COVID-19-associated deaths were reported in the United States (1). Understanding the demographic and clinical characteristics of decedents could inform medical and public health interventions focused on preventing COVID-19-associated mortality. This report describes decedents with laboratory-confirmed infection with SARS-CoV-2, the virus that causes COVID-19, using data from 1) the standardized CDC case-report form (case-based surveillance) (https://www.cdc.gov/coronavirus/2019-ncov/php/ reporting-pui.html) and 2) supplementary data (supplemental surveillance), such as underlying medical conditions and location of death, obtained through collaboration between CDC and 16 public health jurisdictions (15 states and New York City). Case-based surveillanceDemographic and clinical data about COVID-19 cases are reported to CDC from 50 states, the District of Columbia, New York City, and U.S. territories using a standardized case-report form (case-based surveillance) or in aggregate. Data on 52,166 deaths from 47 jurisdictions among persons with laboratoryconfirmed COVID-19 were reported individually to CDC via case-based surveillance during February 12-May 18, 2020. Among the 52,166 decedents, 55.4% were male, 79.6% were aged ≥65 years, 13.8% were Hispanic/Latino (Hispanic), 21.0% were black, 40.3% were white, 3.9% were Asian, 0.3% were American Indian/Alaska Native (AI/AN), 0.1% were Native Hawaiian or other Pacific Islander (NHPI), 2.6% were multiracial or other race, and race/ethnicity was unknown for 18.0%. (Table 1). Median decedent age was 78 years (interquartile range (IQR) = 67-87 years). Because information about underlying medical conditions was missing for the majority of these decedents (30,725; 58.9%), data regarding medical conditions were not analyzed further using the case-based surveillance data set. Because most decedents reported to the supplementary data program were also reported to case-based surveillance, no statistical comparisons of the decedent characteristics between the data sets were made. * Underlying medical conditions include cardiovascular disease (congenital heart disease, coronary artery disease, congestive heart failure, hypertension, cerebrovascular accident/stroke, valvular heart disease, conduction disorders or dysrhythmias, other cardiovascular disease); diabetes mellitus; chronic lung disease (chronic obstructive pulmonary disease/emphysema, asthma, tuberculosis, other chronic lung diseases); immunosuppression (cancer, human immunodeficiency virus (HIV) infection, identified as being immunosuppressed); chronic kidney disease (chronic kidney disease, end-stage renal disease, other kidney diseases); neurologic conditions (dementia, seizure disorder, other neurologic conditions); chronic liver disease (cirrhosis, alcoholic hepatitis, chronic liver disease, end-stage liver disease, hepatitis B, hepatitis C, nonalcoholic steatohepatitis, other chronic liver diseases); obesity (body mass index ≥30 kg/m 2 ). Information was collected from decedent medical records or death certificates. ...

html. † Underlying medical condition status was classified as "known" if any of these 10 conditions, specified on the standard case report form, were reported as present or absent: diabetes mellitus; cardiovascular disease (includes hypertension); severe obesity (body mass index ≥40 kg/m 2 ); chronic renal disease; chronic liver disease; chronic lung disease; immunosuppressive condition; autoimmune condition; neurologic condition (including neurodevelopmental, intellectual, physical, visual, or health impairment); and psychologic/psychiatric condition.

Background The Influenza Incidence Surveillance Project (IISP) monitored outpatient acute respiratory infection (ARI; defined as the presence of ≥2 respiratory symptoms not meeting ILI criteria) and influenza-like illness (ILI) to determine the incidence and contribution of associated viral etiologies. Methods From August 2010 through July 2011, 57 outpatient healthcare providers in 12 US sites reported weekly the number of visits for ILI and ARI and collected respiratory specimens on a subset for viral testing. The incidence was estimated using the number of patients in the practice as the denominator, and the virus-specific incidence of clinic visits was extrapolated from the proportion of patients testing positive. Results The age-adjusted cumulative incidence of outpatient visits for ARI and ILI combined was 95/1000 persons, with a viral etiology identified in 58% of specimens. Most frequently detected were rhinoviruses/enteroviruses (RV/EV) (21%) and influenza viruses (21%); the resulting extrapolated incidence of outpatient visits was 20 and 19/1000 persons respectively. The incidence of influenza virus-associated clinic visits was highest among patients aged 2–17 years, whereas other viruses had varied patterns among age groups. Conclusions The IISP provides a unique opportunity to estimate the outpatient respiratory illness burden by etiology. Influenza virus infection and RV/EV infection(s) represent a substantial burden of respiratory disease in the US outpatient setting, particularly among children.

Summary Background Since the introduction of pandemic influenza A (H1N1) to the USA in 2009, the Influenza Incidence Surveillance Project has monitored the burden of influenza in the outpatient setting through population-based surveillance. Methods From Oct 1, 2009, to July 31, 2013, outpatient clinics representing 13 health jurisdictions in the USA reported counts of influenza-like illness (fever including cough or sore throat) and all patient visits by age. During four years, staff at 104 unique clinics (range 35–64 per year) with a combined median population of 368 559 (IQR 352 595–428 286) attended 35 663 patients with influenza-like illness and collected 13 925 respiratory specimens. Clinical data and a respiratory specimen for influenza testing by RT-PCR were collected from the first ten patients presenting with influenza-like illness each week. We calculated the incidence of visits for influenza-like illness using the size of the patient population, and the incidence attributable to influenza was extrapolated from the proportion of patients with positive tests each week. Findings The site-median peak percentage of specimens positive for influenza ranged from 58.3% to 77.8%. Children aged 2 to 17 years had the highest incidence of influenza-associated visits (range 4.2–28.0 per 1000 people by year), and adults older than 65 years had the lowest (range 0.5–3.5 per 1000 population). Influenza A H3N2, pandemic H1N1, and influenza B equally co-circulated in the first post-pandemic season, whereas H3N2 predominated for the next two seasons. Of patients for whom data was available, influenza vaccination was reported in 3289 (28.7%) of 11 459 patients with influenza-like illness, and antivirals were prescribed to 1644 (13.8%) of 11 953 patients. Interpretation Influenza incidence varied with age groups and by season after the pandemic of 2009 influenza A H1N1. High levels of influenza virus circulation, especially in young children, emphasise the need for additional efforts to increase the uptake of influenza vaccines and antivirals. Funding US Centers for Disease Control and Prevention.

BackgroundInfectious 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 bodyFor 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.ConclusionsThese efforts, along with other infectious disease forecasting initiatives, can foster the continued advancement of forecasting science.