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Viral respiratory infections are an important public health concern due to their prevalence, transmissibility, and potential to cause serious disease. Disease severity is the product of several factors beyond the presence of the infectious agent, including specific host immune responses, host genetic makeup, and bacterial coinfections. To understand these interactions within natural infections, we designed a longitudinal cohort study actively surveilling respiratory viruses over the course of 19 months (2016 to 2018) in a diverse cohort in New York City. We integrated the molecular characterization of 800+ nasopharyngeal samples with clinical data from 104 participants. Transcriptomic data enabled the identification of respiratory pathogens in nasopharyngeal samples, the characterization of markers of immune response, the identification of signatures associated with symptom severity, individual viruses, and bacterial coinfections. Specific results include a rapid restoration of baseline conditions after infection, significant transcriptomic differences between symptomatic and asymptomatic infections, and qualitatively similar responses across different viruses. We created an interactive computational resource (Virome Data Explorer) to facilitate access to the data and visualization of analytical results.
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Coinfecção , Viroses , Vírus , Humanos , Coinfecção/genética , Viroma , Estudos Longitudinais , Vírus/genética , Viroses/genética , Viroses/epidemiologia , Bactérias/genética , Perfilação da Expressão GênicaRESUMO
The COVID-19 pandemic disrupted health systems and economies throughout the world during 2020 and was particularly devastating for the United States, which experienced the highest numbers of reported cases and deaths during 20201-3. Many of the epidemiological features responsible for observed rates of morbidity and mortality have been reported4-8; however, the overall burden and characteristics of COVID-19 in the United States have not been comprehensively quantified. Here we use a data-driven model-inference approach to simulate the pandemic at county-scale in the United States during 2020 and estimate critical, time-varying epidemiological properties underpinning the dynamics of the virus. The pandemic in the United States during 2020 was characterized by national ascertainment rates that increased from 11.3% (95% credible interval (CI): 8.3-15.9%) in March to 24.5% (18.6-32.3%) during December. Population susceptibility at the end of the year was 69.0% (63.6-75.4%), indicating that about one third of the US population had been infected. Community infectious rates, the percentage of people harbouring a contagious infection, increased above 0.8% (0.6-1.0%) before the end of the year, and were as high as 2.4% in some major metropolitan areas. By contrast, the infection fatality rate fell to 0.3% by year's end.
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COVID-19/epidemiologia , COVID-19/transmissão , SARS-CoV-2 , Número Básico de Reprodução , COVID-19/economia , COVID-19/mortalidade , Calibragem , Efeitos Psicossociais da Doença , Humanos , Incidência , Pandemias , Prevalência , Estados Unidos/epidemiologiaRESUMO
When an influenza pandemic emerges, temporary school closures and antiviral treatment may slow virus spread, reduce the overall disease burden, and provide time for vaccine development, distribution, and administration while keeping a larger portion of the general population infection free. The impact of such measures will depend on the transmissibility and severity of the virus and the timing and extent of their implementation. To provide robust assessments of layered pandemic intervention strategies, the Centers for Disease Control and Prevention (CDC) funded a network of academic groups to build a framework for the development and comparison of multiple pandemic influenza models. Research teams from Columbia University, Imperial College London/Princeton University, Northeastern University, the University of Texas at Austin/Yale University, and the University of Virginia independently modeled three prescribed sets of pandemic influenza scenarios developed collaboratively by the CDC and network members. Results provided by the groups were aggregated into a mean-based ensemble. The ensemble and most component models agreed on the ranking of the most and least effective intervention strategies by impact but not on the magnitude of those impacts. In the scenarios evaluated, vaccination alone, due to the time needed for development, approval, and deployment, would not be expected to substantially reduce the numbers of illnesses, hospitalizations, and deaths that would occur. Only strategies that included early implementation of school closure were found to substantially mitigate early spread and allow time for vaccines to be developed and administered, especially under a highly transmissible pandemic scenario.
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Vacinas contra Influenza , Influenza Humana , Humanos , Influenza Humana/tratamento farmacológico , Influenza Humana/epidemiologia , Influenza Humana/prevenção & controle , Preparações Farmacêuticas , Pandemias/prevenção & controle , Vacinas contra Influenza/uso terapêutico , Antivirais/farmacologia , Antivirais/uso terapêuticoRESUMO
The pathogenic bacteria Neisseria meningitidis, which causes invasive meningococcal disease (IMD), predominantly colonizes humans asymptomatically; however, invasive disease occurs in a small proportion of the population. Here, we explore the seasonality of IMD and develop and validate a suite of models for simulating and forecasting disease outcomes in the United States. We combine the models into multi-model ensembles (MME) based on the past performance of the individual models, as well as a naive equally weighted aggregation, and compare the retrospective forecast performance over a six-month forecast horizon. Deployment of the complete vaccination regimen, introduced in 2011, coincided with a change in the periodicity of IMD, suggesting altered transmission dynamics. We found that a model forced with the period obtained by local power wavelet decomposition best fit and forecast observations. In addition, the MME performed the best across the entire study period. Finally, our study included US-level data until 2022, allowing study of a possible IMD rebound after relaxation of non-pharmaceutical interventions imposed in response to the COVID-19 pandemic; however, no evidence of a rebound was found. Our findings demonstrate the ability of process-based models to retrospectively forecast IMD and provide a first analysis of the seasonality of IMD before and after the complete vaccination regimen.
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Infecções Meningocócicas , Neisseria meningitidis , Humanos , Estudos Retrospectivos , Pandemias , Infecções Meningocócicas/epidemiologia , Infecções Meningocócicas/microbiologiaRESUMO
BACKGROUND: Although the mechanisms of adaptive immunity to pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are still unknown, the immune response to the widespread endemic coronaviruses HKU1, 229E, NL63, and OC43 provide a useful reference for understanding repeat infection risk. METHODS: Here we used data from proactive sampling carried out in New York City from fall 2016 to spring 2018. We combined weekly nasal swab collection with self-reports of respiratory symptoms from 191 participants to investigate the profile of recurring infections with endemic coronaviruses. RESULTS: During the study, 12 individuals tested positive multiple times for the same coronavirus. We found no significant difference between the probability of testing positive at least once and the probability of a recurrence for the betacoronaviruses HKU1 and OC43 at 34 weeks after enrollment/first infection. We also found no significant association between repeat infections and symptom severity, but found strong association between symptom severity and belonging to the same family. CONCLUSIONS: This study provides evidence that reinfections with the same endemic coronavirus are not atypical in a time window shorter than 1 year and that the genetic basis of innate immune response may be a greater determinant of infection severity than immune memory acquired after a previous infection.
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Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/virologia , Coronavirus/isolamento & purificação , Adulto , Betacoronavirus , COVID-19/epidemiologia , COVID-19/imunologia , Coronavirus/genética , Infecções por Coronavirus/diagnóstico por imagem , Doenças Endêmicas , Humanos , Imunidade Inata , Cidade de Nova Iorque/epidemiologia , Infecções Respiratórias/diagnóstico por imagem , Infecções Respiratórias/epidemiologia , Infecções Respiratórias/virologia , SARS-CoV-2 , Análise de SobrevidaRESUMO
Chemical transformations involving the diffusion of reactants and subsequent chemical fixation steps are generally termed "diffusion-influenced reactions" (DIR). Virtually all biochemical processes in living media can be counted among them, together with those occurring in an ever-growing number of emerging nano-technologies. The role of the environment's geometry (obstacles, compartmentalization) and distributed reactivity (competitive reactants, traps) is key in modulating the rate constants of DIRs, and is therefore a prime design parameter. Yet, it is a formidable challenge to build a comprehensive theory that is able to describe the environment's "reactive geometry". Here we show that such a theory can be built by unfolding this many-body problem through addition theorems for special functions. Our method is powerful and general and allows one to study a given DIR reaction occurring in arbitrary "reactive landscapes", made of multiple spherical boundaries of given size and reactivity. Importantly, ready-to-use analytical formulas can be derived easily in most cases.
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Correction for 'Theory of diffusion-influenced reactions in complex geometries' by Marta Galanti et al., Phys. Chem. Chem. Phys., 2016, DOI: .
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We present a detailed theory for the total reaction rate constant of a composite core-shell nanoreactor, consisting of a central solid core surrounded by a hydrogel layer of variable thickness, where a given number of small catalytic nanoparticles are embedded at prescribed positions and are endowed with a prescribed surface reaction rate constant. Besides the precise geometry of the assembly, our theory accounts explicitly for the diffusion coefficients of the reactants in the hydrogel and in the bulk as well as for their transfer free energy jump upon entering the hydrogel shell. Moreover, we work out an approximate analytical formula for the overall rate constant, which is valid in the physically relevant range of geometrical and chemical parameters. We discuss in depth how the diffusion-controlled part of the rate depends on the essential variables, including the size of the central core. In particular, we derive some simple rules for estimating the number of nanocatalysts per nanoreactor for an efficient catalytic performance in the case of small to intermediate core sizes. Our theoretical treatment promises to provide a very useful and flexible tool for the design of superior performing nanoreactor geometries with optimized nanoparticle load.
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The spread of suicidal behavior among individuals is often described as a contagion; however, rigorous modeling of suicide as a dynamic, contagious process is minimal. Here, we develop and validate a model-inference system depicting suicide ideation and death and use it to quantify the contagion processes in the US associated with two prominent celebrity suicide events: Robin Williams during 2014 and Kate Spade and Anthony Bourdain, which occurred 3 days apart during 2018. We show that both events produced large transient increases of suicide contagion contact rates, i.e., the spread of suicidal thought and behavior, and a period of elevated suicidal ideation in the general population. Our modeling approach provides a framework for quantifying suicidal contagion and better understanding, preventing, and containing its spread.
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Ideação Suicida , Suicídio , Humanos , Suicídio/psicologia , Masculino , Estados Unidos/epidemiologia , FemininoRESUMO
By August 1, 2022, the SARS-CoV-2 virus had caused over 90 million cases of COVID-19 and one million deaths in the United States. Since December 2020, SARS-CoV-2 vaccines have been a key component of US pandemic response; however, the impacts of vaccination are not easily quantified. Here, we use a dynamic county-scale metapopulation model to estimate the number of cases, hospitalizations, and deaths averted due to vaccination during the first six months of vaccine availability. We estimate that COVID-19 vaccination was associated with over 8 million fewer confirmed cases, over 120 thousand fewer deaths, and 700 thousand fewer hospitalizations during the first six months of the campaign.
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COVID-19 , SARS-CoV-2 , Humanos , Vacinas contra COVID-19 , COVID-19/epidemiologia , COVID-19/prevenção & controle , Vacinação , HospitalizaçãoRESUMO
BACKGROUND: GNAO1-related encephalopathies include a broad spectrum of developmental disorders caused by de novo heterozygous mutations in the GNAO1 gene, encoding the G (o) subunit α of G-proteins. These conditions are characterized by epilepsy, movement disorders and developmental impairment, in combination or as isolated features. OBJECTIVE: This study aimed at describing the profile of neurovisual competences in children with GNAO1 deficiency to better characterize the phenotype of the disease spectrum. METHODS: Four male and three female patients with confirmed genetic diagnosis underwent neurological examination, visual function assessment, and neurovisual and ophthalmological evaluation. Present clinical history of epilepsy and movement disorders, and neuroimaging findings were also evaluated. RESULTS: The assessment revealed two trends in visual development. Some aspects of visual function, such as discrimination and perception of distance, depth and volume, appeared to be impaired at all ages, with no sign of improvement. Other aspects, reliant on temporal lobe competences (ventral stream) and more related to object-face exploration, recognition and environmental control, appeared to be preserved and improved with age. SIGNIFICANCE: Visual function is often impaired, with patterns of visual impairment affecting the ventral stream less.
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Deficiências do Desenvolvimento , Subunidades alfa Gi-Go de Proteínas de Ligação ao GTP , Percepção Visual , Feminino , Humanos , Masculino , Encefalopatias/complicações , Encefalopatias/genética , Deficiências do Desenvolvimento/complicações , Deficiências do Desenvolvimento/genética , Epilepsia/genética , Subunidades alfa Gi-Go de Proteínas de Ligação ao GTP/genética , Subunidades alfa Gi-Go de Proteínas de Ligação ao GTP/metabolismo , Heterozigoto , Transtornos dos Movimentos/genética , Fenótipo , Percepção Visual/genéticaRESUMO
Background: The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5-11 years on COVID-19 burden and resilience against variant strains. Methods: Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5-11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses. Findings: Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5-11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed. Interpretation: Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5-11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. Funding: Various (see acknowledgments).
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Our ability to forecast epidemics more than a few weeks into the future is constrained by the complexity of disease systems, our limited ability to measure the current state of an epidemic, and uncertainties in how human action will affect transmission. Realistic longer-term projections (spanning more than a few weeks) may, however, be possible under defined scenarios that specify the future state of critical epidemic drivers, with the additional benefit that such scenarios can be used to anticipate the comparative effect of control measures. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make 6-month ahead projections of the number of SARS-CoV-2 cases, hospitalizations and deaths. The SMH released nearly 1.8 million national and state-level projections between February 2021 and November 2022. SMH performance varied widely as a function of both scenario validity and model calibration. Scenario assumptions were periodically invalidated by the arrival of unanticipated SARS-CoV-2 variants, but SMH still provided projections on average 22 weeks before changes in assumptions (such as virus transmissibility) invalidated scenarios and their corresponding projections. During these periods, before emergence of a novel variant, a linear opinion pool ensemble of contributed models was consistently more reliable than any single model, and projection interval coverage was near target levels for the most plausible scenarios (e.g., 79% coverage for 95% projection interval). SMH projections were used operationally to guide planning and policy at different stages of the pandemic, illustrating the value of the hub approach for long-term scenario projections.
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Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.
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COVID-19 , Humanos , COVID-19/epidemiologia , Pandemias/prevenção & controle , SARS-CoV-2 , IncertezaRESUMO
Background: SARS-CoV-2 vaccination of persons aged 12 years and older has reduced disease burden in the United States. The COVID-19 Scenario Modeling Hub convened multiple modeling teams in September 2021 to project the impact of expanding vaccine administration to children 5-11 years old on anticipated COVID-19 burden and resilience against variant strains. Methods: Nine modeling teams contributed state- and national-level projections for weekly counts of cases, hospitalizations, and deaths in the United States for the period September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of: 1) presence vs. absence of vaccination of children ages 5-11 years starting on November 1, 2021; and 2) continued dominance of the Delta variant vs. emergence of a hypothetical more transmissible variant on November 15, 2021. Individual team projections were combined using linear pooling. The effect of childhood vaccination on overall and age-specific outcomes was estimated by meta-analysis approaches. Findings: Absent a new variant, COVID-19 cases, hospitalizations, and deaths among all ages were projected to decrease nationally through mid-March 2022. Under a set of specific assumptions, models projected that vaccination of children 5-11 years old was associated with reductions in all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios where children were not vaccinated. This projected effect of vaccinating children 5-11 years old increased in the presence of a more transmissible variant, assuming no change in vaccine effectiveness by variant. Larger relative reductions in cumulative cases, hospitalizations, and deaths were observed for children than for the entire U.S. population. Substantial state-level variation was projected in epidemic trajectories, vaccine benefits, and variant impacts. Conclusions: Results from this multi-model aggregation study suggest that, under a specific set of scenario assumptions, expanding vaccination to children 5-11 years old would provide measurable direct benefits to this age group and indirect benefits to the all-age U.S. population, including resilience to more transmissible variants.
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BACKGROUND: We characterized severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody test prevalence and positive test prevalence across New York City (NYC) in order to investigate disparities in testing outcomes by race and socioeconomic status (SES). METHODS: Serologic data were downloaded from the NYC Coronavirus data repository (August 2020-December 2020). Area-level characteristics for NYC neighborhoods were downloaded from United States census data and a socioeconomic vulnerability index was created. Spatial generalized linear mixed models were performed to examine the association between SES and antibody testing and positivity. RESULTS: The proportion of Hispanic population (posterior median, 0.001 [95% credible interval, 0.0003-0.002]), healthcare workers (0.003 [0.0001-0.006]), essential workers (0.003 [0.001-0.005]), age ≥65 years (0.003 [0.00002-0.006]), and high SES (SES quartile 3 vs 1: 0.034 [0.003-0.062]) were positively associated with antibody tests per 100000 residents. The White proportion (-0.002 [-0.003 to -0.001]), SES index (quartile 3 vs 1, -0.068 [-0.115 to -0.017]; quartile 4 vs 1, -0.077 [-0.134 to -0.018]) and age ≥65 years (-0.005 [-0.009 to -0.002]) were inversely associated with positive test prevalence (%), whereas the Hispanic (0.004 [0.002-0.006]) and essential worker (0.008 [0.003-0.012]) proportions had positive coefficients. CONCLUSIONS: Disparities in serologic testing and seropositivity exist on SES and race/ethnicity across NYC, indicative of excess coronavirus disease burden in vulnerable and marginalized populations.
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During the COVID-19 pandemic, US populations have experienced elevated rates of financial and psychological distress that could lead to increases in suicide rates. Rapid ongoing mental health monitoring is critical for early intervention, especially in regions most affected by the pandemic, yet traditional surveillance data are available only after long lags. Novel information on real-time population isolation and concerns stemming from the pandemic's social and economic impacts, via cellular mobility tracking and online search data, are potentially important interim surveillance resources. Using these measures, we employed transfer function model time-series analyses to estimate associations between daily mobility indicators (proportion of cellular devices completely at home and time spent at home) and Google Health Trends search volumes for terms pertaining to economic stress, mental health, and suicide during 2020 and 2021 both nationally and in New York City. During the first pandemic wave in early-spring 2020, over 50% of devices remained completely at home and searches for economic stressors exceeded 60,000 per 10 million. We found large concurrent associations across analyses between declining mobility and increasing searches for economic stressor terms (national proportion of devices at home: cross-correlation coefficient (CC) = 0.6 (p-value <0.001)). Nationally, we also found strong associations between declining mobility and increasing mental health and suicide-related searches (time at home: mood/anxiety CC = 0.53 (<0.001), social stressor CC = 0.51 (<0.001), suicide seeking CC = 0.37 (0.006)). Our findings suggest that pandemic-related isolation coincided with acute economic distress and may be a risk factor for poor mental health and suicidal behavior. These emergent relationships warrant ongoing attention and causal assessment given the potential for long-term psychological impact and suicide death. As US populations continue to face stress, Google search data can be used to identify possible warning signs from real-time changes in distributions of population thought patterns.
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COVID-19/psicologia , Telefone Celular/estatística & dados numéricos , Ferramenta de Busca/estatística & dados numéricos , Fatores Socioeconômicos , Suicídio/psicologia , Sistemas de Informação Geográfica , Humanos , Saúde Mental/estatística & dados numéricos , Cidade de Nova Iorque , Quarentena/estatística & dados numéricos , Ferramenta de Busca/tendências , Estresse Psicológico , Fatores de Tempo , Estados UnidosRESUMO
BACKGROUND: Respiratory viral infections are a leading cause of disease worldwide. However, the overall community prevalence of infections has not been properly assessed, as standard surveillance is typically acquired passively among individuals seeking clinical care. METHODS: We conducted a prospective cohort study in which participants provided daily diaries and weekly nasopharyngeal specimens that were tested for respiratory viruses. These data were used to analyze healthcare seeking behavior, compared with cross-sectional ED data and NYC surveillance reports, and used to evaluate biases of medically attended ILI as signal for population respiratory disease and infection. RESULTS: The likelihood of seeking medical attention was virus-dependent: higher for influenza and metapneumovirus (19%-20%), lower for coronavirus and RSV (4%), and 71% of individuals with self-reported ILI did not seek care and half of medically attended symptomatic manifestations did not meet the criteria for ILI. Only 5% of cohort respiratory virus infections and 21% of influenza infections were medically attended and classifiable as ILI. We estimated 1 ILI event per person/year but multiple respiratory infections per year. CONCLUSION: Standard, healthcare-based respiratory surveillance has multiple limitations. Specifically, ILI is an incomplete metric for quantifying respiratory disease, viral respiratory infection, and influenza infection. The prevalence of respiratory viruses, as reported by standard, healthcare-based surveillance, is skewed toward viruses producing more severe symptoms. Active, longitudinal studies are a helpful supplement to standard surveillance, can improve understanding of the overall circulation and burden of respiratory viruses, and can aid development of more robust measures for controlling the spread of these pathogens.
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Monitoramento Epidemiológico , Nasofaringe/virologia , Infecções Respiratórias/epidemiologia , Infecções Respiratórias/virologia , Adolescente , Adulto , Criança , Pré-Escolar , Infecções por Coronavirus/epidemiologia , Estudos Transversais , Feminino , Hospitalização , Humanos , Lactente , Recém-Nascido , Influenza Humana/epidemiologia , Estudos Longitudinais , Masculino , Pessoa de Meia-Idade , Cidade de Nova Iorque/epidemiologia , Prevalência , Estudos Prospectivos , Infecções por Vírus Respiratório Sincicial/epidemiologia , Adulto JovemRESUMO
BACKGROUND: Respiratory viral infections are a major cause of morbidity and mortality worldwide. However, their characterization is incomplete because prevalence estimates are based on syndromic surveillance data. Here, we address this shortcoming through the analysis of infection rates among individuals tested regularly for respiratory viral infections, irrespective of their symptoms. METHODS: We carried out longitudinal sampling and analysis among 214 individuals enrolled at multiple New York City locations from fall 2016 to spring 2018. We combined personal information with weekly nasal swab collection to investigate the prevalence of 18 respiratory viruses among different age groups and to assess risk factors associated with infection susceptibility. RESULTS: 17.5% of samples were positive for respiratory viruses. Some viruses circulated predominantly during winter, whereas others were found year round. Rhinovirus and coronavirus were most frequently detected. Children registered the highest positivity rates, and adults with daily contacts with children experienced significantly more infections than their counterparts without children. CONCLUSION: Respiratory viral infections are widespread among the general population with the majority of individuals presenting multiple infections per year. The observations identify children as the principal source of respiratory infections. These findings motivate further active surveillance and analysis of differences in pathogenicity among respiratory viruses.