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As part of the response to the highly pathogenic avian influenza A(H5N1) virus outbreak in U.S. cattle and poultry and the associated human cases, CDC and partners are monitoring influenza A virus levels and detection of the H5 subtype in wastewater. Among 48 states and the District of Columbia that performed influenza A testing of wastewater during May 12–July 13, 2024, a weekly average of 309 sites in 38 states had sufficient data for analysis, and 11 sites in four states reported high levels of influenza A virus. H5 subtype testing was conducted at 203 sites in 41 states, with H5 detections at 24 sites in nine states. For each detection or high level, CDC and state and local health departments evaluated data from other influenza surveillance systems and partnered with wastewater utilities and agriculture departments to investigate potential sources. Among the four states with high influenza A virus levels detected in wastewater, three states had corresponding evidence of human influenza activity from other influenza surveillance systems. Among the 24 sites with H5 detections, 15 identified animal sources within the sewershed or adjacent county, including eight milk-processing inputs. Data from these early investigations can help health officials optimize the use of wastewater surveillance during the upcoming respiratory illness season. [ABSTRACT FROM AUTHOR]

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness. [ABSTRACT FROM AUTHOR]

On April 1, 2024, the Texas Department of State Health Services reported that a dairy farm worker had tested positive for highly pathogenic avian influenza A(H5N1) virus after exposure to presumably infected dairy cattle; CDC confirmed these laboratory findings. A(H5N1) viruses were found in high concentrations in unpasteurized (raw) milk from infected cows. CDC is collaborating with the U.S. Department of Agriculture, the Food and Drug Administration, the Administration for Strategic Preparedness and Response, the Health Resources and Services Administration, the National Institute of Allergy and Infectious Diseases, and state and local public health and animal health officials using a coordinated One Health approach to identify and prepare for developments that could increase the risk to human health. Activities include monitoring of exposed persons, conducting syndromic and laboratory surveillance, planning epidemiologic investigations, and evaluating medical countermeasures. As of May 22, 2024, approximately 350 farm workers with exposure to dairy cattle or infected raw cow's milk had been monitored. These monitoring efforts identified a second human A(H5) case with conjunctivitis in Michigan, which was reported on May 22, 2024. CDC considers the current risk to the U.S. public from A(H5N1) viruses to be low; however, persons with exposure to infected animals or contaminated materials, including raw cow's milk, are at higher risk for A(H5N1) virus infection and should take recommended precautions, including using recommended personal protective equipment, self-monitoring for illness symptoms, and, if they are symptomatic, seeking prompt medical evaluation for influenza testing and antiviral treatment if indicated. Pasteurization inactivates A(H5N1) viruses, and the commercial milk supply is safe for consumption; however, all persons should avoid consuming raw milk or products produced from raw milk. Importantly, the risk to the public might change based on whether A(H5N1) viruses acquire genetic changes that increase their transmissibility to and among humans, which could increase the risk of an influenza pandemic. [ABSTRACT FROM AUTHOR]

Background Antiviral chemoprophylaxis is recommended for use during influenza outbreaks in nursing homes to prevent transmission and severe disease among non-ill residents. Centers for Disease Control and Prevention (CDC) guidance recommends prophylaxis be initiated for all non-ill residents once an influenza outbreak is detected and be continued for at least 14 days and until 7 days after the last laboratory-confirmed influenza case is identified. However, not all facilities strictly adhere to this guidance and the impact of such partial adherence is not fully understood. Methods We developed a stochastic compartmental framework to model influenza transmission within an average-sized US nursing home. We compared the number of symptomatic illnesses and hospitalizations under varying prophylaxis implementation strategies, in addition to different levels of prophylaxis uptake and adherence by residents and healthcare personnel (HCP). Results Prophylaxis implemented according to current guidance reduced total symptomatic illnesses and hospitalizations among residents by a median of 12% and 36%, respectively, compared with no prophylaxis. We did not find evidence that alternative implementations of prophylaxis were more effective: compared to full adoption of current guidance, partial adoption resulted in increased symptomatic illnesses and/or hospitalizations, and longer or earlier adoption offered no additional improvements. In addition, increasing uptake and adherence among nursing home residents was effective in reducing resident illnesses and hospitalizations, but increasing HCP uptake had minimal indirect impacts for residents. Conclusions The greatest benefits of influenza prophylaxis during nursing home outbreaks will likely be achieved through increasing uptake and adherence among residents and following current CDC guidance. [ABSTRACT FROM AUTHOR]

Background: Novel influenza viruses pose a potential pandemic risk, and rapid detection of infections in humans is critical to characterizing the virus and facilitating the implementation of public health response measures. Methods: We use a probabilistic framework to estimate the likelihood that novel influenza virus cases would be detected through testing in different community and healthcare settings (urgent care, emergency department, hospital, and intensive care unit [ICU]) while at low frequencies in the United States. Parameters were informed by data on seasonal influenza virus activity and existing testing practices. Results: In a baseline scenario reflecting the presence of 100 novel virus infections with similar severity to seasonal influenza viruses, the median probability of detecting at least one infection per month was highest in urgent care settings (72%) and when community testing was conducted at random among the general population (77%). However, urgent care testing was over 15 times more efficient (estimated as the number of cases detected per 100,000 tests) due to the larger number of tests required for community testing. In scenarios that assumed increased clinical severity of novel virus infection, median detection probabilities increased across all healthcare settings, particularly in hospitals and ICUs (up to 100%) where testing also became more efficient. Conclusions: Our results suggest that novel influenza virus circulation is likely to be detected through existing healthcare surveillance, with the most efficient testing setting impacted by the disease severity profile. These analyses can help inform future testing strategies to maximize the likelihood of novel influenza detection. [ABSTRACT FROM AUTHOR]

Reconstructing the incidence of SARS-CoV-2 infection is central to understanding the state of the pandemic. Seroprevalence studies are often used to assess cumulative infections as they can identify asymptomatic infection. Since July 2020, commercial laboratories have conducted nationwide serosurveys for the U.S. CDC. They employed three assays, with different sensitivities and specificities, potentially introducing biases in seroprevalence estimates. Using models, we show that accounting for assays explains some of the observed state-to-state variation in seroprevalence, and when integrating case and death surveillance data, we show that when using the Abbott assay, estimates of proportions infected can differ substantially from seroprevalence estimates. We also found that states with higher proportions infected (before or after vaccination) had lower vaccination coverages, a pattern corroborated using a separate dataset. Finally, to understand vaccination rates relative to the increase in cases, we estimated the proportions of the population that received a vaccine prior to infection. SARS-CoV-2 seroprevalence surveys aim to estimate the proportion of the population that has been infected, but their accuracy depends on the characteristics of the test assay used. Here, the authors use statistical models to assess the impact of the use of different assays on estimates of seroprevalence in the United States. [ABSTRACT FROM AUTHOR]

Influenza pandemics typically occur in multiple waves of infection, often associated with initial emergence of a novel virus, followed (in temperate regions) by a resurgence accompanying the onset of the annual influenza season. Here, we examined whether data collected from an initial pandemic wave could be informative, for the need to implement non-pharmaceutical measures in any resurgent wave. Drawing from the 2009 H1N1 pandemic in 10 states in the USA, we calibrated simple mathematical models of influenza transmission dynamics to data for laboratory confirmed hospitalisations during the initial 'spring' wave. We then projected pandemic outcomes (cumulative hospitalisations) during the fall wave, and compared these projections with data. Model results showed reasonable agreement for all states that reported a substantial number of cases in the spring wave. Using this model we propose a probabilistic decision framework that can be used to determine the need for preemptive measures such as postponing school openings, in advance of a fall wave. This work illustrates how model-based evidence synthesis, in real-time during an early pandemic wave, could be used to inform timely decisions for pandemic response. Author summary: Recent events have made clear the importance of pandemic preparedness. When faced with an initial wave of pandemic influenza, we offer a methodology for decision making with respect to non-pharmaceutical interventions in order to mitigate subsequent waves. For example, delayed school openings during vaccine roll-out can substantially reduce the number of hospitalisations and deaths in subsequent waves. We use first-wave data to quantify future risk and aid the associated policy decisions. [ABSTRACT FROM AUTHOR]

Background: Flu Near You (FNY) is an online participatory syndromic surveillance system that collects health-related information. In this article, we summarized the healthcare-seeking behavior of FNY participants who reported influenza-like illness (ILI) symptoms.Methods: We applied inverse probability weighting to calculate age-adjusted estimates of the percentage of FNY participants in the United States who sought health care for ILI symptoms during the 2015-2016 through 2018-2019 influenza season and compared seasonal trends across different demographic and regional subgroups, including age group, sex, census region, and place of care using adjusted χ 2 tests.Results: The overall age-adjusted percentage of FNY participants who sought healthcare for ILI symptoms varied by season and ranged from 22.8% to 35.6%. Across all seasons, healthcare seeking was highest for the <18 and 65+ years age groups, women had a greater percentage compared with men, and the South census region had the largest percentage while the West census region had the smallest percentage.Conclusions: The percentage of FNY participants who sought healthcare for ILI symptoms varied by season, geographical region, age group, and sex. FNY compliments existing surveillance systems and informs estimates of influenza-associated illness by adding important real-time insights into healthcare-seeking behavior. [ABSTRACT FROM AUTHOR]

The authors discuss the significance of collaboration in the development of modeling and data curation during the COVID-19 pandemic in the U.S.

Modeling complements surveillance data to inform coronavirus disease 2019 (COVID-19) public health decision making and policy development. This includes the use of modeling to improve situational awareness, assess epidemiological characteristics, and inform the evidence base for prevention strategies. To enhance modeling utility in future public health emergencies, the Centers for Disease Control and Prevention (CDC) launched the Infectious Disease Modeling and Analytics Initiative. The initiative objectives are to: (1) strengthen leadership in infectious disease modeling, epidemic forecasting, and advanced analytic work; (2) build and cultivate a community of skilled modeling and analytics practitioners and consumers across CDC; (3) strengthen and support internal and external applied modeling and analytic work; and (4) working with partners, coordinate government-wide advanced data modeling and analytics for infectious diseases. These efforts are critical to help prepare the CDC, the country, and the world to respond effectively to present and future infectious disease threats. [ABSTRACT FROM AUTHOR]

SARS-CoV-2, the virus that causes COVID-19, is constantly mutating, leading to new variants (1). Variants have the potential to affect transmission, disease severity, diagnostics, therapeutics, and natural and vaccine-induced immunity. In November 2020, CDC established national surveillance for SARS-CoV-2 variants using genomic sequencing. As of May 6, 2021, sequences from 177,044 SARS-CoV-2-positive specimens collected during December 20, 2020-May 6, 2021, from 55 U.S. jurisdictions had been generated by or reported to CDC. These included 3,275 sequences for the 2-week period ending January 2, 2021, compared with 25,000 sequences for the 2-week period ending April 24, 2021 (0.1% and 3.1% of reported positive SARS-CoV-2 tests, respectively). Because sequences might be generated by multiple laboratories and sequence availability varies both geographically and over time, CDC developed statistical weighting and variance estimation methods to generate population-based estimates of the proportions of identified variants among SARS-CoV-2 infections circulating nationwide and in each of the 10 U.S. Department of Health and Human Services (HHS) geographic regions.* During the 2-week period ending April 24, 2021, the B.1.1.7 and P.1 variants represented an estimated 66.0% and 5.0% of U.S. SARS-CoV-2 infections, respectively, demonstrating the rise to predominance of the B.1.1.7 variant of concern† (VOC) and emergence of the P.1 VOC in the United States. Using SARS-CoV-2 genomic surveillance methods to analyze surveillance data produces timely population-based estimates of the proportions of variants circulating nationally and regionally. Surveillance findings demonstrate the potential for new variants to emerge and become predominant, and the importance of robust genomic surveillance. Along with efforts to characterize the clinical and public health impact of SARS-CoV-2 variants, surveillance can help guide interventions to control the COVID-19 pandemic in the United States. [ABSTRACT FROM AUTHOR]

Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials and private citizens alike. In this work, we focus on a machine-learned anonymized mobility map (hereon referred to as AMM) aggregated over hundreds of millions of smartphones and evaluate its utility in forecasting epidemics. We factor AMM into a metapopulation model to retrospectively forecast influenza in the USA and Australia. We show that the AMM model performs on-par with those based on commuter surveys, which are sparsely available and expensive. We also compare it with gravity and radiation based models of mobility, and find that the radiation model’s performance is quite similar to AMM and commuter flows. Additionally, we demonstrate our model’s ability to predict disease spread even across state boundaries. Our work contributes towards developing timely infectious disease forecasting at a global scale using human mobility datasets expanding their applications in the area of infectious disease epidemiology. [ABSTRACT FROM AUTHOR]

The timing of influenza case incidence during epidemics can differ between regions within nations and states. We conducted a prospective 10-year evaluation (January 2008-February 2019) of a local influenza nowcasting (short-term forecasting) method in 3 urban counties in Sweden with independent public health administrations by using routine health information system data. Detection-of-epidemic-start (detection), peak timing, and peak intensity were nowcasted. Detection displayed satisfactory performance in 2 of the 3 counties for all nonpandemic influenza seasons and in 6 of 9 seasons for the third county. Peak-timing prediction showed satisfactory performance from the influenza season 2011-12 onward. Peak-intensity prediction also was satisfactory for influenza seasons in 2 of the counties but poor in 1 county. Local influenza nowcasting was satisfactory for seasonal influenza in 2 of 3 counties. The less satisfactory performance in 1 of the study counties might be attributable to population mixing with a neighboring metropolitan area. [ABSTRACT FROM AUTHOR]

Health planners from global to local levels must anticipate year‐to‐year and week‐to‐week variation in seasonal influenza activity when planning for and responding to epidemics to mitigate their impact. To help with this, countries routinely collect incidence of mild and severe respiratory illness and virologic data on circulating subtypes and use these data for situational awareness, burden of disease estimates and severity assessments. Advanced analytics and modelling are increasingly used to aid planning and response activities by describing key features of influenza activity for a given location and generating forecasts that can be translated to useful actions such as enhanced risk communications, and informing clinical supply chains. Here, we describe the formation of the Influenza Incidence Analytics Group (IIAG), a coordinated global effort to apply advanced analytics and modelling to public influenza data, both epidemiological and virologic, in real‐time and thus provide additional insights to countries who provide routine surveillance data to WHO. Our objectives are to systematically increase the value of data to health planners by applying advanced analytics and forecasting and for results to be immediately reproducible and deployable using an open repository of data and code. We expect the resources we develop and the associated community to provide an attractive option for the open analysis of key epidemiological data during seasonal epidemics and the early stages of an influenza pandemic. [ABSTRACT FROM AUTHOR]

The article examines the severity of the seasonal influenza epidemic in Utah from 2016 to 2018. It provides important health data from the U.S. National Center for Health Statistics such as health visits due to flu, number of people who had the illness and population of patients who were hospitalized because of influenza through time. It discusses the partnership of the Centers for Disease Control and Prevention (CDC) and the Utah Department of Health to address the medical situation.

Background: Infectious disease forecasting aims to predict characteristics of both seasonal epidemics and future pandemics. Accurate and timely infectious disease forecasts could aid public health responses by informing key preparation and mitigation efforts.Main Body: For forecasts to be fully integrated into public health decision-making, federal, state, and local officials must understand how forecasts were made, how to interpret forecasts, and how well the forecasts have performed in the past. Since the 2013-14 influenza season, the Influenza Division at the Centers for Disease Control and Prevention (CDC) has hosted collaborative challenges to forecast the timing, intensity, and short-term trajectory of influenza-like illness in the United States. Additional efforts to advance forecasting science have included influenza initiatives focused on state-level and hospitalization forecasts, as well as other infectious diseases. Using CDC influenza forecasting challenges as an example, this paper provides an overview of infectious disease forecasting; applications of forecasting to public health; and current work to develop best practices for forecast methodology, applications, and communication.Conclusions: These efforts, along with other infectious disease forecasting initiatives, can foster the continued advancement of forecasting science. [ABSTRACT FROM AUTHOR]

Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is determined by maximizing overall ensemble accuracy over past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats. Author summary: Seasonal influenza outbreaks cause millions of infections and tens of thousands of deaths in the United States each year. Forecasting the track of an influenza season can help public health officials, business leaders, and the general public decide how to respond to an ongoing or emerging outbreak. Our team assembled over 20 unique forecasting models for seasonal influenza and combined them together into a single "ensemble" model. We made predictions of the 2017/2018 influenza season, each week sending real-time forecasts to the US Centers for Disease Control and Prevention (CDC). In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, our ensemble model performed better on average than all individual forecast models in the ensemble. Based on results from this study, the CDC used forecasts from our ensemble model in public communication and internal reports in the subsequent 2018/2019 influenza season. [ABSTRACT FROM AUTHOR]

Introduction: Epidemic forecasting and prediction tools have the potential to provide actionable information in the midst of emerging epidemics. While numerous predictive studies were published during the 2016–2017 Zika Virus (ZIKV) pandemic, it remains unknown how timely, reproducible, and actionable the information produced by these studies was. Methods: To improve the functional use of mathematical modeling in support of future infectious disease outbreaks, we conducted a systematic review of all ZIKV prediction studies published during the recent ZIKV pandemic using the PRISMA guidelines. Using MEDLINE, EMBASE, and grey literature review, we identified studies that forecasted, predicted, or simulated ecological or epidemiological phenomena related to the Zika pandemic that were published as of March 01, 2017. Eligible studies underwent evaluation of objectives, data sources, methods, timeliness, reproducibility, accessibility, and clarity by independent reviewers. Results: 2034 studies were identified, of which n = 73 met the eligibility criteria. Spatial spread, R0 (basic reproductive number), and epidemic dynamics were most commonly predicted, with few studies predicting Guillain-Barré Syndrome burden (4%), sexual transmission risk (4%), and intervention impact (4%). Most studies specifically examined populations in the Americas (52%), with few African-specific studies (4%). Case count (67%), vector (41%), and demographic data (37%) were the most common data sources. Real-time internet data and pathogen genomic information were used in 7% and 0% of studies, respectively, and social science and behavioral data were typically absent in modeling efforts. Deterministic models were favored over stochastic approaches. Forty percent of studies made model data entirely available, 29% provided all relevant model code, 43% presented uncertainty in all predictions, and 54% provided sufficient methodological detail to allow complete reproducibility. Fifty-one percent of predictions were published after the epidemic peak in the Americas. While the use of preprints improved the accessibility of ZIKV predictions by a median of 119 days sooner than journal publication dates, they were used in only 30% of studies. Conclusions: Many ZIKV predictions were published during the 2016–2017 pandemic. The accessibility, reproducibility, timeliness, and incorporation of uncertainty in these published predictions varied and indicates there is substantial room for improvement. To enhance the utility of analytical tools for outbreak response it is essential to improve the sharing of model data, code, and preprints for future outbreaks, epidemics, and pandemics. [ABSTRACT FROM AUTHOR]

Influenza activity* in the United States during the 2018-19 season (September 30, 2018-May 18, 2019) was of moderate severity (1). Nationally, influenza-like illness (ILI)† activity began increasing in November, peaked during mid-February, and returned to below baseline in mid-April; the season lasted 21 weeks,§ making it the longest season in 10 years. Illness attributed to influenza A viruses predominated, with very little influenza B activity. Two waves of influenza A were notable during this extended season: influenza A(H1N1)pdm09 viruses from October 2018 to mid-February 2019 and influenza A(H3N2) viruses from February through May 2019. Compared with the 2017-18 influenza season, rates of hospitalization this season were lower for adults, but were similar for children. Although influenza activity is currently below surveillance baselines, testing for seasonal influenza viruses and monitoring for novel influenza A virus infections should continue year-round. Receiving a seasonal influenza vaccine each year remains the best way to protect against seasonal influenza and its potentially severe consequences. [ABSTRACT FROM AUTHOR]

Background Many countries have acquired antiviral stockpiles for pandemic influenza mitigation and a significant part of the stockpile may be focussed towards community-based treatment. Methods We developed a spreadsheet-based, decision tree model to assess outcomes averted and cost-effectiveness of antiviral treatment for outpatient use from the perspective of the healthcare payer in the UK. We defined five pandemic scenarios—one based on the 2009 A(H1N1) pandemic and four hypothetical scenarios varying in measures of transmissibility and severity. Results Community-based antiviral treatment was estimated to avert 14–23% of hospitalizations in an overall population of 62.28 million. Higher proportions of averted outcomes were seen in patients with high-risk conditions, when compared to non-high-risk patients. We found that antiviral treatment was cost-saving across pandemic scenarios for high-risk population groups, and cost-saving for the overall population in higher severity influenza pandemics. Antiviral effectiveness had the greatest influence on both the number of hospitalizations averted and on cost-effectiveness. Conclusions This analysis shows that across pandemic scenarios, antiviral treatment can be cost-saving for population groups at high risk of influenza-related complications. [ABSTRACT FROM AUTHOR]

Influenza infects an estimated 9-35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Realtime forecasts of influenza transmission can inform public health response to outbreaks.We present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making. [ABSTRACT FROM AUTHOR]

The United States 2017-18 influenza season (October 1, 2017-May 19, 2018) was a high severity season with high levels of outpatient clinic and emergency department visits for influenza-like illness (ILI), high influenza-related hospitalization rates, and elevated and geographically widespread influenza activity across the country for an extended period. Nationally, ILI activity began increasing in November, reaching an extended period of high activity during January-February, and remaining elevated through March. Influenza A(H3N2) viruses predominated through February and were predominant overall for the season; influenza B viruses predominated from March onward. This report summarizes U.S. influenza activity* during October 1, 2017-May 19, 2018.†. [ABSTRACT FROM AUTHOR]

Assessments of influenza season severity can guide public health action. We used the moving epidemic method to develop intensity thresholds (ITs) for 3 US surveillance indicators from the 2003-2004 through 2014-2015 influenza seasons (excluding the 2009 pandemic). The indicators were: 1) outpatient visits for influenza-like illness; 2) influenza-related hospitalizations; and 3) influenza- and pneumonia-related deaths. ITs were developed for the population overall and separately for children, adults, and older adults, and they were set at the upper limit of the 50% (IT50), 90% (IT90), and 98% (IT98) 1-sided confidence intervals of the geometric mean of each season's 3 highest values. Severity was classified as low if ≥2 systems peaked below IT50, moderate if ≥2 peaked between IT50 and IT90, high if ≥2 peaked between IT90 and IT98, and very high if ≥2 peaked above IT98. We pilot-tested this method with the 2015-2016 season and the 2009 pandemic. Overall, 4 seasons were classified as low severity, 7 as moderate, 2 as high, and none as very high. Among the age groups, older adults had the most seasons (n = 3) classified as high, and children were the only group to have seasons (n = 2) classified as very high. We will apply this method to classify the severity of future seasons and inform pandemic response. [ABSTRACT FROM AUTHOR]

Background. Latin America has a substantial burden of influenza and rising Internet access and could benefit from real-time influenza epidemic prediction web tools such as Google Flu Trends (GFT) to assist in risk communication and resource allocation during epidemics. However, there has never been a published assessment of GFT's accuracy inmost Latin American countries or in any low- to middle-income country. Our aim was to evaluate GFT in Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay. Methods. Weekly influenza-test positive proportions for the eight countries were obtained from FluNet for the period January 2011-December 2014. Concurrent weekly Google-predicted influenza activity in the same countries was abstracted from GFT. Pearson correlation coefficients between observed and Google-predicted influenza activity trends were determined for each country. Permutation tests were used to examine background seasonal correlation between FluNet and GFT by country. Results. There were frequent GFT prediction errors, with correlation ranging from r = -0.53 to 0.91. GFT-predicted influenza activity best correlated with FluNet data in Mexico follow byUruguay, Argentina, Chile, Brazil, Peru, Bolivia and Paraguay. Correlation was generally highest in the more temperate countries with more regular influenza seasonality and lowest in tropical regions. A substantial amount of autocorrelation was noted, suggestive that GFT is not fully specific for influenza virus activity. Conclusions. We note substantial inaccuracies with GFT-predicted influenza activity compared with FluNet throughout Latin America, particularly among tropical countries with irregular influenza seasonality. Our findings offer valuable lessons for future Internet- based biosurveillance tools. [ABSTRACT FROM AUTHOR]

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season.Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet).Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones.Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. [ABSTRACT FROM AUTHOR]

Zika virus is a cause of microcephaly and brain abnormalities (1), and it is the first known mosquito-borne infection to cause congenital anomalies in humans. The establishment of a comprehensive surveillance system to monitor pregnant women with Zika virus infection will provide data to further elucidate the full range of potential outcomes for fetuses and infants of mothers with asymptomatic and symptomatic Zika virus infection during pregnancy. In February 2016, Zika virus disease and congenital Zika virus infections became nationally notifiable conditions in the United States (2). Cases in pregnant women with laboratory evidence of Zika virus infection who have either 1) symptomatic infection or 2) asymptomatic infection with diagnosed complications of pregnancy can be reported as cases of Zika virus disease to ArboNET* (2), CDC's national arboviral diseases surveillance system. Under existing interim guidelines from the Council for State and Territorial Epidemiologists (CSTE), asymptomatic Zika virus infections in pregnant women who do not have known pregnancy complications are not reportable. ArboNET does not currently include pregnancy surveillance information (e.g., gestational age or pregnancy exposures) or pregnancy outcomes. To understand the full impact of infection on the fetus and neonate, other systems are needed for reporting and active monitoring of pregnant women with laboratory evidence of possible Zika virus infection during pregnancy. Thus, in collaboration with state, local, tribal, and territorial health departments, CDC established two surveillance systems to monitor pregnancies and congenital outcomes among women with laboratory evidence of Zika virus infection(†) in the United States and territories: 1) the U.S. Zika Pregnancy Registry (USZPR),(§) which monitors pregnant women residing in U.S. states and all U.S. territories except Puerto Rico, and 2) the Zika Active Pregnancy Surveillance System (ZAPSS), which monitors pregnant women residing in Puerto Rico. As of May 12, 2016, the surveillance systems were monitoring 157 and 122 pregnant women with laboratory evidence of possible Zika virus infection from participating U.S. states and territories, respectively. Tracking and monitoring clinical presentation of Zika virus infection, all prenatal testing, and adverse consequences of Zika virus infection during pregnancy are critical to better characterize the risk for congenital infection, the performance of prenatal diagnostic testing, and the spectrum of adverse congenital outcomes. These data will improve clinical guidance, inform counseling messages for pregnant women, and facilitate planning for clinical and public health services for affected families. [ABSTRACT FROM AUTHOR]

Background: In 2012, one third of cases in a multistate outbreak of variant influenza A(H3N2) virus ([H3N2]v) infection occurred in Ohio. We conducted an investigation of (H3N2)v cases associated with agricultural Fair A in Ohio.Methods: We surveyed Fair A swine exhibitors and their household members. Confirmed cases had influenza-like illness (ILI) and a positive laboratory test for (H3N2)v, and probable cases had ILI. We calculated attack rates. We determined risk factors for infection, using multivariable log-binomial regression.Results: We identified 20 confirmed and 94 probable cases associated with Fair A. Among 114 cases, the median age was 10 years, there were no hospitalizations or deaths, and 82% had swine exposure. In the exhibitor household cohort of 359 persons (83 households), we identified 6 confirmed cases (2%) and 40 probable cases (11%). An age of <10 years was a significant risk factor (P < .01) for illness. One instance of likely human-to-human transmission was identified.Conclusions: In this (H3N2)v outbreak, no evidence of sustained human-to-human (H3N2)v transmission was found. Our risk factor analysis contributed to the development of the recommendation that people at increased risk of influenza-associated complications, including children aged <5 years, avoid swine barns at fairs during the 2012 fair season. [ABSTRACT FROM AUTHOR]

Background: Seasonal influenza causes considerable morbidity and mortality across all age groups, and influenza vaccination was recommended in 2010 for all persons aged 6 months and above. We estimated the averted costs due to influenza vaccination, taking into account the seasonal economic burden of the disease. Methods: We used recently published values for averted outcomes due to influenza vaccination for influenza seasons 2005-06, 2006-07, 2007-08, and 2008-09, and age cohorts 6 months-4 years, 5-19 years, 20-64 years, and 65 years and above. Costs were calculated according to a payer and societal perspective (in 2009 US$), and took into account medical costs and productivity losses. Results: When taking into account direct medical costs (payer perspective), influenza vaccination was cost saving only for the older age group (65≥) in seasons 2005-06 and 2007-08. Using the same perspective, influenza vaccination resulted in total costs of $US 1.7 billion (95%CI: $US 0.3–4.0 billion) in 2006-07 and $US 1.8 billion (95%CI: $US 0.1–4.1 billion) in 2008-09. When taking into account a societal perspective (and including the averted lost earnings due to premature death) averted deaths in the older age group influenced the results, resulting in cost savings for all ages combined in season 07-08. Discussion: Influenza vaccination was cost saving in the older age group (65≥) when taking into account productivity losses and, in some seasons, when taking into account medical costs only. Averted costs vary significantly per season; however, in seasons where the averted burden of deaths is high in the older age group, averted productivity losses due to premature death tilt overall seasonal results towards savings. Indirect vaccination effects and the possibility of diminished case severity due to influenza vaccination were not considered, thus the averted burden due to influenza vaccine may be even greater than reported. [ABSTRACT FROM AUTHOR]

The rising importance of infectious disease modeling makes this an appropriate time for a guide for public health practitioners tasked with preparing for, and responding to, an influenza pandemic. We list several questions that public health practitioners commonly ask about pandemic influenza and match these with analytical methods, giving details on when during a pandemic the methods can be used, how long it might take to implement them, and what data are required. Although software to perform these tasks is available, care needs to be taken to understand: (1) the type of data needed, (2) the implementation of the methods, and (3) the interpretation of results in terms of model uncertainty and sensitivity. Public health leaders can use this article to evaluate the modeling literature, determine which methods can provide appropriate evidence for decision-making, and to help them request modeling work from in-house teams or academic groups. [ABSTRACT FROM AUTHOR]

Background. Human illness from influenza A(H7N9) was identified in March 2013, and candidate vaccine viruses were soon developed. To understand factors that may impact influenza vaccination programs, we developed a model to evaluate hospitalizations and deaths averted considering various scenarios. Methods. We utilized a model incorporating epidemic curves with clinical attack rates of 20% or 30% in a single wave of illness, case hospitalization ratios of 0.5% or 4.2%, and case fatality ratios of 0.08% or 0.53%. We considered scenarios that achieved 80% vaccination coverage, various starts of vaccination programs (16 or 8 weeks before, the same week of, or 8 or 16 weeks after start of pandemic), an administration rate of 10 or 30 million doses per week (the latter rate is an untested assumption), and 2 levels of vaccine effectiveness (2 doses of vaccine required; either 62% or 80% effective for persons aged <60 years, and either 43% or 60% effective for persons aged ⩾60 years). Results. The start date of vaccination campaigns most influenced impact; 141 000-2 200 000 hospitalizations and 11 000-281 000 deaths were averted when campaigns started before a pandemic, and <100-1 300 000 hospitalizations and 0-165 000 deaths were averted for programs beginning the same time as or after the introduction of the pandemic virus. The rate of vaccine administration and vaccine effectiveness did not influence campaign impact as much as timing of the start of campaign. Conclusions. Our findings suggest that efforts to improve the timeliness of vaccine production will provide the greatest impacts for future pandemic vaccination programs. [ABSTRACT FROM AUTHOR]

Background. Few data exist describing healthcare-seeking behaviors among persons with influenza-like illness (ILI) or adherence to influenza antiviral treatment recommendations.Methods. We analyzed adult responses to the Behavioral Risk Factor Surveillance System in 31 states and the District of Columbia (DC) and pediatric responses in 25 states and DC for January–April 2011 by demographics and underlying health conditions.Results. Among 75 088 adult and 15 649 child respondents, 8.9% and 33.9%, respectively, reported ILI. ILI was more frequent among adults with asthma (16%), chronic obstruction pulmonary disease (COPD; 26%), diabetes (12%), heart disease (19%), kidney disease (16%), or obesity (11%). Forty-five percent of adults and 57% of children sought healthcare for ILI. Thirty-five percent of adults sought care ≤2 days after ILI onset. Seeking care ≤2 days was more frequent among adults with COPD (48%) or heart disease (55%). Among adults with a self-reported physician diagnosis of influenza, 34% received treatment with antiviral medications. The only underlying health condition with a higher rate of treatment was diabetes (46%).Conclusions. Adults with underlying health conditions were more likely to report ILI, but the majority did not seek care promptly, missing opportunities for early influenza antiviral treatment. [ABSTRACT FROM PUBLISHER]

Background: The potential impact of an influenza pandemic can be assessed by calculating a set of transmissibility parameters, the most important being the reproduction number (R), which is defined as the average number of secondary cases generated per typical infectious case. Methods: We conducted a systematic review to summarize published estimates of R for pandemic or seasonal influenza and for novel influenza viruses (e.g. H5N1). We retained and summarized papers that estimated R for pandemic or seasonal influenza or for human infections with novel influenza viruses. Results: The search yielded 567 papers. Ninety-one papers were retained, and an additional twenty papers were identified from the references of the retained papers. Twenty-four studies reported 51 R values for the 1918 pandemic. The median R value for 1918 was 1.80 (interquartile range [IQR]: 1.47–2.27). Six studies reported seven 1957 pandemic R values. The median R value for 1957 was 1.65 (IQR: 1.53–1.70). Four studies reported seven 1968 pandemic R values. The median R value for 1968 was 1.80 (IQR: 1.56–1.85). Fifty-seven studies reported 78 2009 pandemic R values. The median R value for 2009 was 1.46 (IQR: 1.30–1.70) and was similar across the two waves of illness: 1.46 for the first wave and 1.48 for the second wave. Twenty-four studies reported 47 seasonal epidemic R values. The median R value for seasonal influenza was 1.28 (IQR: 1.19–1.37). Four studies reported six novel influenza R values. Four out of six R values were <1. Conclusions: These R values represent the difference between epidemics that are controllable and cause moderate illness and those causing a significant number of illnesses and requiring intensive mitigation strategies to control. Continued monitoring of R during seasonal and novel influenza outbreaks is needed to document its variation before the next pandemic. [ABSTRACT FROM AUTHOR]

During an outbreak of H3N2v variant influenza, we identified 306 cases in ten states. Most cases reported agricultural fair attendance and/or contact with swine prior to illness. We found no evidence of efficient or sustained person-to-person transmission of H3N2v.Background. Variant influenza virus infections are rare but may have pandemic potential if person-to-person transmission is efficient. We describe the epidemiology of a multistate outbreak of an influenza A(H3N2) variant virus (H3N2v) first identified in 2011.Methods. We identified laboratory-confirmed cases of H3N2v and used a standard case report form to characterize illness and exposures. We considered illness to result from person-to-person H3N2v transmission if swine contact was not identified within 4 days prior to illness onset.Results. From 9 July to 7 September 2012, we identified 306 cases of H3N2v in 10 states. The median age of all patients was 7 years. Commonly reported signs and symptoms included fever (98%), cough (85%), and fatigue (83%). Sixteen patients (5.2%) were hospitalized, and 1 fatal case was identified. The majority of those infected reported agricultural fair attendance (93%) and/or contact with swine (95%) prior to illness. We identified 15 cases of possible person-to-person transmission of H3N2v. Viruses recovered from patients were 93%–100% identical and similar to viruses recovered from previous cases of H3N2v. All H3N2v viruses examined were susceptible to oseltamivir and zanamivir and resistant to adamantane antiviral medications.Conclusions. In a large outbreak of variant influenza, the majority of infected persons reported exposures, suggesting that swine contact at an agricultural fair was a risk for H3N2v infection. We identified limited person-to-person H3N2v virus transmission, but found no evidence of efficient or sustained person-to-person transmission. Fair managers and attendees should be aware of the risk of swine-to-human transmission of influenza viruses in these settings. [ABSTRACT FROM PUBLISHER]

Background: Prior to emergence in human populations, zoonoses such as SARS cause occasional infections in human populations exposed to reservoir species. The risk of widespread epidemics in humans can be assessed by monitoring the reproduction number R (average number of persons infected by a human case). However, until now, estimating R required detailed outbreak investigations of human clusters, for which resources and expertise are not always available. Additionally, existing methods do not correct for important selection and under- ascertainment biases. Here, we present simple estimation methods that overcome many of these limitations. Methods and Findings: Our approach is based on a parsimonious mathematical model of disease transmission and only requires data collected through routine surveillance and standard case investigations. We apply it to assess the transmissibility of swine-origin influenza A H3N2v-M virus in the US, Nipah virus in Malaysia and Bangladesh, and also present a non-zoonotic example (cholera in the Dominican Republic). Estimation is based on two simple summary statistics, the proportion infected by the natural reservoir among detected cases (G) and among the subset of the first detected cases in each cluster (F). If detection of a case does not affect detection of other cases from the same cluster, we find that R can be estimated by 1 -- G; otherwise R can be estimated by 1 -- F when the case detection rate is low. In more general cases, bounds on R can still be derived. Conclusions: We have developed a simple approach with limited data requirements that enables robust assessment of the risks posed by emerging zoonoses. We illustrate this by deriving transmissibility estimates for the H3N2v-M virus, an important step in evaluating the possible pandemic threat posed by this virus. [ABSTRACT FROM AUTHOR]

A type A botulism outbreak was associated with commercially canned food; it is the first botulism outbreak in the United States involving a commercial cannery in >30 years. Because of possible widespread distribution of the implicated product, this outbreak was a major public health concern.Background. On 7 and 11 July 2007, health officials in Texas and Indiana, respectively, reported 4 possible cases of type A foodborne botulism to the US Centers for Disease Control and Prevention. Foodborne botulism is a rare and sometimes fatal illness caused by consuming foods containing botulinum neurotoxin.Methods. Investigators reviewed patients’ medical charts and food histories. Clinical specimens and food samples were tested for botulinum toxin and neurotoxin-producing Clostridium species. Investigators conducted inspections of the cannery that produced the implicated product.Results. Eight confirmed outbreak associated cases were identified from Indiana (n = 2), Texas (n = 3), and Ohio (n = 3). Botulinum toxin type A was identified in leftover chili sauce consumed by the Indiana patients and 1 of the Ohio patients. Cannery inspectors found violations of federal canned-food regulations that could have led to survival of Clostridium botulinum spores during sterilization. The company recalled 39 million cans of chili. Following the outbreak, the US Food and Drug Administration inspected other canneries with similar canning systems and issued warnings to the industry about the danger of C. botulinum and the importance of compliance with canned food manufacturing regulations.Conclusions. Commercially produced hot dog chili sauce caused these cases of type A botulism. This is the first US foodborne botulism outbreak involving a commercial cannery in >30 years. Sharing of epidemiologic and laboratory findings allowed for the rapid identification of implicated food items and swift removal of potentially deadly products from the market by US food regulatory authorities. [ABSTRACT FROM PUBLISHER]

The effects of influenza on a population are attributable to the clinical severity of illness and the number of persons infected, which can vary greatly between seasons or pandemics. To create a systematic framework for assessing the public health effects of an emerging pandemic, we reviewed data from past influenza seasons and pandemics to characterize severity and transmissibility (based on ranges of these measures in the United States) and outlined a formal assessment of the potential effects of a novel virus. The assessment was divided into 2 periods. Because early in a pandemic, measurement of severity and transmissibility is uncertain, we used a broad dichotomous scale in the initial assessment to divide the range of historic values. In the refined assessment, as more data became available, we categorized those values more precisely. By organizing and prioritizing data collection, this approach may inform an evidence-based assessment of pandemic effects and guide decision making. [ABSTRACT FROM AUTHOR]

During August 2011, influenza A (H3N2) variant [A(H3N2)v] virus infection developed in a child who attended an agricultural fair in Pennsylvania, USA; the virus resulted from reassortment of a swine influenza virus with influenza A(H1N1)pdm09. We interviewed fair attendees and conducted a retrospective cohort study among members of an agricultural club who attended the fair. Probable and confirmed cases of A(H3N2)v virus infection were defined by serology and genomic sequencing results, respectively. We identified 82 suspected, 4 probable, and 3 confirmed case-patients who attended the fair. Among 127 cohort study members, the risk for suspected case status increased as swine exposure increased from none (4%; referent) to visiting swine exhibits (8%; relative risk 2.1; 95% CI 0.2-53.4) to touching swine (16%; relative risk 4.4; 95% CI 0.8-116.3). Fairs may be venues for zoonotic transmission of viruses with epidemic potential; thus, health officials should investigate respiratory illness outbreaks associated with agricultural events. [ABSTRACT FROM AUTHOR]

Please cite this paper as: Regan et al. (2012) Epidemiology of influenza A (H1N1)pdm09-associated deaths in the United States, September-October 2009. Influenza and Other Respiratory Viruses 6(601), e169-e177. Background From April to July 2009, the United States experienced a wave of influenza A (H1N1)pdm09 virus (H1N1pdm09) infection. The majority of the deaths during that period occurred in persons <65 years of age with underlying medical conditions. Objective To describe the epidemiology of H1N1pdm09-associated deaths in the US during the fall of 2009. Methods We collected demographic, medical history, and cause of death information on a nationally representative, stratified random sample of 323 H1N1pdm09-associated deaths that occurred during September 1-October 31, 2009. Results Data were available for 302/323 (93%) deaths. Most cases (74%) were 18-64 years of age and had ≥1 underlying medical condition (72%). Among cases aged <18 years, 16/43 (37%) had a chronic lung disease, and 15/43 (35%) a neurological disorder; among cases aged ≥18 years, 94/254 (37%) had a chronic lung disease and 84/254 (33%) had a metabolic disorder. The median number of days between symptom onset and death was six among children (range, 1-48) and 12 among adults (range, 0-109). Influenza antiviral agents were prescribed for 187/268 (70%) of cases, but only 48/153 (31%) received treatment within 2 days of illness onset. Conclusions The characteristics of H1N1pdm09 deaths identified during the fall of 2009 were similar to those occurring April-July 2009. While most cases had conditions that were known to increase the risk for severe outcomes and were recommended to receive antiviral therapy, a minority of cases received antivirals early in the course of illness. [ABSTRACT FROM AUTHOR]

Objectives. The purpose of our study was to more accurately characterize people reporting influenza-like illness (ILI) and evaluate trends in health care seeking and influenza diagnosis and treatment during the 2009 influenza pandemic. Methods. From September 2009 to March 2010, we ascertained ILI (fever with cough or sore throat), health care seeking, and clinical diagnosis and treatment of influenza with influenza antiviral drugs among adults in 51 jurisdictions, and ILI and health care seeking among children in 41 jurisdictions. Results. Among 216 431 adults and 43 511 children, 8.1% and 28.4% reported ILI, respectively. ILI peaked during November interviews and was higher among young people and American Indian/Alaska Natives. Of those with ILI, 40% of adults and 56% of children reported seeking health care; 26% of adults who sought care reported receiving a diagnosis of influenza. Of adults reporting an influenza diagnosis, 36% were treated with influenza antiviral drugs; treatment was highest among adults aged 18 to 49 years. Conclusions. Analysis of ILI data from the Behavioral Risk Factor Surveillance System enabled a better understanding of the factors associated with selfreported ILI, health care seeking, and clinical influenza diagnosis and treatment, and will help inform year-to-year influenza trends. [ABSTRACT FROM AUTHOR]

From April 2009 through March 2010, during the pandemic (H1N1) 2009 outbreak, ≈8.2 million prescriptions for influenza neuraminidase-inhibiting antiviral drugs were filled in the United States. We estimated the number of hospitalizations likely averted due to use of these antiviral medications. After adjusting for prescriptions that were used for prophylaxis and personal stockpiles, as well as for patients who did not complete their drug regimen, we estimated the filled prescriptions prevented ≈8,400-12,600 hospitalizations (on the basis of median values). Approximately 60% of these prevented hospitalizations were among adults 18-64 years of age, with the remainder almost equally divided between children 0-17 years of age and adults ≥65 years of age. Public health officials should consider these estimates an indication of success of treating patients during the 2009 pandemic and a warning of the need for renewed planning to cope with the next pandemic. [ABSTRACT FROM AUTHOR]

In April 2009, an outbreak due to infection with the 2009 pandemic influenza A (H1N1) virus (pH1N1) was investigated in a New York City high school. We surveyed household contacts of ill students to characterize the extent of transmission within households, identify contact groups at highest risk for illness, and assess the potential for preventing household transmission. Influenza-like illness (ILI) was reported by 79 of 702 household contacts (11.3% attack rate). Multivariate analysis showed that older age was protective: for each increasing year of age, the risk of ILI was reduced 5%. Additional protective factors included antiviral prophylaxis and having had a household discussion about influenza. Providing care for the index case patient and watching television with the index case patient were risk factors among parents and siblings, respectively. Fifty percent of cases occurred within 3 days of onset of illness in the student. These factors have implications for mitigating the impact of pH1N1 transmission. [ABSTRACT FROM AUTHOR]

The article discusses the severity of the 2017-2018 influenza season in Minnesota and Utah. It identifies the characteristics of the severe season, including spot shortages of influenza antiviral medications and local severity assessments. The U.S. Centers for Disease Control and Prevention (CDC) is acknowledged for creating resources to support such local assessments.