BACKGROUND: Double-fortified salt (DFS; iron, iodine) improved iron status in randomized trials and was incorporated into India's social safety net programs, suggesting opportunities to address other micronutrient deficiencies. OBJECTIVES: To evaluate acceptability of quadruple-fortified salt (QFS; iron, iodine, folic acid, vitamin B(12)) in women and their households, using a randomized crossover trial design and triangle tests. METHODS: Women 18-49y (n=77) and their households were randomized to receive QFS or DFS in a randomized crossover design over a 3-week period (week 1: QFS/DFS, 2: iodized salt, 3: DFS/QFS). Each week, participants completed a 9-point hedonic questionnaire (1=dislike extremely to 9=like extremely) to evaluate five sensory domains (color, odor, taste, texture, overall acceptability) of the intervention, and remaining salt was weighed using a digital scale. Triangle tests were conducted among women to evaluate sensory discrimination of salt consumed in rice dishes prepared using standardized recipes. Mixed models were used to examine hedonic ratings and salt use; salt type, sequence, and period were included as fixed effects, and household was included as a random effect. Binomial tests were used to evaluate sensory discrimination of salt type in triangle tests. RESULTS: Mean hedonic ratings for most of the five sensory domains were ≥7 (like moderately) and did not differ by salt type (overall acceptability mean [SD]: QFS: 7.8 [0.7] vs. DFS: 7.7 [1.2]; p=0.68). Household salt use (weighed) did not differ by salt type. During the 3-week intervention period, weighed salt use and hedonic ratings significantly increased, indicating a period effect independent of salt type or sequence. In triangle tests, rice samples prepared with QFS, DFS, or iodized salt were not distinguishable. CONCLUSION: Acceptability of QFS was high, based on individual hedonic ratings and weighed household salt use. Rice dishes prepared with DFS, QFS, and iodized salt were not distinguishable. Findings informed the design of a randomized trial of QFS in this population. REGISTRATION NUMBERS: NCT03853304 and REF/2019/03/024479.

Wastewater testing can inform public health action as a component of polio outbreak response. During 2022-2023, a total of 7 US jurisdictions (5 states and 2 cities) participated in prospective or retrospective testing of wastewater for poliovirus after a paralytic polio case was identified in New York state. Two distinct vaccine-derived poliovirus type 2 viruses were detected in wastewater from New York state and New York City during 2022, representing 2 separate importation events. Of those viruses, 1 resulted in persistent community transmission in multiple New York counties and 1 paralytic case. No poliovirus was detected in the other participating jurisdictions (Connecticut, New Jersey, Michigan, and Illinois and Chicago, IL). The value of routine wastewater surveillance for poliovirus apart from an outbreak is unclear. However, these results highlight the ongoing risk for poliovirus importations into the United States and the need to identify undervaccinated communities and increase vaccination coverage to prevent paralytic polio.

PROBLEM/CONDITION: Seasonal influenza accounts for 9.3 million-41 million illnesses, 100,000-710,000 hospitalizations, and 4,900-51,000 deaths annually in the United States. Since 2003, the Influenza Hospitalization Surveillance Network (FluSurv-NET) has been conducting population-based surveillance for laboratory-confirmed influenza-associated hospitalizations in the United States, including weekly rate estimations and descriptions of clinical characteristics and outcomes for hospitalized patients. However, a comprehensive summary of trends in hospitalization rates and clinical data collected from the surveillance platform has not been available. REPORTING PERIOD: 2010-11 through 2022-23 influenza seasons. DESCRIPTION OF SYSTEM: FluSurv-NET conducts population-based surveillance for laboratory-confirmed influenza-associated hospitalizations among children and adults. During the reporting period, the surveillance network included 13-16 participating sites each influenza season, with prespecified geographic catchment areas that covered 27 million-29 million persons and included an estimated 8.8%-9.5% of the U.S. population. A case was defined as a person residing in the catchment area within one of the participating states who had a positive influenza laboratory test result within 14 days before or at any time during their hospitalization. Each site abstracted case data from hospital medical records into a standardized case report form, with selected variables submitted to CDC on a weekly basis for rate estimations. Weekly and cumulative laboratory-confirmed influenza-associated hospitalization rates per 100,000 population were calculated for each season from 2010-11 through 2022-23 and stratified by patient age (0-4 years, 5-17 years, 18-49 years, 50-64 years, and ≥65 years), sex, race and ethnicity, influenza type, and influenza A subtype. During the 2020-21 season, only the overall influenza hospitalization rate was reported because case counts were insufficient to estimate stratified rates. RESULTS: During the 2010-11 to 2022-23 influenza seasons, laboratory-confirmed influenza-associated hospitalization rates varied significantly across seasons. Before the COVID-19 pandemic, hospitalization rates per 100,000 population ranged from 8.7 (2011-12) to 102.9 (2017-18) and had consistent seasonality. After SARS-CoV-2 emerged, the hospitalization rate for 2020-21 was 0.8, and the rate did not return to recent prepandemic levels until 2022-23. Inconsistent seasonality also was observed during 2020-21 through 2022-23, with influenza activity being very low during 2020-21, extending later than usual during 2021-22, and occurring early during 2022-23. Molecular assays, particularly multiplex standard molecular assays, were the most common influenza test type in recent seasons, increasing from 12% during 2017-18 for both pediatric and adult cases to 43% and 55% during 2022-23 for pediatric and adult cases, respectively. During each season, adults aged ≥65 years consistently had the highest influenza-associated hospitalization rate across all age groups, followed in most seasons by children aged 0-4 years. Black or African American and American Indian or Alaska Native persons had the highest age-adjusted influenza-associated hospitalization rates across these seasons. Among patients hospitalized with influenza, the prevalence of at least one underlying medical condition increased with increasing age, ranging from 36.9% among children aged 0-4 years to 95.4% among adults aged ≥65 years. Consistently across each season, the most common underlying medical conditions among children and adolescents were asthma, neurologic disorders, and obesity. The most common underlying medical conditions among adults were hypertension, obesity, chronic metabolic disease, chronic lung disease, and cardiovascular disease. The proportion of FluSurv-NET patients with acute respiratory signs and symptoms at hospital admission decreased from 90.6% during 2018-19 to 83.2% during 2022-23. Although influenza antiviral use increased during the 2010-11 through the 2017-18 influenza seasons, it decreased from 90.2% during 2018-19 to 79.1% during 2022-23, particularly among children and adolescents. Admission to the intensive care unit, need for invasive mechanical ventilation, and in-hospital death ranged from 14.1% to 22.3%, 4.9% to 11.1%, and 2.2% to 3.5% of patients hospitalized with influenza, respectively, during the reported surveillance period. INTERPRETATIONS: Influenza continues to cause severe morbidity and mortality, particularly in older adults, and disparities have persisted in racial and ethnic minority groups. Persons with underlying medical conditions represented a large proportion of patients hospitalized with influenza. Increased use of multiplex tests and other potential changes in facility-level influenza testing practices (e.g., influenza screening at all hospital admissions) could have implications for the detection of influenza infections among hospitalized patients. Antiviral use decreased in recent seasons, and explanations for the decrease should be further evaluated. PUBLIC HEALTH ACTION: Continued robust influenza surveillance is critical to monitor progress in efforts to encourage antiviral treatment and improve clinical outcomes for persons hospitalized with influenza. In addition, robust influenza surveillance can potentially reduce disparities by informing efforts to increase access to preventive measures for influenza and monitoring any subsequent changes in hospitalization rates.

BACKGROUND: Fetal alcohol spectrum disorders (FASDs) are lifelong conditions that can occur in a person with prenatal alcohol exposure. Although studies using intensive, in-person assessments of children in selected communities have found higher estimates of children with FASDs than studies of healthcare claims data, claims-based studies provide more current information about individuals with recognized FASDs from diverse populations. We estimated the proportion of children with administratively reported FASDs in two large healthcare claims databases. METHODS: We analyzed Merative™ MarketScan® commercial and Medicaid claims databases, that include nationwide data from employer-sponsored health plans and from Medicaid programs in 8-10 states, respectively. For each database, we estimated the proportion of children aged 0-17 years with administratively reported FASDs, identified by one inpatient or two outpatient codes for prenatal alcohol exposure or fetal alcohol syndrome during the entire seven-year period from 2015 to 2021 and during each year. RESULTS: During 2015-2021, 1.2 per 10,000 commercially-insured and 6.1 per 10,000 Medicaid-insured children had an administratively reported FASD; estimates varied by sex, geography, and other available demographics. Among commercially-insured children, 0.5 per 10,000 in 2015 and 0.6 per 10,000 children in 2021 had an administratively reported FASD; among Medicaid-insured, 1.2 per 10,000 in 2015 and 2.1 per 10,000 children in 2021 had an administratively reported FASD. CONCLUSIONS: Although an underestimate of the true population of children with FASDs, patterns in administratively reported FASDs by demographics were consistent with previous studies. Healthcare claims studies can provide timely, ongoing information about children with recognized FASDs to complement in-persons studies.

Using multipathogen PCR testing, we identified 195 students with adenovirus type 4 infections on a university campus in South Carolina, USA, during January-May 2022. We co-detected other respiratory viruses in 43 (22%) students. Continued surveillance of circulating viruses is needed to prevent virus infection outbreaks in congregate communities.

BACKGROUND AND PURPOSE: To understand the association of sex-specific race and ethnicity on the short-term outcomes of initial and recurrent ischemic stroke events. METHODS: Using the Paul Coverdell National Acute Stroke Program from 2016-2020, we examined 426,062 ischemic stroke admissions from 629 hospitals limited to non-Hispanic White (NHW), non-Hispanic Black (NHB), and Hispanic patients. We performed multivariate logistic regression analyses to assess the combined effects of sex-specific race and ethnicity on short-term outcomes for acute ischemic stroke patients presenting with initial or recurrent stroke events. Outcomes assessed include rates of in-hospital death, discharge to home, and symptomatic intracranial hemorrhage (sICH) after reperfusion treatment. RESULTS: Among studied patients, the likelihood of developing sICH after reperfusion treatment for initial ischemic stroke was not significantly different. The likelihood of experiencing in-hospital death among patients presenting with initial stroke was notably higher among NHW males (AOR 1.59 [95% CI 1.46, 1.73]), NHW females (AOR 1.34 [95% CI 1.23, 1.45]), and Hispanic males (AOR 1.57 [95% CI 1.36, 1.81]) when compared to NHB females. Hispanic females were more likely to be discharged home when compared to NHB females after initial stroke event (AOR 1.32 [95% CI 1.23, 1.41]). NHB males (AOR 0.90 [95% CI 0.87, 0.94]) and NHW females (AOR 0.89 [95% CI 0.86, 0.92]) were less likely to be discharged to home. All groups with recurrent ischemic strokes experienced higher likelihood of in-hospital death when compared to NHB females with the highest likelihood among NHW males (AOR 2.13 [95% CI 1.87, 2.43]). Hispanic females had a higher likelihood of discharging home when compared to NHB females hospitalized for recurrent ischemic stroke, while NHB males and NHW females with recurrent ischemic stroke hospitalizations were less likely to discharge home. CONCLUSIONS: Sex-specific race and ethnic disparities remain for short-term outcomes in both initial and recurrent ischemic stroke hospitalizations. Further studies are needed to address disparities among recurrent ischemic stroke hospitalizations.

BACKGROUND: Older age and chronic conditions are associated with severe influenza outcomes; however, data are only comprehensively available for adults ≥65 years old. Using data from the Influenza Hospitalization Surveillance Network (FluSurv-NET), we identified characteristics associated with severe outcomes in adults 18-49 years old hospitalized with influenza. METHODS: We included FluSurv-NET data from nonpregnant adults 18-49 years old hospitalized with laboratory-confirmed influenza during the 2011-2012 through 2018-2019 seasons. We used bivariate and multivariable logistic regression to determine associations between select characteristics and severe outcomes including intensive care unit (ICU) admission, invasive mechanical ventilation (IMV), and in-hospital death. RESULTS: A total of 16 140 patients aged 18-49 years and hospitalized with influenza were included in the analysis; the median age was 39 years, and 26% received current-season influenza vaccine before hospitalization. Obesity, asthma, and diabetes mellitus were the most common chronic conditions. Conditions associated with a significantly increased risk of severe outcomes included age group 30-39 or 40-49 years (IMV, age group 30-39 years: adjusted odds ratio [aOR], 1.25; IMV, age group 40-49 years: aOR, 1.36; death, age group 30-39 years: aOR, 1.28; death, age group 40-49 years: aOR, 1.69), being unvaccinated (ICU: aOR, 1.18; IMV: aOR, 1.25; death: aOR, 1.48), and having chronic conditions including extreme obesity and chronic lung, cardiovascular, metabolic, neurologic, or liver diseases (ICU: range aOR, 1.22-1.56; IMV: range aOR, 1.17-1.54; death: range aOR, 1.43-2.36). CONCLUSIONS: To reduce the morbidity and mortality associated with influenza among adults aged 18-49 years, health care providers should strongly encourage receipt of annual influenza vaccine and lifestyle/behavioral modifications, particularly among those with chronic medical conditions.

Adults aged ≥65 years remain at elevated risk for severe COVID-19 disease and have higher COVID-19-associated hospitalization rates compared with those in younger age groups. Data from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) were analyzed to estimate COVID-19-associated hospitalization rates during January-August 2023 and identify demographic and clinical characteristics of hospitalized patients aged ≥65 years during January-June 2023. Among adults aged ≥65 years, hospitalization rates more than doubled, from 6.8 per 100,000 during the week ending July 15 to 16.4 per 100,000 during the week ending August 26, 2023. Across all age groups, adults aged ≥65 years accounted for 62.9% (95% CI = 60.1%-65.7%) of COVID-19-associated hospitalizations, 61.3% (95% CI = 54.7%-67.6%) of intensive care unit admissions, and 87.9% (95% CI = 80.5%-93.2%) of in-hospital deaths associated with COVID-19 hospitalizations. Most hospitalized adults aged ≥65 years (90.3%; 95% CI = 87.2%-92.8%) had multiple underlying conditions, and fewer than one quarter (23.5%; 95% CI = 19.5%-27.7%) had received the recommended COVID-19 bivalent vaccine. Because adults aged ≥65 years remain at increased risk for COVID-19-associated hospitalization and severe outcomes, guidance for this age group should continue to focus on measures to prevent SARS-CoV-2 infection, encourage vaccination, and promote early treatment for persons who receive a positive SARS-CoV-2 test result to reduce their risk for severe COVID-19-associated outcomes.

BACKGROUND: Influenza burden varies across seasons, partly due to differences in circulating influenza virus types or subtypes. Using data from the US population-based surveillance system, Influenza Hospitalization Surveillance Network (FluSurv-NET), we aimed to assess the severity of influenza-associated outcomes in individuals hospitalised with laboratory-confirmed influenza virus infections during the 2010-11 to 2018-19 influenza seasons. METHODS: To evaluate the association between influenza virus type or subtype causing the infection (influenza A H3N2, A H1N1pdm09, and B viruses) and in-hospital severity outcomes (intensive care unit [ICU] admission, use of mechanical ventilation or extracorporeal membrane oxygenation [ECMO], and death), we used FluSurv-NET to capture data for laboratory-confirmed influenza-associated hospitalisations from the 2010-11 to 2018-19 influenza seasons for individuals of all ages living in select counties in 13 US states. All individuals had to have an influenza virus test within 14 days before or during their hospital stay and an admission date between Oct 1 and April 30 of an influenza season. Exclusion criteria were individuals who did not have a complete chart review; cases from sites that contributed data for three or fewer seasons; hospital-onset cases; cases with unidentified influenza type; cases of multiple influenza virus type or subtype co-infection; or individuals younger than 6 months and ineligible for the influenza vaccine. Logistic regression models adjusted for influenza season, influenza vaccination status, age, and FluSurv-NET site compared odds of in-hospital severity by virus type or subtype. When missing, influenza A subtypes were imputed using chained equations of known subtypes by season. FINDINGS: Data for 122 941 individuals hospitalised with influenza were captured in FluSurv-NET from the 2010-11 to 2018-19 seasons; after exclusions were applied, 107 941 individuals remained and underwent influenza A virus imputation when missing A subtype (43·4%). After imputation, data for 104 969 remained and were included in the final analytic sample. Averaging across imputed datasets, 57·7% (weighted percentage) had influenza A H3N2, 24·6% had influenza A H1N1pdm09, and 17·7% had influenza B virus infections; 16·7% required ICU admission, 6·5% received mechanical ventilation or ECMO, and 3·0% died (95% CIs had a range of less than 0·1% and are not displayed). Individuals with A H1N1pdm09 had higher odds of in-hospital severe outcomes than those with A H3N2: adjusted odds ratios (ORs) for A H1N1pdm09 versus A H3N2 were 1·42 (95% CI 1·32-1·52) for ICU admission; 1·79 (1·60-2·00) for mechanical ventilation or ECMO use; and 1·25 (1·07-1·46) for death. The adjusted ORs for individuals infected with influenza B versus influenza A H3N2 were 1·06 (95% CI 1·01-1·12) for ICU admission, 1·14 (1·05-1·24) for mechanical ventilation or ECMO use, and 1·18 (1·07-1·31) for death. INTERPRETATION: Despite a higher burden of hospitalisations with influenza A H3N2, we found an increased likelihood of in-hospital severe outcomes in individuals hospitalised with influenza A H1N1pdm09 or influenza B virus. Thus, it is important for individuals to receive an annual influenza vaccine and for health-care providers to provide early antiviral treatment for patients with suspected influenza who are at increased risk of severe outcomes, not only when there is high influenza A H3N2 virus circulation but also when influenza A H1N1pdm09 and influenza B viruses are circulating. FUNDING: The US Centers for Disease Control and Prevention.

OBJECTIVE: Studies evaluating the incidence, source, and preventability of hospital-onset bacteremia and fungemia (HOB), defined as any positive blood culture obtained after 3 calendar days of hospital admission, are lacking in low- and middle-income countries (LMICs). DESIGN, SETTING, AND PARTICIPANTS: All consecutive blood cultures performed for 6 months during 2020-2021 in 2 hospitals in India were reviewed to assess HOB and National Healthcare Safety Network (NHSN) reportable central-line-associated bloodstream infection (CLABSI) events. Medical records of a convenience sample of 300 consecutive HOB events were retrospectively reviewed to determine source and preventability. Univariate and multivariable logistic regression analyses were performed to identify factors associated with HOB preventability. RESULTS: Among 6,733 blood cultures obtained from 3,558 hospitalized patients, there were 409 and 59 unique HOB and NHSN-reportable CLABSI events, respectively. CLABSIs accounted for 59 (14%) of 409 HOB events. There was a moderate but non-significant correlation (r = 0.51; P = .070) between HOB and CLABSI rates. Among 300 reviewed HOB cases, CLABSIs were identified as source in only 38 (13%). Although 157 (52%) of all 300 HOB cases were potentially preventable, CLABSIs accounted for only 22 (14%) of these 157 preventable HOB events. In multivariable analysis, neutropenia, and sepsis as an indication for blood culture were associated with decreased odds of HOB preventability, whereas hospital stay ≥7 days and presence of a urinary catheter were associated with increased likelihood of preventability. CONCLUSIONS: HOB may have utility as a healthcare-associated infection metric in LMIC settings because it captures preventable bloodstream infections beyond NHSN-reportable CLABSIs.

BACKGROUND: In 2021, four patients who had received solid organ transplants in the USA developed encephalitis beginning 2-6 weeks after transplantation from a common organ donor. We describe an investigation into the cause of encephalitis in these patients. METHODS: From Nov 7, 2021, to Feb 24, 2022, we conducted a public health investigation involving 15 agencies and medical centres in the USA. We tested various specimens (blood, cerebrospinal fluid, intraocular fluid, serum, and tissues) from the organ donor and recipients by serology, RT-PCR, immunohistochemistry, metagenomic next-generation sequencing, and host gene expression, and conducted a traceback of blood transfusions received by the organ donor. FINDINGS: We identified one read from yellow fever virus in cerebrospinal fluid from the recipient of a kidney using metagenomic next-generation sequencing. Recent infection with yellow fever virus was confirmed in all four organ recipients by identification of yellow fever virus RNA consistent with the 17D vaccine strain in brain tissue from one recipient and seroconversion after transplantation in three recipients. Two patients recovered and two patients had no neurological recovery and died. 3 days before organ procurement, the organ donor received a blood transfusion from a donor who had received a yellow fever vaccine 6 days before blood donation. INTERPRETATION: This investigation substantiates the use of metagenomic next-generation sequencing for the broad-based detection of rare or unexpected pathogens. Health-care workers providing vaccinations should inform patients of the need to defer blood donation for at least 2 weeks after receiving a yellow fever vaccine. Despite mitigation strategies and safety interventions, a low risk of transfusion-transmitted infections remains. FUNDING: US Centers for Disease Control and Prevention (CDC), the Biomedical Advanced Research and Development Authority, and the CDC Epidemiology and Laboratory Capacity Cooperative Agreement for Infectious Diseases.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naïve baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work.Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below. CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook. CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation. COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health. Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information& Data Science Pilot Project. Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation. DDS-NBDS: NSF III-1812699. EPIFORECASTS-ENSEMBLE1: Wellcome Trust (210758/Z/18/Z) GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowments, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. & Charlene Zalesky, and Claudia & Paul Raines GT-DeepCOVID: CDC MInD-Healthcare U01CK000531-Supplement. NSF (Expeditions CCF-1918770, CAREER IIS-2028586, RAPID IIS-2027862, Medium IIS-1955883, NRT DGE-1545362), CDC MInD program, ORNL and funds/computing resources from Georgia Tech and GTRI. IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096). IowaStateLW-STEM: Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1916204, NSF CCF-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics. JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, US Office of Foreign Disaster Assistance, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers fo Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant). LANL-GrowthRate: LANL LDRD 20200700ER. MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01. NotreDame-mobility and NotreDame-FRED: NSF RAPID DEB 2027718 UA-EpiCovDA: NSF RAPID Grant # 2028401. UCSB-ACTS: NSF RAPID IIS 2029626. UCSD-NEU: Google Faculty Award, DARPA W31P4Q-21-C-0014, COVID Supplement CDC-HHS-6U01IP001137-01. UMass-MechBayes: NIGMS R35GM119582, NSF 1749854. UMich-RidgeTfReg: The University of Michigan Physics Department and the University of Michigan Office of Research.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:UMass-Amherst IRBAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data and code referred to in the manuscript are publicly available. https://github.com/reichlab/covid19-forecast-hub/ https://github.com/reichlab/covidEnsembles https://zoltardata.com/project/44

Background As of August 21, 2021, >60% of the U.S. population aged ≥18 years were fully vaccinated with vaccines highly effective in preventing hospitalization due to Coronavirus Disease-2019 (COVID-19). Infection despite full vaccination (vaccine breakthrough) has been reported, but characteristics of those with vaccine breakthrough resulting in hospitalization and relative rates of hospitalization in unvaccinated and vaccinated persons are not well described, including during late June and July 2021 when the highly transmissible Delta variant predominated.Methods From January 1–June 30, 2021, cases defined as adults aged ≥18 years with laboratory-confirmed Severe Acute Respiratory Coronavirus-2 (SARS-CoV-2) infection were identified from >250 acute care hospitals in the population-based COVID-19-Associated Hospitalization Surveillance Network (COVID-NET). Through chart review for sampled cases, we examine characteristics associated with vaccination breakthrough. From January 24–July 24, 2021, state immunization information system data linked to both >37,000 cases representative cases and the defined surveillance catchment area population were used to compare weekly hospitalization rates in vaccinated and unvaccinated individuals. Unweighted case counts and weighted percentages are presented.Results From January 1 – June 30, 2021, fully vaccinated cases increased from 1 (0.01%) to 321 (16.1%) per month. Among 4,732 sampled cases, fully vaccinated persons admitted with COVID-19 were older compared with unvaccinated persons (median age 73 years [Interquartile Range (IQR) 65-80] v. 59 years [IQR 48-70]; p<0.001), more likely to have 3 or more underlying medical conditions (201 (70.8%) v. 2,305 (56.1%), respectively; p<0.001) and be residents of long-term care facilities [37 (14.5%) v. 146 (5.5%), respectively; p<0.001]. From January 24 – July 24, 2021, cumulative hospitalization rates were 17 times higher in unvaccinated persons compared with vaccinated persons (423 cases per 100,000 population v. 26 per 100,000 population, respectively); rate ratios were 23, 22 and 13 for those aged 18-49, 50-64, and ≥65 years respectively. For June 27 – July 24, hospitalization rates were ≥10 times higher in unvaccinated persons compared with vaccinated persons for all age groups across all weeks.Conclusion Population-based hospitalization rates show that unvaccinated adults aged ≥18 years are 17 times more likely to be hospitalized compared with vaccinated adults. Rates are far higher in unvaccinated persons in all adult age groups, including during a period when the Delta variant was the predominant strain of the SARS-CoV-2 virus. Vaccines continue to play a critical role in preventing serious COVID-19 illness and remain highly effective in preventing COVID-19 hospitalizations.Competing Interest StatementAll authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. Evan J. Anderson reports grants from Pfizer, grants from Merck, grants from PaxVax, grants from Micron, grants from Sanofi-Pasteur, grants from Janssen, grants from MedImmune, grants from GSK, personal fees from Sanofi-Pasteur, personal fees from Pfizer, personal fees from Medscape, personal fees from Kentucky Bioprocessing, Inc, personal fees from Sanofi-Pasteur, personal fees from Janssen, outside the submitted work; and his institution has also received funding from NIH to conduct clinical trials of Moderna and Janssen COVID-19 vaccines. Ruth Lynfield reports Associate Editor for American Academy of Pediatrics Red Book (Committee on Infectious Diseases), donated fee to Minnesota Department of Health. Laurie M. Billing reports grants from Council of State and Territorial Epidemiologists (CSTE), during the conduct of the study; grants from Centers for Disease Control and Prevention (CDC) outside the submitted work. William Schaffner reports personal fees from VBI Vaccines, outside the submitted work. No other potential conflicts of interest were disclosed.Funding StatementThis work was supported by the Centers of Disease Control and Prevention through an Emerging Infections Program cooperative agreement (grant CK17-1701) and through a Council of State and Territorial Epidemiologists cooperative agreement (grant NU38OT000297-02-00).Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This activity was reviewed by CDC and was conducted consistent with applicable federal law and CDC policy (see e.g., 45 C.F.R. part 46.102(l)(2), 21 C.F.R. part 56; 42 U.S.C. 241(d); 5 U.S.C.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesPublicly available data referred to in this analysis can be found at: https://gis.cdc.gov/grasp/covidnet/covid19_3.html https://gis.cdc.gov/grasp/COVIDNet/COVID19_5.html https://gis.cdc.gov/grasp/covidnet/covid19_3.html https://gis.cdc.gov/grasp/COVIDNet/COVID19_5.html

Background Identification of risk factors for COVID-19-associated hospitalization is needed to guide prevention and clinical care.Objective To examine if age, sex, race/ethnicity, and underlying medical conditions is independently associated with COVID-19-associated hospitalizations.Design Cross-sectional.Setting 70 counties within 12 states participating in the Coronavirus Disease 2019-Associated Hospitalization Surveillance Network (COVID-NET) and a population-based sample of non-hospitalized adults residing in the COVID-NET catchment area from the Behavioral Risk Factor Surveillance System.Participants U.S. community-dwelling adults (≥18 years) with laboratory-confirmed COVID-19-associated hospitalizations, March 1- June 23, 2020.Measurements Adjusted rate ratios (aRR) of hospitalization by age, sex, race/ethnicity and underlying medical conditions (hypertension, coronary artery disease, history of stroke, diabetes, obesity [BMI ≥30 kg/m2], severe obesity [BMI≥40 kg/m2], chronic kidney disease, asthma, and chronic obstructive pulmonary disease).Results Our sample included 5,416 adults with COVID-19-associated hospitalizations. Adults with (versus without) severe obesity (aRR:4.4; 95%CI: 3.4, 5.7), chronic kidney disease (aRR:4.0; 95%CI: 3.0, 5.2), diabetes (aRR:3.2; 95%CI: 2.5, 4.1), obesity (aRR:2.9; 95%CI: 2.3, 3.5), hypertension (aRR:2.8; 95%CI: 2.3, 3.4), and asthma (aRR:1.4; 95%CI: 1.1, 1.7) had higher rates of hospitalization, after adjusting for age, sex, and race/ethnicity. In models adjusting for the presence of an individual underlying medical condition, higher hospitalization rates were observed for adults ≥65 years, 45-64 years (versus 18-44 years), males (versus females), and non-Hispanic black and other race/ethnicities (versus non-Hispanic whites).Limitations Interim analysis limited to hospitalizations with underlying medical condition data.Conclusion Our findings elucidate groups with higher hospitalization risk that may benefit from targeted preventive and therapeutic interventions.Competing Interest StatementDr. Anderson reports personal fees from AbbVie, personal fees from Pfizer, grants from Pfizer, grants from Merck, grants from Micron, grants from Paxvax, grants from Sanofi Pasteur, grants from Novavax, grants from MedImmune, grants from Regeneron, grants from GSK, outside the submitted work. Mr. Henderson, Ms. Kim, Ms. George, and Ms. Hill report grants from Council of State and Territorial Epidemiologists (CSTE), during the conduct of the study. Dr. Lynfield reports grants from CDC- Emerging Infections Program, during the conduct of the study; and Royalties from a book on infectious disease surveillance and compensation for AAP Red Book (Report from Committee on Infectious Disease) donated to Minnesota Dept of Health. Dr. Schaffner reports grants from CDC, during the conduct of the study; personal fees from VBI Vaccines, outside the submitted work. Dr. Talbot reports other from Seqirus, outside the submitted work.Funding StatementThis work was supported by the Centers of Disease Control and Prevention through an Emerging Infections Program cooperative agreement (grant CK17-1701) and through a Council of State and Territorial Epidemiologists cooperative agreement (grant NU38OT000297-02-00).Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This analysis was exempt from CDC's Institutional Review Board, as it was considered part of public health surveillance and emergency response. Participating sites obtained approval for the COVID-NET surveillance protocol from their respective state and local IRBs, as required.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved regi try, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesData is not publically available at this time.

Although humoral immunity is essential for control of SARS-CoV-2, the molecular composition, binding epitopes and effector functions of the immunoglobulin G (IgG) antibodies that circulate in blood plasma following infection are unknown. Proteomic deconvolution of the circulating IgG repertoire (Ig-Seq (1) ) to the spike ectodomain (S-ECD (2) ) in four convalescent study subjects revealed that the plasma response is oligoclonal and directed predominantly (>80%) to S-ECD epitopes that lie outside the receptor binding domain (RBD). When comparing antibodies directed to either the RBD, the N-terminal domain (NTD) or the S2 subunit (S2) in one subject, just four IgG lineages (1 anti-S2, 2 anti-NTD and 1 anti-RBD) accounted for 93.5% of the repertoire. Although the anti-RBD and one of the anti-NTD antibodies were equally potently neutralizing in vitro , we nonetheless found that the anti-NTD antibody was sufficient for protection to lethal viral challenge, either alone or in combination as a cocktail where it dominated the effect of the other plasma antibodies. We identified in vivo protective plasma anti-NTD antibodies in 3/4 subjects analyzed and discovered a shared class of antibodies targeting the NTD that utilize unmutated or near-germline IGHV1-24, the most electronegative IGHV gene in the human genome. Structural analysis revealed that binding to NTD is dominated by interactions with the heavy chain, accounting for 89% of the entire interfacial area, with germline residues uniquely encoded by IGHV1-24 contributing 20% (149 Å (2) ). Together with recent reports of germline IGHV1-24 antibodies isolated by B-cell cloning (3,4) our data reveal a class of shared IgG antibodies that are readily observed in convalescent plasma and underscore the role of NTD-directed antibodies in protection against SARS-CoV-2 infection.

We urgently need answers to basic epidemiological questions regarding SARS-CoV-2 infection in pregnant and postpartum women and its effect on their newborns. While many national registries, health facilities, and research groups are collecting relevant data, we need a collaborative and methodologically rigorous approach to better combine these data and address knowledge gaps, especially those related to rare outcomes. We propose that using a sequential, prospective meta-analysis (PMA) is the best approach to generate data for policy- and practice-oriented guidelines. As the pandemic evolves, additional studies identified retrospectively by the steering committee or through living systematic reviews will be invited to participate in this PMA. Investigators can contribute to the PMA by either submitting individual patient data or running standardized code to generate aggregate data estimates. For the primary analysis, we will pool data using two-stage meta-analysis methods. The meta-analyses will be updated as additional data accrue in each contributing study and as additional studies meet study-specific time or data accrual thresholds for sharing. At the time of publication, investigators of 25 studies, including more than 76,000 pregnancies, in 41 countries had agreed to share data for this analysis. Among the included studies, 12 have a contemporaneous comparison group of pregnancies without COVID-19, and four studies include a comparison group of non-pregnant women of reproductive age with COVID-19. Protocols and updates will be maintained publicly. Results will be shared with key stakeholders, including the World Health Organization (WHO) Maternal, Newborn, Child, and Adolescent Health (MNCAH) Research Working Group. Data contributors will share results with local stakeholders. Scientific publications will be published in open-access journals on an ongoing basis.Competing Interest StatementClare Whitehead declares a a relationship with the following entities, Ferring Pharmaceuticals COVID19 Investigational, Grant, NHMRC Fellowship (salary support). Alice Panchaud declares the following research grants to institution: H2020-Grant (Consortium member of Innovative medicine initiative call 13 topic 9) (ConcePTION), Efficacy and safety studies on Medicines EMA/2017/09/PE/11, Lot 4, WP 2 lead (CONSIGN: Study on impact of COVID-19 infection and medicines in pregnancy), Safety monitoring of COVID-19 vaccines in the EU Reopening of competition no. 20 under a framework contract following procurement procedure EMA/2017/09/PE (Lot 3) 4. Federal Office of Public Health (207000 CHF). (The COVI-Preg registry). Edward Mullins declares a relationship with the following entities National Institute for Health Research (Project grant for PAN COVID study) Deborah Money declares a relationship with the following entities, Canadian Institutes of Health Research (payments to my institution only), Public Health Agency of Canada (payments to my institution only), BC Womens Foundation (payments to my institution only) and is a Member of the COVID-19 Immunity Task Force sponsored by the Canadian government. Torri D. Metz declares a relationship with the following entities, Pfizer (site Principal Investigator for SARS-CoV-2 vaccination in pregnancy study, money paid to institution and member of Medical Advisory Board for SARS-CoV-2 vaccination in pregnancy study, money paid to me), NICHD (subcommittee Chair for the NICHD Maternal-Fetal Medicine Units Network Gestational Research Assessments of COVID-19 (GRAVID) study), and Society for Maternal-Fetal Medicine (board member). Erica Lokken declares a relationship with the following entity, US NIH (paid institution). Karen L. Kotloff declares a relationship with the following entity, Bill and Melinda Gates Foundation. Siran He declares a relationship with the following entity, Bill and Melinda Gates Foundtion (payments made to my institution). Valerie Flaherman declares a relationship with the following entities, Bill and Melinda Gates Foundation (payments to my institution), Yellow Chair Foundati n (payments to my institution), Robert Woods Johnson Foundation (payments to my institution), CDC Foundation, California Health Care Foundation (payments to my institution), Tara Health Foundation (payments to my institution), UCSF Womens Health Center of Excellence (payments to my institution) and California Department of Health Care Services (payments made to my institution). Jose Sanin-Blair declares a relationship with the following entity, Ferring Pharmaceuticals which give a grant ($10,000) for the expenses of RECOGEST trial and is a part of the Columbian Federation of Perinatology Yalda Afshar declares a relationship with the following entities, Bill and Melinda Gates Foundation (payments made to my institution), CDC Foundation (payments made to my institution), Robert Woods Johnson Foundation (payments made to my institution), and UCLA Deans Office COVID-19 research (payments made to my institution). Rebecca Cliffton declares a relationship with the following entity, NIH HD36801 (MFMU Network DCC).Clinical TrialPROSPERO ID: 188955Funding StatementFunded by the Bill & Melinda Gates Foundation grant to Emily Smith (INV-022057) at George Washington University and a grant to Emily Smith via a grant from the Bill & Melinda Gates Foundation to Stephanie Gaw (INV-017035) at University of California San Francisco.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This is a protocol paper and thus exempt from ethical approval. Ultimately, the meta-analysis study is exempt from human research ethics approval as the study authors will be synthesizing de-identified or aggregate data.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThis is a protocol paper and there is no related data to share.

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident hospitalizations, incident cases, incident deaths, and cumulative deaths due to COVID-19 at national, state, and county levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work. Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below: AIpert-pwllnod: Natural Sciences and Engineering Research Council of Canada; Caltech-CS156: Gary Clinard Innovation Fund; CEID-Walk: University of Georgia; CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook; COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health; Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information & Data Science Pilot Project; Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation; CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation; DDS-NBDS: NSF III-1812699; epiforecasts-ensemble1: Wellcome Trust (210758/Z/18/Z) FDANIHASU: supported by the Intramural Research Program of the NIH/NIDDK; GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowment, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. & Charlene Zalesky, and Claudia & Paul Raines, CDC MInD-Healthcare U01CK000531-Supplement; IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096); Imperial-ensemble1: SB acknowledges funding from the Wellcome Trust (219415); Institute of Business Forecasting: IBF; IowaStateLW-STEM: NSF DMS-1916204, Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics; IUPUI CIS: NSF; JHU_CSSE-DECOM: JHU CSSE: National Science Foundation (NSF) RAPID Real-time Forecasting of COVID-19 risk in the USA. 2021-2022. Award ID: 2108526. National Science Foundation (NSF) RAPID Development of an interactive web-based dashboard to track COVID-19 in real-time. 2020. Award ID: 2028604; JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers for Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant); JHU_UNC_GAS-StatMechP ol: NIH NIGMS: R01GM140564; JHUAPL-Bucky: US Dept of Health and Human Services; KITmetricslab-select_ensemble: Daniel Wolffram gratefully acknowledges support by the Klaus Tschira Foundation; LANL-GrowthRate: LANL LDRD 20200700ER; MIT-Cassandra: MIT Quest for Intelligence; MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01; CA NU38OT000297 from the Council of State and Territorial Epidemiologists (CSTE); NotreDame-FRED: NSF RAPID DEB 2027718; NotreDame-mobility: NSF RAPID DEB 2027718; PSI-DRAFT: NSF RAPID Grant # 2031536; QJHong-Encounter: NSF DMR-2001411 and DMR-1835939; SDSC_ISG-TrendModel: The development of the dashboard was partly funded by the Fondation Privee des Hopitaux Universitaires de Geneve; UA-EpiCovDA: NSF RAPID Grant # 2028401; UChicagoCHATTOPADHYAY-UnIT: Defense Advanced Research Projects Agency (DARPA) #HR00111890043/P00004 (I. Chattopadhyay, University of Chicago); UCSB-ACTS: NSF RAPID IIS 2029626; UCSD_NEU-DeepGLEAM: Google Faculty Award, W31P4Q-21-C-0014; UMass-MechBayes: NIGMS #R35GM119582, NSF #1749854, NIGMS #R35GM119582; UMich-RidgeTfReg: This project is funded by the University of Michigan Physics Department and the University of Michigan Office of Research; UVA-Ensemble: National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and Virginia Dept of Health Grant VDH-21-501-0141; Wadnwani_AI-BayesOpt: This study is made possible by the generous support of the American People through the United States Agency for International Development (USAID). The work described in this article was implemented under the TRACETB Project, managed by WIAI under the terms of Cooperative Agreement Number 72038620CA00006. The contents of this manuscript are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government; WalmartLabsML-LogForecasting: Team acknowledges Walmart to support this study Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data produced are available online at https://github.com/reichlab/covid19-forecast-hub https://github.com/reichlab/covid19-forecast-hub

Background New sequencing technologies have lowered financial barriers to whole genome sequencing, but resulting assemblies are often fragmented and far from ‘finished’. Updating multi-scaffold drafts to chromosome-level status can be achieved through experimental mapping or re-sequencing efforts. Avoiding the costs associated with such approaches, comparative genomic analysis of gene order conservation (synteny) to predict scaffold neighbours (adjacencies) offers a potentially useful complementary method for improving draft assemblies.Results We employed three gene synteny-based methods applied to 21 Anopheles mosquito assemblies to produce consensus sets of scaffold adjacencies. For subsets of the assemblies we integrated these with additional supporting data to confirm and complement the synteny-based adjacencies: six with physical mapping data that anchor scaffolds to chromosome locations, 13 with paired-end RNA sequencing (RNAseq) data, and three with new assemblies based on re-scaffolding or Pacific Biosciences long-read data. Our combined analyses produced 20 new superscaffolded assemblies with improved contiguities: seven for which assignments of non-anchored scaffolds to chromosome arms span more than 75% of the assemblies, and a further seven with chromosome anchoring including an 88% anchored Anopheles arabiensis assembly and, respectively, 73% and 84% anchored assemblies with comprehensively updated cytogenetic photomaps for Anopheles funestus and Anopheles stephensi.Conclusions Experimental data from probe mapping, RNAseq, or long-read technologies, where available, all contribute to successful upgrading of draft assemblies. Our comparisons show that gene synteny-based computational methods represent a valuable alternative or complementary approach. Our improved Anopheles reference assemblies highlight the utility of applying comparative genomics approaches to improve community genomic resources.ADADSEQAGOAGOUTI-basedAGOUTIannotated genome optimization using transcriptome information toolALNalignment-basedCAMSAcomparative analysis and merging of scaffold assemblies toolDPdynamic programmingFISHfluorescence in situ hybridizationGAGOS-ASMGOS-ASMGene order scaffold assemblerKbpkilobasepairsMbpmegabasepairsOSORTHOSTITCHPacBioPacific BiosciencesPBPacBio-basedPHYphysical-mapping-basedRNAseqRNA sequencingQTLquantitative trait lociSYNsynteny-based.

Background The recent detection of the South Asian malaria vector An. stephensi in Ethiopia and other regions in the Horn of Africa has raised concerns about its potential impact on malaria transmission. We report here findings of survey for this species in eastern Ethiopia using both morphological and molecular methods for species identification.Methods Adult and larval/pupal collections were conducted at ten sites in eastern Ethiopia and Anopheles specimens’ species were determined using standard morphological keys and genetic analysis.Results In total, 2,231 morphologically identified An. stephensi were collected. A molecular approach incorporating both PCR endpoint assay and sequencing of portions of the internal transcribed spacer 2 (ITS2) and cytochrome oxidase I (COI) loci confirmed the identity of the An. stephensi in most cases (119/124 of the morphologically identified An. stephensi confirmed molecularly). Additionally, we observed Aedes aegypti larvae and pupae at many of the An. stephensi larval habitats.Conclusions Our findings show that An. stephensi is widely distributed in eastern Ethiopia and highlight the need for further surveillance in the southern, western and northern parts of the country and throughout the Horn of Africa.

From August 2020 to June 2021, we assessed the efficacy of SumiShield 50WG (clothianidin), Fludora Fusion 56.25WP-SB (mixture of clothianidin and deltamethrin) and Actellic 300CS (pirimiphos-methyl) in experimental huts when partially sprayed against wild, free-flying populations of Anopheles gambiae s.l. in Tiassalé, Côte d'Ivoire. A one-month baseline period of mosquito collections was conducted to determine mosquito density and resting behavior in unsprayed huts, after which two treatments of partial indoor residual spraying (IRS) were tested: spraying only the top half of walls + ceilings or only the bottom half of walls + ceilings. These were compared to fully sprayed applications using the three IRS insecticide formulations, during twenty nights per month of collection for nine consecutive months. Mortality was assessed at the time of collection, and after a 24 h holding period (Actellic) or up to 120 h (SumiShield and Fludora Fusion). Unsprayed huts were used as a negative control. The efficacy of each partially sprayed treatment of each insecticide was compared monthly to the fully sprayed huts over the study period with a non-inferiority margin set at 10%. The residual efficacy of each insecticide sprayed was also monitored. A total of 2197 Anopheles gambiae s.l. were collected during the baseline and 17,835 during the 9-month period after spraying. During baseline, 42.6% were collected on the bottom half versus 24.3% collected on the top half of the walls, and 33.1% on the ceilings. Over the nine-month post treatment period, 73.5% were collected on the bottom half of the wall, 11.6% collected on the top half and 14.8% on the ceilings. For Actellic, the mean mortality over the nine-month period was 88.5% [87.7, 89.3] for fully sprayed huts, 88.3% [85.1, 91.4] for bottom half + ceiling sprayed walls and 80.8% [74.5, 87.1] for the top half + ceiling sprayed huts. For Fludora Fusion an overall mean mortality of 85.6% [81.5, 89.7] was recorded for fully sprayed huts, 83.7% [82.9, 84.5] for bottom half + ceiling sprayed huts and 81.3% [79.6, 83.0] for the top half + ceiling sprayed huts. For SumiShield, the overall mean mortality was 86.7% [85.3, 88.1] for fully sprayed huts, 85.6% [85.4, 85.8] for the bottom half + ceiling sprayed huts and 76.9% [76.6, 77.3] for the top half + ceiling sprayed huts. For Fludora Fusion, both iterations of partial IRS were non-inferior to full spraying. However, for SumiShield and Actellic, this was true only for the huts with the bottom half + ceiling, reflecting the resting site preference of the local vectors. The results of this study suggest that partial spraying may be a way to reduce the cost of IRS without substantially compromising IRS efficacy.

The emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2500 COVID-19 cases associated with this variant have been detected in the US since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight the primary ports of entry for B.1.1.7 in the US and locations of possible underreporting of B.1.1.7 cases. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.

BACKGROUND: Clinical trials initiated during emerging infectious disease outbreaks must quickly enroll participants to identify treatments to reduce morbidity and mortality. This may be at odds with enrolling a representative study population, especially when the population affected is undefined. METHODS: We evaluated the utility of the Centers for Disease Control and Prevention's COVID-19-Associated Hospitalization Surveillance Network (COVID-NET), the COVID-19 Case Surveillance System (CCSS), and 2020 United States (US) Census data to determine demographic representation in the 4 stages of the Adaptive COVID-19 Treatment Trial (ACTT). We compared the cumulative proportion of participants by sex, race, ethnicity, and age enrolled at US ACTT sites, with respective 95% confidence intervals, to the reference data in forest plots. RESULTS: US ACTT sites enrolled 3509 adults hospitalized with COVID-19. When compared with COVID-NET, ACTT enrolled a similar or higher proportion of Hispanic/Latino and White participants depending on the stage, and a similar proportion of African American participants in all stages. In contrast, ACTT enrolled a higher proportion of these groups when compared with US Census and CCSS. The proportion of participants aged ≥65 years was either similar or lower than COVID-NET and higher than CCSS and the US Census. The proportion of females enrolled in ACTT was lower than the proportion of females in the reference datasets. CONCLUSIONS: Although surveillance data of hospitalized cases may not be available early in an outbreak, they are a better comparator than US Census data and surveillance of all cases, which may not reflect the population affected and at higher risk of severe disease.

Rapid environmental change in Alaska and other regions of the Arctic and sub-Arctic has raised concerns about increasing human exposure to ticks and the pathogens they carry. We tested a sample of ticks collected through a combination of passive and active surveillance from humans, domestic animals, and wildlife hosts in Alaska for a panel of the most common tick-borne pathogens in the contiguous United States to characterize the diversity of microbes present in this region. We tested 189 pooled tick samples collected in 2019-2020 for Borrelia spp., Anaplasma spp., Ehrlichia spp., and Babesia spp. using a multiplex PCR amplicon sequencing assay. We found established populations of Ixodes angustus Neumann (Acari: Ixodidae), Ixodes uriae White (Acari: Ixodidae), and Haemaphysalis leporispalustris Packard (Acari: Ixodidae) in Alaska, with I. angustus found on a variety of hosts including domestic companion animals (dogs and cats), small wild mammals, and humans. Ixodes angustus were active from April through October with peaks in adult and nymphal activity observed in summer months (mainly July). Although no known human pathogens were detected, Babesia microti-like parasites and candidatus Ehrlichia khabarensis were identified in ticks and small mammals. The only human pathogen detected (B. burgdorferi s.s.) was found in a tick associated with a dog that had recently traveled to New York, where Lyme disease is endemic. This study highlights the value of a combined passive and active tick surveillance system to detect introduced tick species and pathogens and to assess which tick species and microbes are locally established.

OBJECTIVE: To assess whether knowledge of Tuskegee, the U.S. Immigration and Customs Enforcement (ICE) agency's detainment of children, and satisfaction with the George Floyd death investigation were associated with trust in actors involved in the development and distribution of coronavirus vaccines. DATA SOURCES AND STUDY SETTING: National survey with a convenience sample of Black (n = 1019) and Hispanic (n = 994) adults between July 1 and 26, 2021. STUDY DESIGN: Observational study using stratified adjusted logistic regression models to measure the association between ratings of the trustworthiness of actors involved in the development and distribution of coronavirus vaccines. PRINCIPAL FINDINGS: Among Black respondents, lower satisfaction with the George Floyd death investigation was associated with lower trustworthiness ratings of pharmaceutical companies (ME: -0.09; CI: -0.15, 0.02), the FDA (ME: -0.07; CI: -0.14, -0.00), the Trump Administration (ME: -0.09; CI: -0.16, -0.02), the Biden Administration (ME: -0.07, CI: -0.10, 0.04), and elected officials (ME: -0.10, CI: -0.18, -0.03). Among Hispanic respondents, lower satisfaction was associated with lower trustworthiness ratings of the Trump Administration (ME: -0.14, CI: -0.22, -0.06) and elected officials (ME: -0.11; CI: -0.19, -0.02). Greater knowledge of ICE's detainment of children and families among Hispanic respondents was associated with lower trustworthiness ratings of state elected officials (ME: -0.09, CI: -0.16, 0.01). Greater knowledge of the US Public Health Service Study of Syphilis in Tuskegee was associated with higher trustworthiness ratings of their usual source of care (ME: 0.09; CI: 0.28, 0.15) among Black respondents (ME: 0.09; CI: 0.01, 0.16). CONCLUSIONS: Among Black respondents, lower satisfaction with the George Floyd death investigation was associated with lowered levels of trust in pharmaceutical companies, some government officials, and administrators; it was not associated with the erosion of trust in direct sources of health care delivery, information, or regulation. Among Hispanic respondents, greater knowledge of the ICE detainments was associated with lower trustworthiness ratings of elected state officials. Paradoxically, higher knowledge of the Study of Syphilis in Tuskegee was associated with higher trustworthiness ratings in usual sources of care.

BACKGROUND: There are large and persistent disparities in life expectancy among racial-ethnic groups in the USA, but the extent to which these patterns vary geographically on a local scale is not well understood. This analysis estimated life expectancy for five racial-ethnic groups, in 3110 US counties over 20 years, to describe spatial-temporal variations in life expectancy and disparities between racial-ethnic groups. METHODS: We applied novel small-area estimation models to death registration data from the US National Vital Statistics System and population data from the US National Center for Health Statistics to estimate annual sex-specific and age-specific mortality rates stratified by county and racial-ethnic group (non-Latino and non-Hispanic White [White], non-Latino and non-Hispanic Black [Black], non-Latino and non-Hispanic American Indian or Alaska Native [AIAN], non-Latino and non-Hispanic Asian or Pacific Islander [API], and Latino or Hispanic [Latino]) from 2000 to 2019. We adjusted these mortality rates to correct for misreporting of race and ethnicity on death certificates and then constructed abridged life tables to estimate life expectancy at birth. FINDINGS: Between 2000 and 2019, trends in life expectancy differed among racial-ethnic groups and among counties. Nationally, there was an increase in life expectancy for people who were Black (change 3·9 years [95% uncertainty interval 3·8 to 4·0]; life expectancy in 2019 75·3 years [75·2 to 75·4]), API (2·9 years [2·7 to 3·0]; 85·7 years [85·3 to 86·0]), Latino (2·7 years [2·6 to 2·8]; 82·2 years [82·0 to 82·5]), and White (1·7 years [1·6 to 1·7]; 78·9 years [78·9 to 79·0]), but remained the same for the AIAN population (0·0 years [-0·3 to 0·4]; 73·1 years [71·5 to 74·8]). At the national level, the negative difference in life expectancy for the Black population compared with the White population decreased during this period, whereas the negative difference for the AIAN population compared with the White population increased; in both cases, these patterns were widespread among counties. The positive difference in life expectancy for the API and Latino populations compared with the White population increased at the national level from 2000 to 2019; however, this difference declined in a sizeable minority of counties (615 [42·0%] of 1465 counties) for the Latino population and in most counties (401 [60·2%] of 666 counties) for the API population. For all racial-ethnic groups, improvements in life expectancy were more widespread across counties and larger from 2000 to 2010 than from 2010 to 2019. INTERPRETATION: Disparities in life expectancy among racial-ethnic groups are widespread and enduring. Local-level data are crucial to address the root causes of poor health and early death among disadvantaged groups in the USA, eliminate health disparities, and increase longevity for all. FUNDING: National Institute on Minority Health and Health Disparities; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Office of Disease Prevention; and Office of Behavioral and Social Science Research, US National Institutes of Health.

The fourth author’s name is incorrect. The correct name is Lawrence C. Brody. The correct citation is: Khoury MJ, Feero WG, Chambers DA, Brody LC, Aziz N, Green RC, et al. (2018) A collaborative translational research framework for evaluating and implementing the appropriate use of human genome sequencing to improve health. PLoS Med 15(8): e1002631. https://doi.org/10.1371/journal.pmed.1002631.

The authors reply: Weng et al. question the clinical benefit of remdesivir treatment. In our article, we noted that the decision to administer remdesivir for compassionate use was based on the patient’s worsening clinical status. No inferences are possible from the uncontrolled treatment of one patient, and we stated, “randomized, controlled trials are needed to determine the safety and efficacy of remdesivir and any other investigational agents for treatment of patients with 2019-nCoV infection.” | | Tsung notes that an increase in lymphocyte counts and subsequent clinical improvement are consistent with activation of the adaptive immune response and resolution of SARS-CoV-2 infection. IgM and IgA antibodies may be detectable early in the clinical course, and IgG antibodies can be detected a median of 14 days after the onset of illness.1 We agree that the adaptive immune response contributes to clinical recovery and clearance of SARS-CoV-2, although one study showed that seroconversion was not correlated with a rapid decline in the SARS-CoV-2 load.2 In another study that showed a good correlation between IgG and neutralizing antibody titers, an increase in IgG antibody levels was correlated with a decrease in the viral load between 1 and 3 weeks after the onset of illness, but SARS-CoV-2 RNA was still detectable for prolonged periods.3 | | Zhang inquires about detection of SARS-CoV-2 in stool and urine specimens after remdesivir treatment. In our patient, although a stool specimen collected on day 7 of illness was positive with high cycle threshold values (36 to 38) that were consistent with detection of viral RNA and probably not infectious virus, a stool specimen obtained from the patient on day 14 of illness was negative. SARS-CoV-2 RNA was not detected in urine specimens; these findings are consistent with those in a larger study.4 | | Wen et al. and Link and Hold raise the issue of fecal–oral transmission of SARS-CoV-2. Diarrhea has been reported to occur in patients with Covid-19, and it can precede the development of respiratory symptoms and progression to pneumonia. SARS-CoV-2 RNA has been detected in stool specimens, and recovery of live infectious virus from stool has been reported.4 Further studies are needed to understand the implications of SARS-CoV-2 detected in stool for transmission of the virus. | | Ren et al. argue that high-resolution low-dose chest CT should be performed instead of chest radiography in persons with fever and suspected Covid-19. The Centers for Disease Control and Prevention recommends collection of nasopharyngeal swab specimens and lower respiratory specimens, if available, for SARS-CoV-2 testing and prioritizes testing of hospitalized patients and symptomatic health care workers. Furthermore, the American College of Radiology has noted concerns regarding prevention and control of SARS-CoV-2 transmission in health care facilities, including transmission that may occur with the use of CT scanners, and has recommended that CT should not be used to screen for or diagnose Covid-19.5

In the 2 years since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) there has been an unprecedented collective effort from the academic, public, and private sectors to advance surveillance for pandemic preparedness and response. The coronavirus disease (COVID-19) pandemic has created momentum that will define the future of public health intelligence. On 2 and 3 February 2022, the World Health Organization (WHO) Hub for Pandemic and Epidemic Intelligence convened a meeting of a small group of surveillance innovators to share insights and approaches about their initiatives and future directions. The meeting served as an opportunity for participants to share updates about their work, to explore potential for collaboration, exchange ideas, cross-fertilise our work and discuss challenges in the field of surveillance. Although the group of attendees was not geographically representative of the global surveillance community, the meeting was the first in a planned series of exchanges convened by the WHO Pandemic Hub that will generate dialogue among global thought leaders and new voices in the surveillance community. In this first convening we discussed several themes, including what is meaningful collaboration for success; how to bring the public back into public health; what are individual-centred approaches; how new kinds of data have new privacy concerns; how government structures affect the functioning of surveillance systems; how to inform the decisionmaking process; cross-scaling and down-scaling tools and technologies; investing in human talent and future practitioners; and achieving sustainability into surveillance. In this meeting report, we summarise the discussions on innovations in public health surveillance and provide a list with references and links to the organisations and initiatives represented at the meeting.