Data from the National Immunization Survey-Child (NIS-Child) were analyzed to estimate coverage with childhood vaccines recommended by the Advisory Committee on Immunization Practices among U.S. children by age 24 months. Coverage with nearly all vaccines was lower among children born in 2020 and 2021 than it was among those born in 2018 and 2019, with declines ranging from 1.3 to 7.8 percentage points. Analyses of NIS-Child data for earlier birth cohorts have not revealed such widespread declines in routine childhood vaccination coverage. Coverage among children born during 2020-2021 varied by race and ethnicity, health insurance status, poverty status, urbanicity, and jurisdiction. Compared with non-Hispanic White children, coverage with four of the 17 vaccine measures was lower among non-Hispanic Black or African American children as well as Hispanic or Latino (Hispanic) and non-Hispanic American Indian or Alaska Native children. Coverage was also generally lower among those covered by Medicaid or other nonprivate insurance, uninsured children, children living below the federal poverty level, and children living in rural areas. Coverage varied widely by jurisdiction, especially coverage with ≥2 doses of influenza vaccine. Children born during 2020-2021 were born during or after the period of major disruption of primary care from the COVID-19 pandemic. Providers should review children's histories and recommend needed vaccinations during every clinical encounter. Addressing financial barriers, access issues, vaccine hesitancy, and vaccine-related misinformation can also help to increase coverage, reduce disparities, and protect all children from vaccine-preventable diseases. Strategies that have been found effective include implementation of standing orders and reminder and recall systems, strong physician recommendations to vaccinate, and use of immunization information systems to identify areas of lower coverage that could benefit from targeted interventions to increase immunization rates.

BACKGROUND: In previous publications, the Task Force on Reference Measurement System Implementation proposed a procedural approach combining a critical review of entries available in the Joint Committee on Traceability in Laboratory Medicine (JCTLM) database with a comparison of this information against analytical performance specifications for measurement uncertainty (MU) and applied it to a group of 13 measurands. CONTENT: Here we applied this approach to 17 additional measurands, of which measurements are frequently requested. The aims of the study were (a) to describe the main characteristics for implementing traceability and the potential to fulfill the maximum allowable MU (MAU) at the clinical sample level of certified reference materials and reference measurement procedures listed in the JCTLM database; (b) to discuss limitations and obstacles, if any, to the achievement of the required quality of laboratory measurements; and (c) to provide a gap analysis by highlighting what is still missing in the database. Results were integrated with those obtained in the previous study, therefore offering an overview of where we are and what is still missing in the practical application of the metrological traceability concept to 30 common biochemical tests employed in laboratory medicine. SUMMARY: Our analysis shows that for 28 out of 30 measurands, conditions exist to correctly implement metrological traceability to the International System of units and fulfill at least the MAU of the minimum quality level derived according to internationally recommended models. For 2 measurands (serum albumin and chloride), further improvements in MU of higher-order references would be necessary.

INTRODUCTION: The Vaccines for Children (VFC) program was established in 1994 to provide recommended vaccines at no cost to eligible children and help ensure that all U.S. children are protected from life-threatening vaccine-preventable diseases. METHODS: CDC analyzed data from the 2012-2022 National Immunization Survey-Child (NIS-Child) to assess trends in vaccination coverage with ≥1 dose of measles, mumps, and rubella vaccine (MMR), 2-3 doses of rotavirus vaccine, and a combined 7-vaccine series, by VFC program eligibility status, and to examine differences in coverage among VFC-eligible children by sociodemographic characteristics. VFC eligibility was defined as meeting at least one of the following criteria: 1) American Indian or Alaska Native; 2) insured by Medicaid, Indian Health Service (IHS), or uninsured; or 3) ever received at least one vaccination at an IHS-operated center, Tribal health center, or urban Indian health care facility. RESULTS: Overall, approximately 52.2% of U.S. children were VFC eligible. Among VFC-eligible children born during 2011-2020, coverage by age 24 months was stable for ≥1 MMR dose (88.0%-89.9%) and the combined 7-vaccine series (61.4%-65.3%). Rotavirus vaccination coverage by age 8 months was 64.8%-71.1%, increasing by an average of 0.7 percentage points annually. Among all children born in 2020, coverage was 3.8 (≥1 MMR dose), 11.5 (2-3 doses of rotavirus vaccine), and 13.8 (combined 7-vaccine series) percentage points lower among VFC-eligible than among non-VFC-eligible children. CONCLUSIONS AND IMPLICATIONS FOR PUBLIC HEALTH PRACTICE: Although the VFC program has played a vital role in increasing and maintaining high levels of childhood vaccination coverage for 30 years, gaps remain. Enhanced efforts must ensure that parents and guardians of VFC-eligible children are aware of, have confidence in, and are able to obtain all recommended vaccines for their children.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble is the 2(nd) most accurate model measured by WIS in 2021-22 and the 5(th) most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved into numerous lineages with unique spike mutations and caused multiple epidemics domestically and globally. Although COVID-19 vaccines are available, new variants with the capacity for immune evasion continue to emerge. To understand and characterize the evolution of circulating SARS-CoV-2 variants in the U.S., the Centers for Disease Control and Prevention (CDC) initiated the National SARS-CoV-2 Strain Surveillance (NS3) program and has received thousands of SARS-CoV-2 clinical specimens from across the nation as part of a genotype to phenotype characterization process. Focus reduction neutralization with various antisera was used to antigenically characterize 143 SARS-CoV-2 Delta, Mu and Omicron subvariants from selected clinical specimens received between May 2021 and February 2023, representing a total of 59 unique spike protein sequences. BA.4/5 subvariants BU.1, BQ.1.1, CR.1.1, CQ.2 and BA.4/5 + D420N + K444T; BA.2.75 subvariants BM.4.1.1, BA.2.75.2, CV.1; and recombinant Omicron variants XBF, XBB.1, XBB.1.5 showed the greatest escape from neutralizing antibodies when analyzed against post third-dose original monovalent vaccinee sera. Post fourth-dose bivalent vaccinee sera provided better protection against those subvariants, but substantial reductions in neutralization titers were still observed, especially among BA.4/5 subvariants with both an N-terminal domain (NTD) deletion and receptor binding domain (RBD) substitutions K444M + N460K and recombinant Omicron variants. This analysis demonstrated a framework for long-term systematic genotype to antigenic characterization of circulating and emerging SARS-CoV-2 variants in the U.S., which is critical to assessing their potential impact on the effectiveness of current vaccines and antigen recommendations for future updates.

BACKGROUND: Vaccine hesitancy (VH) has been a major contributor to large outbreaks of vaccine-preventable diseases globally, including in the United States. METHODS: Data from the 2019-2022 National Immunization Surveys were analyzed to assess parental hesitancy toward routine vaccination of their children aged 6 months -17 years. Joinpoint regression was employed to investigate trends in VH from 2019 to 2022 nationally overall and among socio-demographic subgroups. Using logistic regression, the difference between the prevalence of VH before and after the authorization of the COVID-19 vaccine for children aged 6 months-4 years, 5-11 years, and 12-17 years was computed. Both unadjusted and adjusted estimates were reported. VH was also compared within each socio-demographic subgroup with a reference level, at two-time points- before and after the authorization of the COVID-19 vaccine for each age group. RESULTS: Overall, VH remained around 19.0 % from Q2 2019 to Q3 2022. Parents of non-Hispanic Black children had the largest average quarterly decrease in VH (β = -0.55; p < 0.05 by test for trend). After the authorization of the COVID-19 vaccine for children aged 6 months to 4 years, the adjusted percentage of children having parents that reported VH decreased by 2.2 (95 % CI: -3.9, -0.6) percentage points (pp) from 21.6 % to 19.4 %. Conversely, for children aged 5-11 years, VH increased by 1.2 (95 % CI: 0.2, 2.3) pp, from 19.8 % to 21.0 %. VH among parents of non-Hispanic Black children decreased after the authorization of the COVID-19 vaccine for adolescents aged 12-17 years but remained significantly higher compared to parents of non-Hispanic White children before and after authorization of the COVID-19 vaccine for all age groups. DISCUSSION: About 1 in 5 children had parents reporting VH from 2019 to 2022. Parental VH increased after the authorization of the COVID-19 vaccine for children aged 5-11 years and declined for children aged 6 months-4 years.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.

BACKGROUND: Limited diagnostic capabilities, resources and health worker skills have deterred the advancement of birth defects surveillance systems in most low- and middle-income countries (LMICs). Empowering health workers to identify and diagnose major external birth defects (BDs) is crucial to establishing effective hospital-based BD surveillance. Makerere University-Johns Hopkins University (MU-JHU) Research Collaboration BD Surveillance System consists of three diagnostic levels: (1) surveillance midwives, (2) MU-JHU clinical team, and (3) U.S. Centers for Disease Control and Prevention (CDC) birth defects subject matter experts (SMEs) who provide confirmatory diagnosis. The diagnostic concordance of major external BDs by surveillance midwives or MU-JHU clinical team with CDC birth defects SMEs were estimated. METHODS: Study staff went through a series of trainings, including birth defects identification and confirmation, before surveillance activities were implemented. To assess the diagnostic concordance, we analyzed surveillance data from 2015 to 2021 for major external BDs: anencephaly, iniencephaly, encephalocele, spina bifida, craniorachischisis, microcephaly, anophthalmia/microphthalmia, anotia/microtia, cleft palate alone, cleft lip alone, cleft lip with cleft palate, imperforate anus, hypospadias, talipes equinovarus, limb reduction, gastroschisis, and omphalocele. Positive predictive value (PPV) as the proportion of BDs diagnosed by surveillance midwives or MU-JHU clinical team that were confirmed by CDC birth defects SMEs was computed. PPVs between 2015 and 2018 and 2019-2021 were compared to assess the accuracy of case diagnosis over time. RESULTS: Of the 204,332 infants examined during 2015-2021, 870 infants had a BD. Among the 1,245 BDs identified, 1,232 (99.0%) were confirmed by CDC birth defects SMEs. For surveillance midwives, PPV for 7 of 17 BDs was > 80%. For the MU-JHU clinical team, PPV for 13 of 17 BDs was > 80%. Among surveillance midwives, PPV improved significantly from 2015 to 2018 to 2019-2021, for microcephaly (+ 50.0%), cleft lip with cleft palate (+ 17.0%), imperforate anus (+ 30.0%), and talipes equinovarus (+ 10.8%). Improvements in PPV were also observed among MU-JHU clinical team; however, none were significant. CONCLUSION: The diagnostic accuracy of the midwives and clinical team increased, highlighting that BD surveillance, by front-line health care workers (midwives) in LMICs is possible when midwives receive comprehensive training, technical support, funding and continuous professional development.

Most human papillomavirus (HPV) surveillance studies target 30-50 of the more than 200 known types. We applied our recently described enriched whole-genome sequencing (eWGS) assay to demonstrate the impact of detecting all known and novel HPV types in male genital samples (n = 50). HPV was detected in nearly all (82%) samples, (mean number of types/samples 13.6; range 1-85), and nearly all HPV-positive samples included types in multiple genera (88%). A total of 560 HPV detections (237 unique HPV types: 46 alpha, 55 beta, 135 gamma, and 1 mu types) were made. The most frequently detected HPV types were alpha (HPV90, 43, and 74), beta (HPV115, 195, and 120), and gamma (HPV134, mSD2, and HPV50). High-risk alpha types (HPV16, 18, 31, 39, 52, and 58) were not common. A novel gamma type was identified (now officially HPV229) along with 90 unclassified types. This pilot study demonstrates the utility of the eWGS assay for broad-spectrum type detection and suggests a significantly higher type diversity in males compared to females that warrants further study.

Background Knowledge of expected rates of potential adverse events of special interest (AESI) that may occur coincidentally following COVID-19 vaccination is essential for vaccine safety surveillance and assessment. We calculated the expected rates of 21 potential AESI following COVID-19 vaccination among vaccinated persons within 1 day, 7 days, and 42 days of vaccination.Methods We used meta-analytic methods to estimate background rates of 21 medical conditions considered potential AESI and calculated expected rates of each potential AESI within 1 day, 7 days, and 42 days of vaccination.Results Background rates of three commonly monitored AESI, Guillain-Barre syndrome (GBS), myopericarditis, and all-cause deaths were 2.0 GBS cases/100,000 person-years, 1.3 myopericarditis cases/100,000 person-years, and 863.8 all-cause deaths/100,000 person-years, respectively. Based on these background rates, if 10,000,000 persons are vaccinated, we would expect 0.5, 3.7, and 22.5 GBS cases; 0.3, 2.4, and 14.3 myopericarditis cases; and 236.5, 1655.5, and 9932.8 all-cause deaths to occur in coincident temporal association (i.e., as a result of background incidence) within 1 day, 7 days, and 42 days of vaccination, respectively.Conclusion Knowledge of expected rates of potential AESI can help contextualize adverse health events associated temporally with immunization, aid in safety signal detection, guide COVID-19 vaccine public health communication, and inform benefit-risk assessments of COVID-19 vaccines.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThere are no funding sources for this study.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 Institutional Review Board review.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.YesWe conducted a meta-analysis using incidence rate data from eligible published studies cited in this paper.

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&amp; 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. &amp; Charlene Zalesky, and Claudia &amp; 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 &amp; 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, &amp; 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

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

WHAT IS ALREADY KNOWN ABOUT THIS TOPIC? The highly transmissible SARS-CoV-2 Delta variant has begun to cause increases in cases, hospitalizations, and deaths in parts of the United States. With slowed vaccination uptake, this novel variant is expected to increase the risk of pandemic resurgence in the US in July-December 2021. WHAT IS ADDED BY THIS REPORT? Data from nine mechanistic models project substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant. These resurgences, which have now been observed in most states, were projected to occur across most of the US, coinciding with school and business reopening. Reaching higher vaccine coverage in July-December 2021 reduces the size and duration of the projected resurgence substantially. The expected impact of the outbreak is largely concentrated in a subset of states with lower vaccination coverage. WHAT ARE THE IMPLICATIONS FOR PUBLIC HEALTH PRACTICE? Renewed efforts to increase vaccination uptake are critical to limiting transmission and disease, particularly in states with lower current vaccination coverage. Reaching higher vaccination goals in the coming months can potentially avert 1.5 million cases and 21,000 deaths and improve the ability to safely resume social contacts, and educational and business activities. Continued or renewed non-pharmaceutical interventions, including masking, can also help limit transmission, particularly as schools and businesses reopen.

Background SARS-CoV-2 vaccination of persons aged 12 years and older has reduced disease burden in the United States. The COVID-19 Scenario Modeling Hub convened multiple modeling teams in September 2021 to project the impact of expanding vaccine administration to children 5-11 years old on anticipated COVID-19 burden and resilience against variant strains. Methods Nine modeling teams contributed state- and national-level projections for weekly counts of cases, hospitalizations, and deaths in the United States for the period September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of: 1) presence vs. absence of vaccination of children ages 5-11 years starting on November 1, 2021; and 2) continued dominance of the Delta variant vs. emergence of a hypothetical more transmissible variant on November 15, 2021. Individual team projections were combined using linear pooling. The effect of childhood vaccination on overall and age-specific outcomes was estimated by meta-analysis approaches. Findings Absent a new variant, COVID-19 cases, hospitalizations, and deaths among all ages were projected to decrease nationally through mid-March 2022. Under a set of specific assumptions, models projected that vaccination of children 5-11 years old was associated with reductions in all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios where children were not vaccinated. This projected effect of vaccinating children 5-11 years old increased in the presence of a more transmissible variant, assuming no change in vaccine effectiveness by variant. Larger relative reductions in cumulative cases, hospitalizations, and deaths were observed for children than for the entire U.S. population. Substantial state-level variation was projected in epidemic trajectories, vaccine benefits, and variant impacts. Conclusions Results from this multi-model aggregation study suggest that, under a specific set of scenario assumptions, expanding vaccination to children 5-11 years old would provide measurable direct benefits to this age group and indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Some racial and ethnic groups are at increased risk for COVID-19 and associated hospitalization and death because of systemic and structural inequities contributing to higher prevalences of high-risk conditions and increased exposure (1). Vaccination is the most effective prevention intervention against COVID-19-related morbidity and mortality*; ensuring more equitable vaccine access is a public health priority. Differences in adult COVID-19 vaccination coverage by race and ethnicity have been previously reported (2,3), but similar information for children and adolescents is limited (4,5). CDC analyzed data from the National Immunization Survey-Child COVID Module (NIS-CCM) to describe racial and ethnic differences in vaccination status, parental intent to vaccinate their child, and behavioral and social drivers of vaccination among children and adolescents aged 5-17 years. By August 31, 2022, approximately one third (33.2%) of children aged 5-11 years, more than one half (59.0%) of children and adolescents aged 12-15 years, and more than two thirds (68.6%) of adolescents aged 16-17 years had received ≥1 COVID-19 vaccine dose. Vaccination coverage was highest among non-Hispanic Asian (Asian) children and adolescents, ranging from 63.4% among those aged 5-11 years to 91.8% among those aged 16-17 years. Coverage was next highest among Hispanic or Latino (Hispanic) children and adolescents (34.5%-77.3%). Coverage was similar for non-Hispanic Black or African American (Black), non-Hispanic White (White), and non-Hispanic other race(†) or multiple race (other/multiple race) children and adolescents aged 12-15 and 16-17 years. Among children aged 5-11 years, coverage among Black children was lower than that among Hispanic, Asian, and other/multiple race children. Enhanced public health efforts are needed to increase COVID-19 vaccination coverage for all children and adolescents. To address disparities in child and adolescent COVID-19 vaccination coverage, vaccination providers and trusted messengers should provide culturally relevant information and vaccine recommendations and build a higher level of trust among those groups with lower coverage.

BACKGROUND: The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5-11 years on COVID-19 burden and resilience against variant strains. METHODS: Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5-11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses. FINDINGS: Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5-11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed. INTERPRETATION: Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5-11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. FUNDING: Various (see acknowledgments).

Understanding the distribution of pathogens causing acute febrile illness (AFI) is important for clinical management of patients in resource-poor settings. We evaluated the proportion of AFI caused by specific pathogens among outpatients in Bangladesh. During May 2019-March 2020, physicians screened patients aged 2 years in outpatient departments of four tertiary level public hospitals. We randomly enrolled patients having measured fever (100.4F) during assessment with onset within the past 14 days. Blood and urine samples were tested at icddr,b through rapid diagnostic tests, bacterial culture, and polymerase chain reaction (PCR). Acute and convalescent samples were sent to the Centers for Disease Control and Prevention (USA) for Rickettsia and Orientia (R/O) and Leptospira tests. Among 690 patients, 69 (10%) had enteric fever (Salmonella enterica serotype Typhi orSalmonella enterica serotype Paratyphi), 51 (7.4%) Escherichia coli, and 28 (4.1%) dengue detected. Of the 441 patients tested for R/O, 39 (8.8%) had rickettsioses. We found 7 (2%) Leptospira cases among the 403 AFI patients tested. Nine patients (1%) were hospitalized, and none died. The highest proportion of enteric fever (15%, 36/231) and rickettsioses (14%, 25/182) was in Rajshahi. Dhaka had the most dengue cases (68%, 19/28). R/O affected older children and young adults (IQR 8-23 years) and was detected more frequently in the 21-25 years age-group (17%, 12/70). R/O was more likely to be found in patients in Rajshahi region than in Sylhet (aOR 2.49, 95% CI 0.85-7.32) between July and December (aOR 2.01, 1.01-5.23), and who had a history of recent animal entry inside their house than not (aOR 2.0, 0.93-4.3). Gram-negative Enterobacteriaceae were the most common bacterial infections, and dengue was the most common viral infection among AFI patients in Bangladeshi hospitals, though there was geographic variability. These results can help guide empiric outpatient AFI management.

The evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in the emergence of new variant lineages that have exacerbated the COVID-19 pandemic. Some of those variants were designated as variants of concern/interest (VOC/VOI) by national or international authorities based on many factors including their potential impact on vaccine-mediated protection from disease. To ascertain and rank the risk of VOCs and VOIs, we analyze the ability of 14 variants (614G, Alpha, Beta, Gamma, Delta, Epsilon, Zeta, Eta, Theta, Iota, Kappa, Lambda, Mu, and Omicron) to escape from mRNA vaccine-induced antibodies. The variants show differential reductions in neutralization and replication by post-vaccination sera. Although the Omicron variant (BA.1, BA.1.1, and BA.2) shows the most escape from neutralization, sera collected after a third dose of vaccine (booster sera) retain moderate neutralizing activity against that variant. Therefore, vaccination remains an effective strategy during the COVID-19 pandemic.

In Spring 2021, the highly transmissible SARS-CoV-2 Delta variant began to cause increases in cases, hospitalizations, and deaths in parts of the United States. At the time, with slowed vaccination uptake, this novel variant was expected to increase the risk of pandemic resurgence in the US in summer and fall 2021. As part of the COVID-10 Scenario Modeling Hub, an ensemble of nine mechanistic models produced six-month scenario projections for July-December 2021 for the United States. These projections estimated substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant, projected to occur across most of the US, coinciding with school and business reopening. The scenarios revealed that reaching higher vaccine coverage in July-December 2021 reduced the size and duration of the projected resurgence substantially, with the expected impacts was largely concentrated in a subset of states with lower vaccination coverage. Despite accurate projection of COVID-19 surges occurring and timing, the magnitude was substantially underestimated 2021 by the models compared with the of the reported cases, hospitalizations, and deaths occurring during July-December, highlighting the continued challenges to predict the evolving COVID-19 pandemic. Vaccination uptake remains critical to limiting transmission and disease, particularly in states with lower vaccination coverage. Higher vaccination goals at the onset of the surge of the new variant were estimated to avert over 1.5 million cases and 21,000 deaths, though may have had even greater impacts, considering the underestimated resurgence magnitude from the model.

SignificanceThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

OBJECTIVE: There is a need for reliable serological assays to determine accurate estimates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence. Most single target antigen assays have shown some limitations in Africa. To assess the performance of a multi-antigen assay, we evaluated a commercially available SARS-CoV-2 Multi-Antigen IgG assay for human coronavirus disease 2019 (COVID-19) in Nigeria. METHODS: Validation of the xMAP SARS-CoV-2 Multi-Antigen IgG assay was carried out using well-characterized SARS-CoV-2 reverse transcription polymerase chain reactive positive (97) and pre-COVID-19 pandemic (86) plasma panels. Cross-reactivity was assessed using pre-COVID-19 pandemic plasma specimens (213) from the 2018 Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS). RESULTS: The overall sensitivity of the xMAP SARS-CoV-2 Multi-Antigen IgG assay was 75.3% [95% CI: 65.8%- 82.8%] and specificity was 99.0% [95% CI: 96.8%- 99.7%]. The sensitivity estimate increased to 83.3% [95% CI: 70.4%- 91.3%] for specimens >14 days post-confirmation of diagnosis. However, using the NAIIS pre-pandemic specimens, the false positivity rate was 1.4% (3/213). CONCLUSIONS: Our results showed overall lower sensitivity and a comparable specificity with the manufacturer's validation. There appears to be less cross-reactivity with NAIIS pre-pandemic COVID-19 specimens using the xMAP SARS-CoV-2 Multi-Antigen IgG assay. In-country SARS-CoV-2 serology assay validation can help guide the best choice of assays in Africa.

Through active surveillance and contact tracing from outpatients, we aimed to identify and characterize SARS-CoV-2 variants circulating in Porto Velho, Rondnia a city in the Brazilian Amazon. As part of a prospective cohort, we gather information from 2,506 individuals among COVID-19 patients and household contacts. Epidemiological data, nasopharyngeal swabs, and blood samples were collected from all participants. Nasopharyngeal swabs were tested for antigen rapid diagnostic test and reverse transcription polymerase chain reaction (RT-PCR) followed by genomic sequencing. Blood samples underwent ELISA testing for IgA, IgG and IgM antibody levels. From 757 specimens sequenced, three were identified as Mu variant, none of the individuals carrying this variant had travel history in the previous 15 days before diagnosis. One case was asymptomatic and two presented mild symptoms. Two infected individuals from different household caring virus with additional amino acid substitutions ORF7a P45L and ORF1a T1055A compared to the Mu virus reference sequence. One patient presented IgG levels. Our results highlight that genomic surveillance for SARS-CoV-2 variants can assist in detecting the emergency of SARS-CoV-2 variants in the community, prior to its identification in other parts of the country. This article is protected by copyright. All rights reserved.

Using meta-analytic methods, we calculated expected rates of 21 potential adverse events of special interest (AESI) that would occur following COVID-19 vaccination within 1-, 7-, and 42-day intervals without causal associations. Based on these expected rates, if 10,000,000 persons are vaccinated, 0.5, 3.7, and 22.5 Guillain-Barre syndrome cases; 0.3, 2.4, and 14.3 myopericarditis cases; and 236.5, 1655.5, and 9932.8 all-cause deaths would occur coincidentally within 1, 7, and 42 days post-vaccination, respectively. Expected rates of potential AESI can contextualize events associated temporally with immunization, aid in safety signal detection, guide COVID-19 vaccine health communications, and inform COVID-19 vaccine benefit-risk assessments.

This case study is part of a series centered on the Centers for Disease Control and Prevention's National Healthcare Safety Network's (NHSN) health care-associated infection (HAI) surveillance definitions. This is the first analytic case study published in AJIC since the CDC/ NHSN updated its HAI risk adjustment models and rebaselined the standardized infection ratios (SIRs) in 2015. This case describes a scenario that Infection Preventionists (IPs) have encountered during their analysis of surgical site infection (SSI) surveillance data. The case study is intended to illustrate how specific models can impact the SIR results by highlighting differences in the criteria for NHSN's older and newer risk models: the original versions and the updated models introduced in 2015. Understanding these differences provides insight into how SSI SIR calculations differ between the older and newer NHSN baseline models. NHSN plans to produce another set of HAI risk adjustment models in the future, using newer HAI incidence and risk factor data. While the timetable for these changes remains to be determined, the statistical methods used to produce future models and SIR calculations will continue the precedents that NHSN has established. An online survey link is provided where participants may confidentially answer questions related to the case study and receive immediate feedback in the form of correct answers, explanations, rationales, and summary of teaching points. Details of the case study, answers, and explanations have been reviewed and approved by NHSN staff. We hope that participants take advantage of this educational offering and thereby gain a greater understanding of the NHSN's HAI data analysis. There are 2 baselines available for SSI standardized infection ration (SIRs) in the National Healthcare Safety Network (NHSN); one based on the 2006-2008 national aggregate data and another based on the 2015 data. Each of the 2 baselines has a different set of inclusion criteria for the SSI data, which impact the calculation of the SIR. In this case study, we focused on the impact of the inclusion of PATOS in the calculation of the 2006-2008 baseline SSI SIR and the exclusion of PATOS from the calculation of the 2015 baseline SSI SIR. In the 2006-2008 baseline SSI SIRs, PATOS events and the procedures to which they are linked are included in the calculation of the SSI SIR whereas in the 2015 baseline SSI SIRs, PATOS events and the procedures to which they are linked are excluded from the calculation of the SSI SIR. Meaning, if we control for all other inclusion criteria other than PATOS data for both baselines, we will notice differences in the number of observed events as well as the number of predicted infections for the 2 baselines. For details of the 2015 baseline and risk adjustment calculation, please review the NHSN Guide to the SIR referenced below. For details of the 2006-2008 baseline4 and risk adjustment, please see the SHEA paper "Improving Risk-Adjusted Measures of Surgical Site Infection for the National Healthcare Safety Network" by author Yi Mu.

BACKGROUND: The JCTLM created a Task Force on Reference Measurement System Implementation (TF-RMSI) to provide guidance on metrological traceability implementation for the in vitro diagnostics (IVD) community. CONTENT: TF-RMSI investigated the reference measurement systems (RMS) for 13 common measurands by applying the following procedural steps: (a) extracting data from the JCTLM database of available certified reference materials (CRMs) and reference measurement procedures (RMPs); (b) describing the RMS to which each recruited CRM or RMP belongs; (c) identifying the intended use of the CRMs, and, if used as a common calibrator for IVD measuring systems and/or trueness assessment of field methods was included, checking the CRM's certificate for information about commutability with clinical samples; and (d) checking if the CRM or RMP measurement uncertainty (MU) has the potential to be small enough to avoid significantly affecting the analytical performance specifications (APS) for MU of clinical sample results when the MU from the IVD calibrator and from the end-user measuring system were combined. SUMMARY: We produced a synopsis of JCTLM-listed higher-order CRMs and RMPs for the selected measurands, including their main characteristics for implementing traceability and fulfilling (or not) the APS for suitable MU. Results showed that traceability to higher-order references can be established by IVD manufacturers within the defined APS for most of the 13 selected measurands. However, some measurands do not yet have suitable CRMs for use as common calibrators. For these measurands, splitting clinical samples with a laboratory performing the RMP may provide a practical alternative for establishing a calibration hierarchy.

BACKGROUND: Timely and appropriate administration of post-exposure prophylaxis (PEP) is an essential component of human rabies prevention programs. We evaluated patient care at rabies clinics in a high-risk county in Hunan Province, China to inform strategies needed to achieve dog-mediated human rabies elimination by 2030. METHODS: We collected information on PEP, staff capacity, and service availability at the 17 rabies clinics in the high-risk county during onsite visits and key staff interviews. Additionally, we conducted observational assessments at five of these clinics, identified through purposive sampling to capture real-time information on patient care during a four-week period. Wound categories assigned by trained observers were considered accurate per national guidelines for comparison purposes. We used the kappa statistic and an alpha level of 0.05 to assess agreement between observers and clinic staff. RESULTS: In 2015, the 17 clinics provided PEP to 5,261 patients. Although rabies vaccines were available at all 17 clinics, rabies immune globulin (RIG) was only available at the single urban clinic in the county. During the assessment period in 2016, 196 patients sought care for possible rabies virus exposures. According to observers, 88 (44%) patients had category III wounds, 104 (53%) had category II wounds and 4 (2%) had category I wounds. Observers and PEP clinic staff agreed on approximately half of the assigned wound categories (kappa = 0.55, p-value< 0.001). Agreement for the urban county-level CDC clinic (kappa = 0.93, p-value<0.001) was higher than that of the rural township clinics (kappa = 0.16, p-value = 0.007). Using observer assigned wound categories, 142 (73%) patients received rabies vaccinations and RIG as outlined in the national guidelines. CONCLUSION: Rabies PEP services were available at each town of the project county; however, gaps between clinical practice of PEP and recommendations of national rabies guidelines were identified. We used these findings to develop and implement a training to rabies clinic staff on wound categorization, wound care, and appropriate use of PEP. Additional risk-based approaches for evaluating human rabies virus exposures may be needed as China progresses towards elimination.

Multiplex assays for malaria antigen detection can gather data from large sample sets, but considerations for the consistency and quality assurance (QA) of mass testing lack evaluation. We present a QA framework for a study occurring November 2019 to March 2020 involving 504 assay plates detecting four Plasmodium antigens: pan-Plasmodium aldolase and lactate dehydrogenase (LDH), histidine-rich protein 2 (HRP2), P. vivax LDH (PvLDH). Controls on each plate included buffer blank, antigen negative blood, and 4-point positive dilution curve. The blank and negative blood provided consistently low signal for all targets except for pAldolase, which showed variability. Positive curve signals decreased throughout the 5-month study duration but retained a coefficient of variation (CV) of < 5%, with the exception of HRP2 in month 5 (CV of 11%). Regression fittings for inter-plate control signals provided mean and standard deviations (SDs), and of 504 assay plates, 6 (1.2%) violated the acceptable deviation limits and were repeated. For the 40,272 human blood samples assayed in this study, of 161,088 potential data points (each sample × 4 antigens), 160,641 (99.7%) successfully passed quality checks. The QA framework presented here can be utilized to ensure quality of laboratory antigen detection for large sample sets.

With reduced prevalence of visceral leishmaniasis (VL) in the Indian subcontinent (ISC), direct and field deployable diagnostic tests are needed to implement an effective diagnostic and surveillance algorithm for post-elimination VL control. In this regard, here we investigated the diagnostic efficacies of a loop-mediated isothermal amplification (LAMP) assay (Loopamp™ Leishmania Detection Kit, Eiken Chemical CO., Ltd, Japan), a real-time quantitative PCR assay (qPCR) and the Leishmania antigen ELISA (CLIN-TECH, UK) with different sampling techniques and evaluated their prospect to incorporate into post-elimination VL control strategies. Eighty clinically and rK39 rapid diagnostic test confirmed VL cases and 80 endemic healthy controls were enrolled in the study. Peripheral blood and dried blood spots (DBS) were collected from all the participants at the time of diagnosis. DNA was extracted from whole blood (WB) and DBS via silica columns (QIAGEN) and boil & spin (B&S) methods and tested with qPCR and Loopamp. Urine was collected from all participants at the time of diagnosis and was directly subjected to the Leishmania antigen ELISA. 41 patients were followed up and urine samples were collected at day 30 and day 180 after treatment and ELISA was performed. The sensitivities of the Loopamp-WB(B&S) and Loopamp-WB(QIA) were 96.2% (95% CI 89·43-99·22) and 95% (95% CI 87·69-98·62) respectively. The sensitivity of Loopamp-DBS(QIA) was 85% (95% CI 75·26- 92·00). The sensitivities of the qPCR-WB(QIA) and qPCR-DBS(QIA) were 93.8% (95% CI 86·01-97·94) and 72.5% (95% CI 61·38-81·90) respectively. The specificity of all molecular assays was 100%. The sensitivity and specificity of the Leishmania antigen ELISA were 97.5% (95% CI 91·47-99·70) and 91.95% (95% CI 84·12-96·70) respectively. The Leishmania antigen ELISA depicted clinical cure at day 180 in all the followed-up cases. Efficacy and sustainability identify the Loopamp-WB(B&S) and the Leishmania antigen ELISA as promising and minimally invasive VL diagnostic tools to support VL diagnostic and surveillance activities respectively in the post-elimination era.