 du syndrome respiratoire aigu sévère (SARS-CoV-2) sont apparus, certains entraînant une forte augmentation du nombre d'infections, d'hospitalisations et de décès dans le monde. L'impact du virus sur la santé publique dépend de nombreux facteurs, notamment l'émergence de nouveaux variants viraux et leur propagation à l'échelle mondiale. Par conséquent, la détection précoce et la surveillance des variants ainsi que la caractérisation de leurs effets cliniques sont essentielles pour évaluer leur risque pour la santé. La capacité sans précédent de séquençage du génome viral et de partage des données, capacité mise en place à l'échelle mondiale pendant la pandémie, a permis de détecter et d'évaluer rapidement de nouveaux variants. Le présent article décrit les principaux variants circulant dans le monde entre janvier 2020 et juin 2023, les caractéristiques génétiques à l'origine de leur évolution et leur impact épidémiologique dans les différents pays et régions. Ensuite, nous expliquerons comment l'intégration de la surveillance des variants génétiques aux données épidémiologiques et à la surveillance fondée sur les événements, par l'intermédiaire d'un réseau de partenaires de l'Organisation mondiale de la santé, a permis de faciliter l'évaluation des risques et de fournir des orientations sur les mesures à prendre en période de pandémie. En outre, compte tenu des caractéristiques évolutives des variants en circulation et de l'état immunitaire des populations, nous proposons des orientations futures pour une surveillance génomique durable des variants du SARS-CoV-2, au niveau tant national qu'international: (i) optimiser la surveillance des variants en incluant le suivi environnemental; (ii) coordonner l'évaluation en laboratoire de l'évolution des variants et du phénotype; (iii) établir un lien entre les données sur les variants en circulation et les données cliniques; et (iv) étendre la surveillance génomique à d'autres agents pathogènes. L'expérience de la pandémie de COVID-19 a mis en évidence que la surveillance génomique des agents pathogènes peut fournir en temps utile des informations essentielles fondées sur des preuves en vue de la prise de décisions en matière de santé publique.

Desde el inicio de la pandemia de la enfermedad por coronavirus de 2019 (COVID-19), han aparecido numerosas variantes del coronavirus de tipo 2 causante del síndrome respiratorio agudo severo (SRAS-CoV-2), algunas de las que han provocado un gran aumento de las infecciones, hospitalizaciones y muertes en todo el mundo. El impacto del virus en la salud pública depende de muchos factores, entre ellos la aparición de nuevas variantes víricas y su propagación mundial. En consecuencia, la detección y vigilancia tempranas de las variantes y la caracterización de sus efectos clínicos son vitales para evaluar su riesgo sanitario. La capacidad sin precedentes de secuenciación genómica viral y de intercambio de datos creada a nivel mundial durante la pandemia ha permitido detectar y evaluar rápidamente variantes nuevas. En este artículo se describen las principales variantes que circulan a nivel mundial entre enero de 2020 y junio de 2023, la característica genética que impulsa la evolución de las variantes y el impacto epidemiológico de estas variantes en los diferentes países y regiones. En segundo lugar, se informa de cómo la integración de la vigilancia de variantes genéticas con los datos epidemiológicos y la vigilancia basada en eventos, a través de una red de asociados de la Organización Mundial de la Salud, apoyó la evaluación de riesgos y ayudó a proporcionar orientación sobre las respuestas a la pandemia. Además, dadas las características evolutivas de las variantes circulantes y el estado inmunitario de las poblaciones, se proponen orientaciones futuras para la vigilancia genómica sostenible de las variantes del SRAS-CoV-2, tanto a nivel nacional como internacional: (i) optimizar la vigilancia de las variantes mediante la inclusión de la monitorización ambiental; (ii) coordinar la evaluación de laboratorio de la evolución y el fenotipo de las variantes; (iii) vincular los datos sobre las variantes circulantes con los datos clínicos; y (iv) ampliar la vigilancia genómica a patógenos adicionales. La experiencia durante la pandemia de la COVID-19 ha demostrado que la vigilancia genómica de patógenos puede proporcionar información esencial, oportuna y basada en evidencias para la toma de decisiones en materia de salud pública.

WHO Global Situational Alert System: a mixed methods multistage approach to identify country-level COVID-19 alerts.

BACKGROUND: Globally, since 1 January 2020 and as of 24 January 2023, there have been over 664 million cases of COVID-19 and over 6.7 million deaths reported to WHO. WHO developed an evidence-based alert system, assessing public health risk on a weekly basis in 237 countries, territories and areas from May 2021 to June 2022. This aimed to facilitate the early identification of situations where healthcare capacity may become overstretched. METHODS: The process involved a three-stage mixed methods approach. In the first stage, future deaths were predicted from the time series of reported cases and deaths to produce an initial alert level. In the second stage, this alert level was adjusted by incorporating a range of contextual indicators and accounting for the quality of information available using a Bayes classifier. In the third stage, countries with an alert level of 'High' or above were added to an operational watchlist and assistance was deployed as needed. RESULTS: Since June 2021, the system has supported the release of more than US$27 million from WHO emergency funding, over 450 000 rapid antigen diagnostic testing kits and over 6000 oxygen concentrators. Retrospective evaluation indicated that the first two stages were needed to maximise sensitivity, where 44% (IQR 29%-67%) of weekly watchlist alerts would not have been identified using only reported cases and deaths. The alerts were timely and valid in most cases; however, this could only be assessed on a non-representative sample of countries with hospitalisation data available. CONCLUSIONS: The system provided a standardised approach to monitor the pandemic at the country level by incorporating all available data on epidemiological analytics and contextual assessments. While this system was developed for COVID-19, a similar system could be used for future outbreaks and emergencies, with necessary adjustments to parameters and indicators.

COVID-19 Mortality and Progress Toward Vaccinating Older Adults - World Health Organization, Worldwide, 2020-2022.

After the emergence of SARS-CoV-2 in late 2019, transmission expanded globally, and on January 30, 2020, COVID-19 was declared a public health emergency of international concern.* Analysis of the early Wuhan, China outbreak (1), subsequently confirmed by multiple other studies (2,3), found that 80% of deaths occurred among persons aged ≥60 years. In anticipation of the time needed for the global vaccine supply to meet all needs, the World Health Organization (WHO) published the Strategic Advisory Group of Experts on Immunization (SAGE) Values Framework and a roadmap for prioritizing use of COVID-19 vaccines in late 2020 (4,5), followed by a strategy brief to outline urgent actions in October 2021.† WHO described the general principles, objectives, and priorities needed to support country planning of vaccine rollout to minimize severe disease and death. A July 2022 update to the strategy brief§ prioritized vaccination of populations at increased risk, including older adults,¶ with the goal of 100% coverage with a complete COVID-19 vaccination series** for at-risk populations. Using available public data on COVID-19 mortality (reported deaths and model estimates) for 2020 and 2021 and the most recent reported COVID-19 vaccination coverage data from WHO, investigators performed descriptive analyses to examine age-specific mortality and global vaccination rollout among older adults (as defined by each country), stratified by country World Bank income status. Data quality and COVID-19 death reporting frequency varied by data source; however, persons aged ≥60 years accounted for >80% of the overall COVID-19 mortality across all income groups, with upper- and lower-middle-income countries accounting for 80% of the overall estimated excess mortality. Effective COVID-19 vaccines were authorized for use in December 2020, with global supply scaled up sufficiently to meet country needs by late 2021 (6). COVID-19 vaccines are safe and highly effective in reducing severe COVID-19, hospitalizations, and mortality (7,8); nevertheless, country-reported median completed primary series coverage among adults aged ≥60 years only reached 76% by the end of 2022, substantially below the WHO goal, especially in middle- and low-income countries. Increased efforts are needed to increase primary series and booster dose coverage among all older adults as recommended by WHO and national health authorities.

Characterising within-hospitalSARS-CoV-2 transmission events using epidemiological and viral genomic data across two pandemic waves.

Hospital outbreaks of COVID19 result in considerable mortality and disruption to healthcare services and yet little is known about transmission within this setting. We characterise within hospital transmission by combining viral genomic and epidemiological data using Bayesian modelling amongst 2181 patients and healthcare workers from a large UK NHS Trust. Transmission events were compared between Wave 1 (1st March to 25th J'uly 2020) and Wave 2 (30th November 2020 to 24th January 2021). We show that staff-to-staff transmissions reduced from 31.6% to 12.9% of all infections. Patient-to-patient transmissions increased from 27.1% to 52.1%. 40%-50% of hospital-onset patient cases resulted in onward transmission compared to 4% of community-acquired cases. Control measures introduced during the pandemic likely reduced transmissions between healthcare workers but were insufficient to prevent increasing numbers of patient-to-patient transmissions. As hospital-acquired cases drive most onward transmission, earlier identification of nosocomial cases will be required to break hospital transmission chains.

Increased transmissibility and global spread of SARS-CoV-2 variants of concern as at June 2021.

We present a global analysis of the spread of recently emerged SARS-CoV-2 variants and estimate changes in effective reproduction numbers at country-specific level using sequence data from GISAID. Nearly all investigated countries demonstrated rapid replacement of previously circulating lineages by the World Health Organization-designated variants of concern, with estimated transmissibility increases of 29% (95% CI: 24-33), 25% (95% CI: 20-30), 38% (95% CI: 29-48) and 97% (95% CI: 76-117), respectively, for B.1.1.7, B.1.351, P.1 and B.1.617.2.

Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data.

There exists significant interest in developing statistical and computational tools for inferring 'who infected whom' in an infectious disease outbreak from densely sampled case data, with most recent studies focusing on the analysis of whole genome sequence data. However, genomic data can be poorly informative of transmission events if mutations accumulate too slowly to resolve individual transmission pairs or if there exist multiple pathogens lineages within-host, and there has been little focus on incorporating other types of outbreak data. We present here a methodology that uses contact data for the inference of transmission trees in a statistically rigorous manner, alongside genomic data and temporal data. Contact data is frequently collected in outbreaks of pathogens spread by close contact, including Ebola virus (EBOV), severe acute respiratory syndrome coronavirus (SARS-CoV) and Mycobacterium tuberculosis (TB), and routinely used to reconstruct transmission chains. As an improvement over previous, ad-hoc approaches, we developed a probabilistic model that relates a set of contact data to an underlying transmission tree and integrated this in the outbreaker2 inference framework. By analyzing simulated outbreaks under various contact tracing scenarios, we demonstrate that contact data significantly improves our ability to reconstruct transmission trees, even under realistic limitations on the coverage of the contact tracing effort and the amount of non-infectious mixing between cases. Indeed, contact data is equally or more informative than fully sampled whole genome sequence data in certain scenarios. We then use our method to analyze the early stages of the 2003 SARS outbreak in Singapore and describe the range of transmission scenarios consistent with contact data and genetic sequence in a probabilistic manner for the first time. This simple yet flexible model can easily be incorporated into existing tools for outbreak reconstruction and should permit a better integration of genomic and epidemiological data for inferring transmission chains.

outbreaker2: a modular platform for outbreak reconstruction.

BACKGROUND: Reconstructing individual transmission events in an infectious disease outbreak can provide valuable information and help inform infection control policy. Recent years have seen considerable progress in the development of methodologies for reconstructing transmission chains using both epidemiological and genetic data. However, only a few of these methods have been implemented in software packages, and with little consideration for customisability and interoperability. Users are therefore limited to a small number of alternatives, incompatible tools with fixed functionality, or forced to develop their own algorithms at considerable personal effort. RESULTS: Here we present outbreaker2, a flexible framework for outbreak reconstruction. This R package re-implements and extends the original model introduced with outbreaker, but most importantly also provides a modular platform allowing users to specify custom models within an optimised inferential framework. As a proof of concept, we implement the within-host evolutionary model introduced with TransPhylo, which is very distinct from the original genetic model in outbreaker, and demonstrate how even complex model results can be successfully included with minimal effort. CONCLUSIONS: outbreaker2 provides a valuable starting point for future outbreak reconstruction tools, and represents a unifying platform that promotes customisability and interoperability. Implemented in the R software, outbreaker2 joins a growing body of tools for outbreak analysis.

When are pathogen genome sequences informative of transmission events?

Recent years have seen the development of numerous methodologies for reconstructing transmission trees in infectious disease outbreaks from densely sampled whole genome sequence data. However, a fundamental and as of yet poorly addressed limitation of such approaches is the requirement for genetic diversity to arise on epidemiological timescales. Specifically, the position of infected individuals in a transmission tree can only be resolved by genetic data if mutations have accumulated between the sampled pathogen genomes. To quantify and compare the useful genetic diversity expected from genetic data in different pathogen outbreaks, we introduce here the concept of 'transmission divergence', defined as the number of mutations separating whole genome sequences sampled from transmission pairs. Using parameter values obtained by literature review, we simulate outbreak scenarios alongside sequence evolution using two models described in the literature to describe transmission divergence of ten major outbreak-causing pathogens. We find that while mean values vary significantly between the pathogens considered, their transmission divergence is generally very low, with many outbreaks characterised by large numbers of genetically identical transmission pairs. We describe the impact of transmission divergence on our ability to reconstruct outbreaks using two outbreak reconstruction tools, the R packages outbreaker and phybreak, and demonstrate that, in agreement with previous observations, genetic sequence data of rapidly evolving pathogens such as RNA viruses can provide valuable information on individual transmission events. Conversely, sequence data of pathogens with lower mean transmission divergence, including Streptococcus pneumoniae, Shigella sonnei and Clostridium difficile, provide little to no information about individual transmission events. Our results highlight the informational limitations of genetic sequence data in certain outbreak scenarios, and demonstrate the need to expand the toolkit of outbreak reconstruction tools to integrate other types of epidemiological data.

epicontacts: Handling, visualisation and analysis of epidemiological contacts.

Epidemiological outbreak data is often captured in line list and contact format to facilitate contact tracing for outbreak control. epicontacts is an R package that provides a unique data structure for combining these data into a single object in order to facilitate more efficient visualisation and analysis. The package incorporates interactive visualisation functionality as well as network analysis techniques. Originally developed as part of the Hackout3 event, it is now developed, maintained and featured as part of the R Epidemics Consortium (RECON). The package is available for download from the Comprehensive R Archive Network (CRAN) and GitHub.