Informing the pandemic response: the role of the WHO's COVID-19 Weekly Epidemiological Update.

On 31 December 2019, the Municipal Health Commission of Wuhan, China, reported a cluster of atypical pneumonia cases. On 5 January 2020, the WHO publicly released a Disease Outbreak News (DON) report, providing information about the pneumonia cases, implemented response interventions, and WHO's risk assessment and advice on public health and social measures. Following 9 additional DON reports and 209 daily situation reports, on 17 August 2020, WHO published the first edition of the COVID-19 Weekly Epidemiological Update (WEU). On 1 September 2023, the 158th edition of the WEU was published on WHO's website, marking its final issue. Since then, the WEU has been replaced by comprehensive global epidemiological updates on COVID-19 released every 4 weeks. During the span of its publication, the webpage that hosts the WEU and the COVID-19 Operational Updates was accessed annually over 1.4 million times on average, with visits originating from more than 100 countries. This article provides an in-depth analysis of the WEU process, from data collection to publication, focusing on the scope, technical details, main features, underlying methods, impact and limitations. We also discuss WHO's experience in disseminating epidemiological information on the COVID-19 pandemic at the global level and provide recommendations for enhancing collaboration and information sharing to support future health emergency responses.

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.

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.

Epidemiology of invasive meningococcal B disease in Australia, 1999-2015: priority populations for vaccination.

OBJECTIVES: To describe trends in the age-specific incidence of serogroup B invasive meningococcal disease (IMD) in Australia, 1999-2015. DESIGN, SETTING, PARTICIPANTS: Analysis in February 2017 of de-identified notification data from the Australian National Notifiable Diseases Surveillance System of all notifications of IMD in Australia with a recorded diagnosis date during 1999-2015.Major outcomes: IMD notification rates in Australia, 1999-2015, by age, serogroup, Indigenous status, and region. RESULTS: The incidence of meningococcal serogroup B (MenB) disease declined progressively from 1.52 cases per 100 000 population in 2001 to 0.47 per 100 000 in 2015. During 2006-2015, MenB accounted for 81% of IMD cases with a known serogroup; its highest incidence was among infants under 12 months of age (11.1 [95% CI, 9.81-12.2] per 100 000), children aged 1-4 years (2.82 [95% CI, 2.52-3.15] per 100 000), and adolescents aged 15-19 years (2.40 [95% CI, 2.16-2.67] per 100 000). Among the 473 infants under 2 years of age with MenB, 43% were under 7 months and 69% under 12 months of age. The incidence of meningococcal serogroup C (MenC) disease prior to the introduction of the MenC vaccine in 2003 was much lower in infants than for MenB (2.60 cases per 100 000), the rate peaking in people aged 15-19 years (3.32 per 100 000); the overall case fatality rate was also higher (MenC, 8%; MenB, 4%). The incidence of MenB disease was significantly higher among Indigenous than non-Indigenous Australians during 2006-2015 (incidence rate ratio [IRR], 3.8; 95% CI, 3.3-4.5). CONCLUSIONS: Based on disease incidence at its current low endemic levels, priority at risk age/population groups for MenB vaccination include all children between 2 months and 5 years of age, Indigenous children under 10 years of age, and all adolescents aged 15-19 years. Given marked variation in meningococcal disease trends over time, close scrutiny of current epidemiologic data is essential.

Atypical outbreak of Q fever affecting low-risk residents of a remote rural town in New South Wales.

We investigated an outbreak of Q fever in a remote rural town in New South Wales, Australia. Cases identified through active and passive case finding activities, and retrospective laboratory record review were interviewed using a standard questionnaire. Two sets of case-case analyses were completed to generate hypotheses regarding clinical, epidemiological and exposure risk factors associated with infection during the outbreak. Laboratory-confirmed outbreak cases (n=14) were compared with an excluded case group (n=16) and a group of historic Q fever cases from the region (n=106). In comparison with the historic case group, outbreak cases were significantly more likely to be female (43% vs. 18% males, P = 0.04) and identify as Aboriginal (29% vs. 7% non-Aboriginal, P = 0.03). Similarly, very few cases worked in high-risk occupations (21% vs. 84%, P < 0.01). Most outbreak cases (64%) reported no high-risk exposure activities in the month prior to onset. In comparison with the excluded case group, a significantly increased proportion of outbreak cases had contact with dogs (100% vs. 63%, P = 0.02) or sighted kangaroos on their residential property (100% vs. 60%, P = 0.02). High rates of tick exposure (92%) were also reported, although this was not significantly different from the excluded case group. While a source of this outbreak could not be confirmed, our findings suggest infections likely occurred via inhalation of aerosols or dust contaminated by Coxiella burnetii, dispersed through the town from either an unidentified animal facility or from excreta of native wildlife or feral animals. Alternatively transmission may have occurred via companion animals or tick vectors.

Serum levels of inflammatory cytokines in Rift Valley fever patients are indicative of severe disease.

BACKGROUND: Rift Valley fever (RVF) is a mosquito-borne viral zoonosis affecting domestic and wild ruminants, camels and humans. Outbreaks of RVF are characterized by a sudden onset of abortions and high mortality amongst domestic ruminants. Humans develop disease ranging from a mild flu-like illness to more severe complications including hemorrhagic syndrome, ocular and neurological lesions and death. During the RVF outbreak in South Africa in 2010/11, a total of 278 human cases were laboratory confirmed, including 25 deaths. The role of the host inflammatory response to RVF pathogenesis is not completely understood. METHODS: Virus load in serum from human fatal and non-fatal cases was determined by standard tissue culture infective dose 50 (TCID50) titration on Vero cells. Patient serum concentration of chemokines and cytokines involved in inflammatory responses (IL-8, RANTES, CXCL9, MCP-1, IP-10, IL-1ß, IL-6, IL-10, TNF and IL-12p70) was determined using cytometric bead assays and flow cytometry. RESULTS: Fatal cases had a 1-log10 higher TCID50/ml serum concentration of RVF virus (RVFV) than survivors (p < 0.05). There were no significant sequence differences between isolates recovered from fatal and non-fatal cases. Chemokines and pro- and anti-inflammatory cytokines were detected at significantly increased (IL-8, CXCL9, MCP-1, IP-10, IL-10) or decreased (RANTES) levels when comparing fatal cases to infected survivors and uninfected controls, or when comparing combined infected patients to uninfected controls. CONCLUSIONS: The results suggest that regulation of the host inflammatory responses plays an important role in the outcome of RVFV infection in humans. Dysregulation of the inflammatory response contributes to a fatal outcome. The cytokines and chemokines identified in this study that correlate with fatal outcomes warrant further investigation as markers for disease severity.

The impact of prior information on estimates of disease transmissibility using Bayesian tools.

The basic reproductive number (R0) and the distribution of the serial interval (SI) are often used to quantify transmission during an infectious disease outbreak. In this paper, we present estimates of R0 and SI from the 2003 SARS outbreak in Hong Kong and Singapore, and the 2009 pandemic influenza A(H1N1) outbreak in South Africa using methods that expand upon an existing Bayesian framework. This expanded framework allows for the incorporation of additional information, such as contact tracing or household data, through prior distributions. The results for the R0 and the SI from the influenza outbreak in South Africa were similar regardless of the prior information (R0 = 1.36-1.46, µ = 2.0-2.7, µ = mean of the SI). The estimates of R0 and µ for the SARS outbreak ranged from 2.0-4.4 and 7.4-11.3, respectively, and were shown to vary depending on the use of contact tracing data. The impact of the contact tracing data was likely due to the small number of SARS cases relative to the size of the contact tracing sample.

School closures during the 2009 influenza pandemic: national and local experiences.

BACKGROUND: School closure is a non-pharmaceutical intervention that was considered in many national pandemic plans developed prior to the start of the influenza A(H1N1)pdm09 pandemic, and received considerable attention during the event. Here, we retrospectively review and compare national and local experiences with school closures in several countries during the A(H1N1)pdm09 pandemic. Our intention is not to make a systematic review of country experiences; rather, it is to present the diversity of school closure experiences and provide examples from national and local perspectives. METHODS: Data were gathered during and following a meeting, organized by the European Centres for Disease Control, on school closures held in October 2010 in Stockholm, Sweden. A standard data collection form was developed and sent to all participants. The twelve participating countries and administrative regions (Bulgaria, China, France, Hong Kong Special Administrative Region (SAR), Italy, Japan, New Zealand, Serbia, South Africa, Thailand, United Kingdom, and United States) provided data. RESULTS: Our review highlights the very diverse national and local experiences on school closures during the A(H1N1)pdm09 pandemic. The processes including who was in charge of making recommendations and who was in charge of making the decision to close, the school-based control strategies, the extent of school closures, the public health tradition of responses and expectations on school closure varied greatly between countries. Our review also discusses the many challenges associated with the implementation of this intervention and makes recommendations for further practical work in this area. CONCLUSIONS: The single most important factor to explain differences observed between countries may have been the different public health practises and public expectations concerning school closures and influenza in the selected countries.

Determining the dynamics of influenza transmission by age.

BACKGROUND: It is widely accepted that influenza transmission dynamics vary by age; however methods to quantify the reproductive number by age group are limited. We introduce a simple method to estimate the reproductive number by modifying the method originally proposed by Wallinga and Teunis and using existing information on contact patterns between age groups. We additionally perform a sensitivity analysis to determine the potential impact of differential healthcare seeking patterns by age. We illustrate this method using data from the 2009 H1N1 Influenza pandemic in Gauteng Province, South Africa. RESULTS: Our results are consistent with others in showing decreased transmission with age. We show that results can change markedly when we make the account for differential healthcare seeking behaviors by age. CONCLUSIONS: We show substantial heterogeneity in transmission by age group during the Influenza A H1N1 pandemic in South Africa. This information can greatly assist in targeting interventions and implementing social distancing measures.

Estimating the reproductive number in the presence of spatial heterogeneity of transmission patterns.

BACKGROUND: Estimates of parameters for disease transmission in large-scale infectious disease outbreaks are often obtained to represent large groups of people, providing an average over a potentially very diverse area. For control measures to be more effective, a measure of the heterogeneity of the parameters is desirable. METHODS: We propose a novel extension of a network-based approach to estimating the reproductive number. With this we can incorporate spatial and/or demographic information through a similarity matrix. We apply this to the 2009 Influenza pandemic in South Africa to understand the spatial variability across provinces. We explore the use of five similarity matrices to illustrate their impact on the subsequent epidemic parameter estimates. RESULTS: When treating South Africa as a single entity with homogeneous transmission characteristics across the country, the basic reproductive number, R0, (and imputation range) is 1.33 (1.31, 1.36). When fitting a new model for each province with no inter-province connections this estimate varies little (1.23-1.37). Using the proposed method with any of the four similarity measures yields an overall R0 that varies little across the four new models (1.33 to 1.34). However, when allowed to vary across provinces, the estimated R0 is greater than one consistently in only two of the nine provinces, the most densely populated provinces of Gauteng and Western Cape. CONCLUSIONS: Our results suggest that the spatial heterogeneity of influenza transmission was compelling in South Africa during the 2009 pandemic. This variability makes a qualitative difference in our understanding of the epidemic. While the cause of this fluctuation might be partially due to reporting differences, there is substantial evidence to warrant further investigation.

Measles outbreak in South Africa: epidemiology of laboratory-confirmed measles cases and assessment of intervention, 2009-2011.

BACKGROUND: Since 1995, measles vaccination at nine and 18 months has been routine in South Africa; however, coverage seldom reached >95%. We describe the epidemiology of laboratory-confirmed measles case-patients and assess the impact of the nationwide mass vaccination campaign during the 2009 to 2011 measles outbreak in South Africa. METHODS: Serum specimens collected from patients with suspected-measles were tested for measles-specific IgM antibodies using an enzyme-linked immunosorbent assay and genotypes of a subset were determined. To estimate the impact of the nationwide mass vaccination campaign, we compared incidence in the seven months pre- (1 September 2009-11 April 2010) and seven months post-vaccination campaign (24 May 2010-31 December 2010) periods in seven provinces of South Africa. RESULTS: A total of 18,431 laboratory-confirmed measles case-patients were reported from all nine provinces of South Africa (cumulative incidence 37 per 100,000 population). The highest cumulative incidence per 100,000 population was in children aged <1 year (603), distributed as follows: <6 months (302/100,000), 6 to 8 months (1083/100,000) and 9 to 11 months (724/100,000). Forty eight percent of case-patients were ≥ 5 years (cumulative incidence 54/100,000). Cumulative incidence decreased with increasing age to 2/100,000 in persons ≥ 40 years. A single strain of measles virus (genotype B3) circulated throughout the outbreak. Prior to the vaccination campaign, cumulative incidence in the targeted vs. non-targeted age group was 5.9-fold higher, decreasing to 1.7 fold following the campaign (P<0.001) and an estimated 1,380 laboratory-confirmed measles case-patients were prevented. CONCLUSION: We observed a reduction in measles incidence following the nationwide mass vaccination campaign even though it was conducted approximately one year after the outbreak started. A booster dose at school entry may be of value given the high incidence in persons >5 years.

Epidemiologic Investigations into Outbreaks of Rift Valley Fever in Humans, South Africa, 2008-2011.

Rift Valley fever (RVF) is an emerging zoonosis posing a public health threat to humans in Africa. During sporadic RVF outbreaks in 2008-2009 and widespread epidemics in 2010-2011, 302 laboratory-confirmed human infections, including 25 deaths (case-fatality rate, 8%) were identified. Incidence peaked in late summer to early autumn each year, which coincided with incidence rate patterns in livestock. Most case-patients were adults (median age 43 years), men (262; 87%), who worked in farming, animal health or meat-related industries (83%). Most case-patients reported direct contact with animal tissues, blood, or other body fluids before onset of illness (89%); mosquitoes likely played a limited role in transmission of disease to humans. Close partnership with animal health and agriculture sectors allowed early recognition of human cases and appropriate preventive health messaging.

Reproductive number and serial interval of the first wave of influenza A(H1N1)pdm09 virus in South Africa.

BACKGROUND/OBJECTIVE: Describing transmissibility parameters of past pandemics from diverse geographic sites remains critical to planning responses to future outbreaks. We characterize the transmissibility of influenza A(H1N1)pdm09 (hereafter pH1N1) in South Africa during 2009 by estimating the serial interval (SI), the initial effective reproductive number (initial R(t)) and the temporal variation of R(t). METHODS: We make use of data from a central registry of all pH1N1 laboratory-confirmed cases detected throughout South Africa. Whenever date of symptom onset is missing, we estimate it from the date of specimen collection using a multiple imputation approach repeated 100 times for each missing value. We apply a likelihood-based method (method 1) for simultaneous estimation of initial R(t) and the SI; estimate initial R(t) from SI distributions established from prior field studies (method 2); and the Wallinga and Teunis method (method 3) to model the temporal variation of R(t). RESULTS: 12,360 confirmed pH1N1 cases were reported in the central registry. During the period of exponential growth of the epidemic (June 21 to August 3, 2009), we simultaneously estimate a mean R(t) of 1.47 (95% CI: 1.30-1.72) and mean SI of 2.78 days (95% CI: 1.80-3.75) (method 1). Field studies found a mean SI of 2.3 days between primary cases and laboratory-confirmed secondary cases, and 2.7 days when considering both suspected and confirmed secondary cases. Incorporating the SI estimate from field studies using laboratory-confirmed cases, we found an initial R(t) of 1.43 (95% CI: 1.38-1.49) (method 2). The mean R(t) peaked at 2.91 (95% CI: 0.85-2.91) on June 21, as the epidemic commenced, and R(t)>1 was sustained until August 22 (method 3). CONCLUSIONS: Transmissibility characteristics of pH1N1 in South Africa are similar to estimates reported by countries outside of Africa. Estimations using the likelihood-based method are in agreement with field findings.

Introduction of 2009 pandemic influenza A virus subtype H1N1 into South Africa: clinical presentation, epidemiology, and transmissibility of the first 100 cases.

BACKGROUND: We documented the introduction of 2009 pandemic influenza A virus subtype H1N1 (A[H1N1]pdm09) into South Africa and describe its clinical presentation, epidemiology, and transmissibility. METHODS: We conducted a prospective descriptive study of the first 100 laboratory-confirmed cases of A(H1N1)pdm09 infections identified through active case finding and surveillance. Infected patients and the attending clinicians were interviewed, and close contacts were followed up to investigate household transmission. FINDINGS: The first case was confirmed on 14 June 2009, and by 15 July 2009, 100 cases were diagnosed. Forty-two percent of patients reported international travel within 7 days prior to onset of illness. Patients ranged in age from 4 to 70 years (median age, 21.5 years). Seventeen percent of household contacts developed influenza-like illness, and 10% of household contacts had laboratory-confirmed A(H1N1)pdm09 infection. We found a mean serial interval (± SD) of 2.3 ± 1.3 days (range, 1-5 days) between successive laboratory-confirmed cases in the transmission chain. CONCLUSIONS: A(H1N1)pdm09 established itself rapidly in South Africa. Transmissibility of the virus was comparable to observations from outside of Africa and to seasonal influenza virus strains.

Searching for sharp drops in the incidence of pandemic A/H1N1 influenza by single year of age.

BACKGROUND: During the 2009 H1N1 pandemic (pH1N1), morbidity and mortality sparing was observed among the elderly population; it was hypothesized that this age group benefited from immunity to pH1N1 due to cross-reactive antibodies generated from prior infection with antigenically similar influenza viruses. Evidence from serologic studies and genetic similarities between pH1N1 and historical influenza viruses suggest that the incidence of pH1N1 cases should drop markedly in age cohorts born prior to the disappearance of H1N1 in 1957, namely those at least 52-53 years old in 2009, but the precise range of ages affected has not been delineated. METHODS AND FINDINGS: To test for any age-associated discontinuities in pH1N1 incidence, we aggregated laboratory-confirmed pH1N1 case data from 8 jurisdictions in 7 countries, stratified by single year of age, sex (when available), and hospitalization status. Using single year of age population denominators, we generated smoothed curves of the weighted risk ratio of pH1N1 incidence, and looked for sharp drops at varying age bandwidths, defined as a significantly negative second derivative. Analyses stratified by hospitalization status and sex were used to test alternative explanations for observed discontinuities. We found that the risk of laboratory-confirmed infection with pH1N1 declines with age, but that there was a statistically significant leveling off or increase in risk from about 45 to 50 years of age, after which a sharp drop in risk occurs until the late fifties. This trend was more pronounced in hospitalized cases and in women and was independent of the choice in smoothing parameters. The age range at which the decline in risk accelerates corresponds to the cohort born between 1951-1959 (hospitalized) and 1953-1960 (not hospitalized). CONCLUSIONS: The reduced incidence of pH1N1 disease in older individuals shows a detailed age-specific pattern consistent with protection conferred by exposure to influenza A/H1N1 viruses circulating before 1957.