BACKGROUND: Growing evidence suggests that severe acute COVID-19 illness increases the risk of long COVID (also known as post-COVID-19 condition). However, few studies have examined associations between acute symptoms and long COVID onset. OBJECTIVE: This study aimed to examine associations between acute COVID-19 symptom profiles and long COVID prevalence using a population-based sample. METHODS: We used a dual mode (phone and web-based) population-based probability survey of adults with polymerase chain reaction-confirmed SARS-CoV-2 between June 2020 and May 2022 in the Michigan Disease Surveillance System to examine (1) how acute COVID-19 symptoms cluster together using latent class analysis, (2) sociodemographic and clinical predictors of symptom clusters using multinomial logistic regression accounting for classification uncertainties, and (3) associations between symptom clusters and long COVID prevalence using modified Poisson regression. RESULTS: In our sample (n=4169), 15.9% (n=693) had long COVID, defined as new or worsening symptoms at least 90 days post SARS-CoV-2 infection. We identified 6 acute COVID-19 symptom clusters resulting from the latent class analysis, with flu-like symptoms (24.7%) and fever (23.6%) being the most prevalent in our sample, followed by nasal congestion (16.4%), multi-symptomatic (14.5%), predominance of fatigue (10.8%), and predominance of shortness of breath (10%) clusters. Long COVID prevalence was highest in the multi-symptomatic (39.7%) and predominance of shortness of breath (22.4%) clusters, followed by the flu-like symptom (15.8%), predominance of fatigue (14.5%), fever (6.4%), and nasal congestion (5.6%) clusters. After adjustment, females (vs males) had greater odds of membership in the multi-symptomatic, flu-like symptom, and predominance of fatigue clusters, while adults who were Hispanic or another race or ethnicity (vs non-Hispanic White) had greater odds of membership in the multi-symptomatic cluster. Compared with the nasal congestion cluster, the multi-symptomatic cluster had the highest prevalence of long COVID (adjusted prevalence ratio [aPR] 6.1, 95% CI 4.3-8.7), followed by the predominance of shortness of breath (aPR 3.7, 95% CI 2.5-5.5), flu-like symptom (aPR 2.8, 95% CI 1.9-4.0), and predominance of fatigue (aPR 2.2, 95% CI 1.5-3.3) clusters. CONCLUSIONS: Researchers and clinicians should consider acute COVID-19 symptom profiles when evaluating subsequent risk of long COVID, including potential mechanistic pathways in a research context, and proactively screen high-risk patients during the provision of clinical care.

Human cases of avian influenza virus (AIV) infections are associated with an age-specific disease burden. As the influenza virus N2 neuraminidase (NA) gene was introduced from avian sources during the 1957 pandemic, we investigate the reactivity of N2 antibodies against A(H9N2) AIVs. Serosurvey of healthy individuals reveal the highest rates of AIV N2 antibodies in individuals aged ≥65 years. Exposure to the 1968 pandemic N2, but not recent N2, protected against A(H9N2) AIV challenge in female mice. In some older adults, infection with contemporary A(H3N2) virus could recall cross-reactive AIV NA antibodies, showing discernable human- or avian-NA type reactivity. Individuals born before 1957 have higher anti-AIV N2 titers compared to those born between 1957 and 1968. The anti-AIV N2 antibodies titers correlate with antibody titers to the 1957 N2, suggesting that exposure to the A(H2N2) virus contribute to this reactivity. These findings underscore the critical role of neuraminidase immunity in zoonotic and pandemic influenza risk assessment.

Estimates of county-level disease prevalence have a variety of applications. Such estimation is often done via model-based small-area estimation using survey data. However, for conditions with low prevalence (i.e., rare diseases or newly diagnosed diseases), counties with a high fraction of zero counts in surveys are common. They are often more common than the model used would lead one to expect; such zeros are called 'excess zeros'. The excess zeros can be structural (there are no cases to find) or sampling (there are cases, but none were selected for sampling). These issues are often addressed by combining multiple years of data. However, this approach can obscure trends in annual estimates and prevent estimates from being timely. Using single-year survey data, we proposed a Bayesian weighted Binomial Zero-inflated (BBZ) model to estimate county-level rare diseases prevalence. The BBZ model accounts for excess zero counts, the sampling weights and uses a power prior. We evaluated BBZ with American Community Survey results and simulated data. We showed that BBZ yielded less bias and smaller variance than estimates based on the binomial distribution, a common approach to this problem. Since BBZ uses only a single year of survey data, BBZ produces more timely county-level incidence estimates. These timely estimates help pinpoint the special areas of county-level needs and help medical researchers and public health practitioners promptly evaluate rare diseases trends and associations with other health conditions. © 2023 The Author(s).

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.

Glyphosate is the most widely applied herbicide worldwide. Glyphosate biomonitoring data are limited for agricultural settings. We measured urinary glyphosate concentrations and assessed exposure determinants in the Biomarkers of Exposure and Effect in Agriculture (BEEA) study. We selected four groups of BEEA participants based on self-reported pesticide exposure: recently exposed farmers with occupational glyphosate use in the last 7 days (n = 98), farmers with high lifetime glyphosate use (>80th percentile) but no use in the last 7 days (n = 70), farming controls with minimal lifetime use (n = 100), and nonfarming controls with no occupational pesticide exposures and no recent home/garden glyphosate use (n = 100). Glyphosate was quantified in first morning void urine using ion chromatography isotope-dilution tandem mass spectrometry. We estimated associations between urinary glyphosate concentrations and potential determinants using multivariable linear regression. Glyphosate was detected (≥0.2 µg/L) in urine of most farmers with recent (91 %) and high lifetime (93 %) use, as well as farming (88 %) and nonfarming (81 %) controls; geometric mean concentrations were 0.89, 0.59, 0.46, and 0.39 µg/L (0.79, 0.51, 0.42, and 0.37 µg/g creatinine), respectively. Compared with both control groups, urinary glyphosate concentrations were significantly elevated among recently exposed farmers (P < 0.0001), particularly those who used glyphosate in the previous day [vs. nonfarming controls; geometric mean ratio (GMR) = 5.46; 95 % confidence interval (CI): 3.75, 7.93]. Concentrations among high lifetime exposed farmers were also elevated (P < 0.01 vs. nonfarming controls). Among recently exposed farmers, glyphosate concentrations were higher among those not wearing gloves when applying glyphosate (GMR = 1.91; 95 % CI: 1.17, 3.11), not wearing long-sleeved shirts when mixing/loading glyphosate (GMR = 2.00; 95 % CI: 1.04, 3.86), applying glyphosate exclusively using broadcast/boom sprayers (vs. hand sprayer only; GMR = 1.70; 95 % CI: 1.00, 2.92), and applying glyphosate to crops (vs. non-crop; GMR = 1.72; 95 % CI: 1.04, 2.84). Both farmers and nonfarmers are exposed to glyphosate, with recency of occupational glyphosate use being the strongest determinant of urinary glyphosate concentrations. Continued biomonitoring of glyphosate in various settings is warranted.

BACKGROUND: Xpert MTB/RIF Ultra (Ultra) is an automated molecular test for the detection of Mycobacterium tuberculosis in sputum. We compared the sensitivity of Ultra to that of mycobacterial growth indicator tube (MGIT) liquid culture, considered the most sensitive assay in routine clinical use. METHODS: In this prospective, multicentre, cross-sectional diagnostic accuracy study, we used a non-inferiority design to assess whether the sensitivity of a single Ultra test was non-inferior to that of a single liquid culture for detection of M tuberculosis in sputum. We enrolled adults (age ≥18 years) with pulmonary tuberculosis symptoms in 11 countries and each adult provided three sputum specimens with a minimum volume of 2 mL over 2 days. Ultra was done directly on sputum 1, and Ultra and MGIT liquid culture were done on resuspended pellet from sputum 2. Results of MGIT and solid media cultures done on sputum 3 were considered the reference standard. The pre-defined non-inferiority margin was 5·0%. FINDINGS: Between Feb 18, 2016, and Dec 4, 2019, we enrolled 2906 participants. 2600 (89%) participants were analysed, including 639 (25%) of 2600 who were positive for tuberculosis by the reference standard. Of the 2357 included in the non-inferiority analysis, 877 (37%) were HIV-positive and 984 (42%) were female. Sensitivity of Ultra performed directly on sputum 1 was non-inferior to that of sputum 2 MGIT culture (MGIT 91·1% vs Ultra 91·9%; difference -0·8 percentage points; 95% CI -2·8 to 1·1). Sensitivity of Ultra performed on sputum 2 pellet was also non-inferior to that of sputum 2 MGIT (MGIT 91·1% vs Ultra 91·9%; difference -0·8 percentage points; -2·7 to 1·0). INTERPRETATION: For the detection of M tuberculosis in sputum from adults with respiratory symptoms, there was no difference in sensitivity of a single Ultra test to that of a single MGIT culture. Highly sensitive, rapid molecular approaches for M tuberculosis detection, combined with advances in genotypic methods for drug resistance detection, have potential to replace culture. FUNDING: US National Institute of Allergy and Infectious Diseases.

Streptococcus dysgalactiae subsp. equisimilis (SDSE) is an emerging cause of human infection with invasive disease incidence and clinical manifestations comparable to the closely related species, Streptococcus pyogenes. Through systematic genomic analyses of 501 disseminated SDSE strains, we demonstrate extensive overlap between the genomes of SDSE and S. pyogenes. More than 75% of core genes are shared between the two species with one third demonstrating evidence of cross-species recombination. Twenty-five percent of mobile genetic element (MGE) clusters and 16 of 55 SDSE MGE insertion regions were shared across species. Assessing potential cross-protection from leading S. pyogenes vaccine candidates on SDSE, 12/34 preclinical vaccine antigen genes were shown to be present in >99% of isolates of both species. Relevant to possible vaccine evasion, six vaccine candidate genes demonstrated evidence of inter-species recombination. These findings demonstrate previously unappreciated levels of genomic overlap between these closely related pathogens with implications for streptococcal pathobiology, disease surveillance and prevention.

OBJECTIVE: To examine differences in the incidence and prevalence of diagnosed diabetes by county rurality. PATIENTS AND METHODS: This observational, cross-sectional study used US Centers for Disease Control and Prevention data from 2004 through 2019 for county estimates of incidence and prevalence of diagnosed diabetes. County rurality was based on 6 levels (large central metro counties [most urban] to noncore counties [most rural]). Weighted least squares regression was used to relate rurality with diabetes incidence rates (IRs; per 1000 adults) and prevalence (percentage) in adults aged 20 years or older after adjusting for county-level sociodemographic factors (eg, food environment, health care professionals, inactivity, obesity). RESULTS: Overall, in 3148 counties and county equivalents, the crude IR and prevalence of diabetes were highest in noncore counties. In age and sex ratio-adjusted models, the IR of diabetes increased monotonically with increasing rurality (P<.001), whereas prevalence had a weak, nonmonotonic but statistically significant increase (P=.002). Further adjustment for sociodemographic factors including food environment, health care professionals, inactivity, and obesity attenuated differences in incidence across rurality levels, and reversed the pattern for prevalence (prevalence ratios [vs large central metro] ranged from 0.98 [95% CI, 0.97 to 0.99] for large fringe metro to 0.94 [95% CI, 0.93 to 0.96] for noncore). In region-stratified analyses adjusted for sociodemographic factors including inactivity and obesity, increasing rurality was inversely associated with incidence in the Midwest and West only and inversely associated with prevalence in all regions. CONCLUSION: The crude incidence and prevalence of diagnosed diabetes increased with increasing county rurality. After accounting for sociodemographic factors including food environment, health care professionals, inactivity, and obesity, county rurality showed no association with incidence and an inverse association with prevalence. Therefore, interventions targeting modifiable sociodemographic factors may reduce diabetes disparities by region and rurality.

Hexavalent chromium [Cr(VI)] is a common environmental pollutant and chronic exposure to Cr(VI) causes lung cancer in humans, however, the mechanism of Cr(VI) carcinogenesis has not been well understood. Lung cancer is the leading cause of cancer-related death, although the mechanisms of how lung cancer develops and progresses have been poorly understood. While long non-coding RNAs (lncRNAs) are found abnormally expressed in cancer, how dysregulated lncRNAs contribute to carcinogenesis remains largely unknown. The goal of this study is to investigate the mechanism of Cr(VI)-induced lung carcinogenesis focusing on the role of the lncRNA ABHD11 antisense RNA 1 (tail to tail) (ABHD11-AS1). It was found that the lncRNA ABHD11-AS1 expression levels are up-regulated in chronic Cr(VI) exposure-transformed human bronchial epithelial cells, chronically Cr(VI)-exposed mouse lung tissues, and human lung cancer cells as well. Bioinformatics analysis revealed that ABHD11-AS1 levels are up-regulated in lung adenocarcinomas (LUADs) tissues and associated with worse overall survival of LUAD patients but not in lung squamous cell carcinomas. It was further determined that up-regulation of ABHD11-AS1 expression plays an important role in chronic Cr(VI) exposure-induced cell malignant transformation and tumorigenesis, and the stemness of human lung cancer cells. Mechanistically, it was found that ABHD11-AS1 directly binds SART3 (spliceosome associated factor 3, U4/U6 recycling protein). The interaction of ABHD11-AS1 with SART3 promotes USP15 (ubiquitin specific peptidase 15) nuclear localization. Nuclear localized USP15 interacts with pre-mRNA processing factor 19 (PRPF19) to increase CD44 RNA alternative splicing activating β-catenin and enhancing cancer stemness. Together, these findings indicate that lncRNA ABHD11-AS1 interacts with SART3 and regulates CD44 RNA alternative splicing to promote cell malignant transformation and lung carcinogenesis.

People living with cardiovascular disease and other chronic conditions had a greater risk of complications and death during the COVID-19 pandemic (Abbasi, 2022; Clerkin et al., 2020; Vosko et al., 2023; Xie et al., 2022). Like many other health conditions, chronic diseases disproportionately affect people from minority groups and people with lower incomes (Caraballo et al., 2022; Crook & Peters, 2008). These health disparities were exacerbated by the COVID-19 disease and the effects of pandemic response measures on preventive healthcare in the USA (Andraska et al., 2021; Boehmer et al., 2022; Lopez et al., 2021). Amid the unprecedented public health crisis of COVID-19, there were many opportunities for prevention and for implementation scientists to create and test innovative solutions to mitigate these effects (Wensing et al., 2020). | | Implementation science has emerged as a potential solution to the failure to translate evidence from research into effective practice (Eccles & Mittman, 2006) and policy evident in many fields. Implementation science in health is the study of methods to promote the adoption and integration of evidence-based practices, interventions, and policies into routine healthcare and public health settings to improve our impact on population health (National Institutes of Health, 2022). The field seeks to understand the approaches that work best to translate research to real-world systems of care and further apply and adapt these approaches in different contexts and settings to improve public health. Implementation science, thus, could help maximize reach and impact of interventions for populations with chronic diseases.

BACKGROUND: The SARS-CoV-2 pandemic has underscored the need for field specimen collection and transport to diagnostic and public health laboratories. Self-collected nasal swabs transported without dependency on a cold chain have the potential to remove critical barriers to testing, expand testing capacity, and reduce opportunities for exposure of health professionals in the context of a pandemic. OBJECTIVE: We compared nasal swab collection by study participants from themselves and their children at home to collection by trained research staff. METHODS: Each adult participant collected 1 nasal swab, sampling both nares with the single swab, after which they collected 1 nasal swab from 1 child. After all the participant samples were collected for the household, the research staff member collected a separate single duplicate sample from each individual. Immediately after the sample collection, the adult participants completed a questionnaire about the acceptability of the sampling procedures. Swabs were placed in temperature-stable preservative and respiratory viruses were detected by shotgun RNA sequencing, enabling viral genome analysis. RESULTS: In total, 21 households participated in the study, each with 1 adult and 1 child, yielding 42 individuals with paired samples. Study participants reported that self-collection was acceptable. Agreement between identified respiratory viruses in both swabs by RNA sequencing demonstrated that adequate collection technique was achieved by brief instructions. CONCLUSIONS: Our results support the feasibility of a scalable and convenient means for the identification of respiratory viruses and implementation in pandemic preparedness for novel respiratory pathogens.

COVID-19 has highlighted challenges in the measurement quality and comparability of serological binding and neutralization assays. Due to many different assay formats and reagents, these measurements are known to be highly variable with large uncertainties. The development of the WHO international standard (WHO IS) and other pool standards have facilitated assay comparability through normalization to a common material but does not provide assay harmonization nor uncertainty quantification. In this paper, we present the results from an interlaboratory study that led to the development of (1) a novel hierarchy of data analyses based on the thermodynamics of antibody binding and (2) a modeling framework that quantifies the probability of neutralization potential for a given binding measurement. Importantly, we introduced a precise, mathematical definition of harmonization that separates the sources of quantitative uncertainties, some of which can be corrected to enable, for the first time, assay comparability. Both the theory and experimental data confirmed that mAbs and WHO IS performed identically as a primary standard for establishing traceability and bridging across different assay platforms. The metrological anchoring of complex serological binding and neuralization assays and fast turn-around production of an mAb reference control can enable the unprecedented comparability and traceability of serological binding assay results for new variants of SARS-CoV-2 and immune responses to other viruses.

BACKGROUND: To assist clinicians with identifying children at risk of severe outcomes, we assessed the association between laboratory findings and severe outcomes among severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected children and determined if SARS-CoV-2 test result status modified the associations. METHODS: We conducted a cross-sectional analysis of participants tested for SARS-CoV-2 infection in 41 pediatric emergency departments in 10 countries. Participants were hospitalized, had laboratory testing performed, and completed 14-day follow-up. The primary objective was to assess the associations between laboratory findings and severe outcomes. The secondary objective was to determine if the SARS-CoV-2 test result modified the associations. RESULTS: We included 1817 participants; 522 (28.7%) SARS-CoV-2 test-positive and 1295 (71.3%) test-negative. Seventy-five (14.4%) test-positive and 174 (13.4%) test-negative children experienced severe outcomes. In regression analysis, we found that among SARS-CoV-2-positive children, procalcitonin ≥0.5 ng/mL (adjusted odds ratio [aOR], 9.14; 95% CI, 2.90-28.80), ferritin >500 ng/mL (aOR, 7.95; 95% CI, 1.89-33.44), D-dimer ≥1500 ng/mL (aOR, 4.57; 95% CI, 1.12-18.68), serum glucose ≥120 mg/dL (aOR, 2.01; 95% CI, 1.06-3.81), lymphocyte count <1.0 × 10(9)/L (aOR, 3.21; 95% CI, 1.34-7.69), and platelet count <150 × 10(9)/L (aOR, 2.82; 95% CI, 1.31-6.07) were associated with severe outcomes. Evaluation of the interaction term revealed that a positive SARS-CoV-2 result increased the associations with severe outcomes for elevated procalcitonin, C-reactive protein (CRP), D-dimer, and for reduced lymphocyte and platelet counts. CONCLUSIONS: Specific laboratory parameters are associated with severe outcomes in SARS-CoV-2-infected children, and elevated serum procalcitonin, CRP, and D-dimer and low absolute lymphocyte and platelet counts were more strongly associated with severe outcomes in children testing positive compared with those testing negative.

BACKGROUND: Organophosphate esters (OPEs) have replaced flame retardant polybrominated diphenyl ethers as flame retardants in consumer products, but few longitudinal studies have characterized childhood OPE exposure. OBJECTIVE: We aimed to examine the exposure pattern of urinary OPE metabolites in children. METHODS: We quantified three urinary OPE metabolites five times in children (1, 2, 3, 5, 8 years) from 312 mother-child pairs in the Health Outcomes and Measures of the Environment (HOME) Study, a prospective pregnancy and birth cohort in Cincinnati, Ohio, USA. We examined the associations of average maternal OPE metabolite concentrations with OPE metabolite concentrations in childhood, characterized childhood OPE trajectories with latent class growth analysis (LCGA), and examined factors related to trajectory membership. RESULTS: Bis(2-chloroethyl) phosphate (BCEP) had the lowest median concentrations over time (0.66-0.97 mg/L) while the median concentrations of bis(1,3-dichloro-2-propyl) phosphate (BDCIPP) increased with age (1.44-3.80 mg/L). The median concentrations of diphenyl phosphate (DPHP) fluctuated between 1.96 and 2.69 mg/L. Intraclass correlation coefficients for urinary metabolites measured at five time points indicated high variability within individuals (0.13-0.24). Average maternal urinary BCEP and BDCIPP were associated with concentrations in early childhood. Maternal education, the birth year of the child, and having a carpet in the main activity room were associated with BCEP and BDCIPP trajectory while none of the factors were associated with DPHP trajectory. SIGNIFICANCE: The trajectory analysis showed different patterns of urinary OPE metabolite concentrations, suggesting the need to collect multiple samples to adequately reflect OPE exposure. IMPACT STATEMENT: In this well-established cohort, we evaluated the patterns of urinary OPE metabolites in children ages 1-8 years. The number of repeated measures over childhood has not been achieved in prior studies. Our results suggested the high variability of urinary OPE metabolites within individuals. Maternal metabolite concentrations during pregnancy were related to child concentrations at ages 1-3 years. BCEP, BDCIPP, and DPHP demonstrated different trajectories in children, which suggests that multiple samples may be required to capture OPE exposure patterns in childhood.

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

Background Coronavirus disease 2019 (COVID-19), the respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first identified in Wuhan, China and has since become pandemic. As part of initial response activities in the United States, enhanced contact investigations were conducted to enable early identification and isolation of additional cases and to learn more about risk factors for transmission.Methods Close contacts of nine early travel-related cases in the United States were identified. Close contacts meeting criteria for active monitoring were followed, and selected individuals were targeted for collection of additional exposure details and respiratory samples. Respiratory samples were tested for SARS-CoV-2 by real-time reverse transcription polymerase chain reaction (RT-PCR) at the Centers for Disease Control and Prevention.Results There were 404 close contacts who underwent active monitoring in the response jurisdictions; 338 had at least basic exposure data, of whom 159 had ≥1 set of respiratory samples collected and tested. Across all known close contacts under monitoring, two additional cases were identified; both secondary cases were in spouses of travel-associated case patients. The secondary attack rate among household members, all of whom had ≥1 respiratory sample tested, was 13% (95% CI: 4 – 38%).Conclusions The enhanced contact tracing investigations undertaken around nine early travel-related cases of COVID-19 in the United States identified two cases of secondary transmission, both spouses. Rapid detection and isolation of the travel-associated case patients, enabled by public awareness of COVID-19 among travelers from China, may have mitigated transmission risk among close contacts of these cases.Competing Interest StatementThe authors have declared no competing interest.Funding StatementNo external funding was sought or received.Author DeclarationsAll relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.YesAll 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.YesData may be available upon reasonable request.

The evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in the emergence of many 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 vaccines. To ascertain and rank the risk of VOCs and VOIs, we analyzed their ability to escape from vaccine-induced antibodies. The variants showed differential reductions in neutralization and replication titers by post-vaccination sera. Although the Omicron variant showed the most escape from neutralization, sera collected after a third dose of vaccine (booster sera) retained moderate neutralizing activity against that variant. Therefore, vaccination remains the most effective strategy to combat the COVID-19 pandemic.

Background: We estimated combined protection conferred by prior SARS-CoV-2 infection and COVID-19 vaccination against COVID-19-associated acute respiratory illness (ARI). Method(s): During SARS-CoV-2 Delta (B.1.617.2) and Omicron (B.1.1.529) variant circulation between October 2021 and April 2022, prospectively enrolled adult patients with outpatient ARI had respiratory and filter paper blood specimens collected for SARS-CoV-2 molecular testing and serology. Dried blood spots were tested for immunoglobulin-G antibodies against SARSCoV-2 nucleocapsid (NP) and spike protein receptor binding domain antigen using a validated multiplex bead assay. Evidence of prior SARS-CoV-2 infection also included documented or self-reported laboratory-confirmed COVID-19. We used documented COVID-19 vaccination status to estimate vaccine effectiveness (VE) by multivariable logistic regression by prior infection status. Result(s): 455 (29%) of 1577 participants tested positive for SARS-CoV-2 infection at enrollment; 209 (46%) case-patients and 637 (57%) test-negative patients were NP seropositive, had documented previous laboratory-confirmed COVID-19, or self-reported prior infection. Among previously uninfected patients, three-dose VE was 97% (95% confidence interval [CI], 60%-99%) against Delta, but not statistically significant against Omicron. Among previously infected patients, three-dose VE was 57% (CI, 20%-76%) against Omicron; VE against Delta could not be estimated. Conclusion(s): Three mRNA COVID-19 vaccine doses provided additional protection against SARS-CoV-2 Omicron variant-associated illness among previously infected participants. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license.

BACKGROUND: We estimated combined protection conferred by prior SARS-CoV-2 infection and COVID-19 vaccination against COVID-19-associated acute respiratory illness (ARI). METHODS: During SARS-CoV-2 Delta (B.1.617.2) and Omicron (B.1.1.529) variant circulation between October 2021 and April 2022, prospectively enrolled adult patients with outpatient ARI had respiratory and filter paper blood specimens collected for SARS-CoV-2 molecular testing and serology. Dried blood spots were tested for immunoglobulin-G antibodies against SARS-CoV-2 nucleocapsid (NP) and spike protein receptor binding domain antigen using a validated multiplex bead assay. Evidence of prior SARS-CoV-2 infection also included documented or self-reported laboratory-confirmed COVID-19. We used documented COVID-19 vaccination status to estimate vaccine effectiveness (VE) by multivariable logistic regression by prior infection status. RESULTS: Four hundred fifty-five (29%) of 1577 participants tested positive for SARS-CoV-2 infection at enrollment; 209 (46%) case-patients and 637 (57%) test-negative patients were NP seropositive, had documented previous laboratory-confirmed COVID-19, or self-reported prior infection. Among previously uninfected patients, three-dose VE was 97% (95% confidence interval [CI], 60%-99%) against Delta, but not statistically significant against Omicron. Among previously infected patients, three-dose VE was 57% (CI, 20%-76%) against Omicron; VE against Delta could not be estimated. CONCLUSIONS: Three mRNA COVID-19 vaccine doses provided additional protection against SARS-CoV-2 Omicron variant-associated illness among previously infected participants.

CaliciNet China, a network of provincial, county, and city laboratories coordinated by the Chinese Centers for Disease Control and Prevention, was launched in October 2016 to monitor the epidemiology and genotype distribution of norovirus outbreaks in China. During October 2016-September 2018, a total of 556 norovirus outbreaks were reported, and positive fecal samples from 470 (84.5%) outbreaks were genotyped. Most of these outbreaks were associated with person-to-person transmission (95.1%), occurred in childcare centers or schools (78.2%), and were reported during November-March of each year (63.5%). During the 2-year study period, 81.2% of all norovirus outbreaks were typed as GII.2[P16]. In China, most norovirus outbreaks are reported by childcare centers or schools; GII.2[P16] is the predominant genotype. Ongoing surveillance by CaliciNet China will provide information about the evolving norovirus genotype distribution and outbreak characteristics important for the development of effective interventions, including vaccines.

Following its emergence in Wuhan, China, in late November or early December 2019, the SARS-CoV-2 virus has rapidly spread throughout the world. On March 11, 2020, the World Health Organization declared Coronavirus Disease 2019 (COVID-19) a pandemic. Genome sequencing of SARS-CoV-2 strains allows for the reconstruction of transmission history connecting these infections. Here, we analyze 346 SARS-CoV-2 genomes from samples collected between 20 February and 15 March 2020 from infected patients in Washington State, USA. We found that the large majority of SARS-CoV-2 infections sampled during this time frame appeared to have derived from a single introduction event into the state in late January or early February 2020 and subsequent local spread, strongly suggesting cryptic spread of COVID-19 during the months of January and February 2020, before active community surveillance was implemented. We estimate a common ancestor of this outbreak clade as occurring between 18 January and 9 February 2020. From genomic data, we estimate an exponential doubling between 2.4 and 5.1 days. These results highlight the need for large-scale community surveillance for SARS-CoV-2 introductions and spread and the power of pathogen genomics to inform epidemiological understanding.

This article summarizes what is currently known about the 2019 novel coronavirus and offers interim guidance.

Broadly neutralizing antibodies against SARS-CoV-2 variants are sought to curb COVID-19 infections. Here we produced and characterized a set of mouse monoclonal antibodies (mAbs) specific for the ancestral SARS-CoV-2 receptor binding domain (RBD). Two of them, 17A7 and 17B10, were highly potent in microneutralization assay with 50% inhibitory concentration (IC(50) ) ≤ 135 ng/ml against infectious SARS-CoV-2 variants, including G614, Alpha, Beta, Gamma, Delta, Epsilon, Zeta, Kappa, Lambda, B.1.1.298, B.1.222, B.1.5 and R.1. Both mAbs (especially 17A7) also exhibited strong in vivo efficacy in protecting K18-hACE2 transgenic mice from the lethal infection with G614, Alpha, Beta, Gamma and Delta viruses. Structural analysis indicated that 17A7 and 17B10 target the tip of the receptor binding motif (RBM) in the RBD-up conformation. A third RBD-reactive mAb (3A6) although escaped by Beta and Gamma, was highly effective in cross-neutralizing Delta and Omicron BA.1 variants in vitro and in vivo. In competition experiments, antibodies targeting epitopes similar to these 3 mAbs were rarely enriched in human COVID-19 convalescent sera or post-vaccination sera. These results are helpful to inform new antibody/vaccine design and these mAbs can be useful tools for characterizing SARS-CoV-2 variants and elicited antibody responses. This article is protected by copyright. All rights reserved.

The rapid evolution of SARS-CoV-2 Omicron sublineages mandates a better understanding of viral replication and cross-neutralization among these sublineages. Here we used K18-hACE2 mice and primary human airway cultures to examine the viral fitness and antigenic relationship among Omicron sublineages. In both K18-hACE2 mice and human airway cultures, Omicron sublineages exhibited a replication order of BA.5 ≥ BA.2 ≥ BA.2.12.1 > BA.1; no difference in body weight loss was observed among different sublineage-infected mice. The BA.1-, BA.2-, BA.2.12.1-, and BA.5-infected mice developed distinguishable cross-neutralizations against Omicron sublineages, but exhibited little neutralization against the index virus (i.e., USA-WA1/2020) or the Delta variant. Surprisingly, the BA.5-infected mice developed higher neutralization activity against heterologous BA.2 and BA.2.12.1 than that against homologous BA.5; serum neutralizing titers did not always correlate with viral replication levels in infected animals. Our results revealed a distinct antigenic cartography of Omicron sublineages and support the bivalent vaccine approach.

OBJECTIVE: to conduct a systematic review of the observational studies analyzing the association between ultra-processed food (UPF) intake and the risk of depression. DESIGN: the search adhered to the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA); a search for observational studies published until June 2020 was performed in PubMed, Embase, Cochrane Library, and Web of Science databases, followed by additional manual searches. Eight reviewers, working independently in teams of two, screened studies for eligibility, extracted data, and assessed risk of bias. We resolved disagreements through discussion or, if necessary, through adjudication by a third (LH). And the study assessed cross-sectional studies using the Agency for Healthcare Research and Quality (AHRQ) methodological checklist and cohort and case-control studies using the Newcastle-Ottawa Scale (NOS) for quality. We used a tabular format to summarize the articles. RESULTS: twenty-eight studies evaluating UPF intake and risk of depression were finally selected, 21 of which had a cross-sectional design, 6 studies had a cohort design, and 1 had a case-control design. Of these, 4 cohort studies and 17 cross-sectional studies found that consumption of UPF were positively associated with depression or depressive symptoms. CONCLUSIONS: our review demonstrated that most studies included in the systematic review showed that UPF consumption is associated with the risk of depression. Future studies should consider the use of validated food intake assessments and standardized depression assessment methods to promote comparability between studies.

BACKGROUND: Natural language processing (NLP) of unstructured text from Electronic Medical Records (EMR) can improve characterization of COVID-19 signs and symptoms, but large-scale studies demonstrating the real-world application and validation of NLP for this purpose are limited. OBJECTIVE: To assess the contribution of NLP when identifying COVID-19 signs and symptoms from EMR. METHODS: This study was conducted in Kaiser Permanente Southern California, a large integrated healthcare system using data from all patients with positive SARS-CoV-2 laboratory tests from March 2020 to May 2021. An NLP algorithm was developed to extract free text from EMR on 12 established signs and symptoms of COVID-19, including fever, cough, headache, fatigue, dyspnea, chills, sore throat, myalgia, anosmia, diarrhea, vomiting/nausea and abdominal pain. The proportion of patients reporting each symptom and the corresponding onset dates were described before and after supplementing structured EMR data with NLP-extracted signs and symptoms. A random sample of 100 chart-reviewed and adjudicated SARS-CoV-2 positive cases were used to validate the algorithm performance. RESULTS: A total of 359,938 patients (mean age: 40.4 years; 53% female) with confirmed SARS-CoV-2 infection were identified over the study period. The most common signs and symptoms identified through NLP-supplemented analyses were cough (61%), fever (52%), myalgia (43%), and headache (40%). The NLP algorithm identified an additional 55,568 (15%) symptomatic cases that were previously defined as asymptomatic using structured data alone. The proportion of additional cases with each selected symptom identified in NLP-supplemented analysis varied across the selected symptoms, from 29% of all records for cough, to 61% of all records with nausea or vomiting. Of 295,305 symptomatic patients, the median time from symptom onset to testing was 3 days using structured data alone, whereas the NLP-algorithm identified signs or symptoms approximately one day earlier. When validated against chart-reviewed cases, the NLP algorithm successfully identified most signs and symptoms with consistently high sensitivity (ranging from 87% to 100%) and specificity (94% to 100%). CONCLUSIONS: These findings demonstrate that NLP can identify and characterize a broad set of COVID-19 signs and symptoms from unstructured data within the EMR, with enhanced detail and timeliness compared with structured data alone.

Here we interrogate the factors responsible for SARS-CoV-2 breakthrough infections in a K18-hACE2 transgenic mouse model. We show that Delta and the closely related Kappa variant cause viral pneumonia and severe lung lesions in K18-hACE2 mice. Human COVID-19 mRNA post-vaccination sera after the 2(nd) dose are significantly less efficient in neutralizing Delta/Kappa than early 614G virus in vitro and in vivo. By 5 months post-vaccination, ≥50% of donors lack detectable neutralizing antibodies against Delta and Kappa and all mice receiving 5-month post-vaccination sera die after the lethal challenges. Although a 3(rd) vaccine dose can boost antibody neutralization against Delta in vitro and in vivo, the mean log neutralization titers against the latest Omicron subvariants are 1/3-1/2 of those against the original 614D virus. Our results suggest that enhanced virulence, greater immune evasion and waning of vaccine-elicited protection account for SARS-CoV-2 variants caused breakthrough infections.

Organophosphate esters (OPEs) are developmental toxicants in experimental studies of animals, but limited evidence is available in humans. We included 340 mother-infant pairs in the Health Outcomes and Measures of the Environment (HOME) Study (Cincinnati, Ohio, USA) for the analysis. We evaluated gestational exposure to OPEs with gestation age at birth and newborn anthropometric measures. We quantified four OPE urinary metabolites at 16 weeks and 26 weeks of gestation. We extracted gestational age at birth, newborn weight, length, and head circumference from the chart review. We calculated z-scores for these anthropometric measures and the ponderal index. We used multiple informant models to examine the associations between repeated OPE measurements and the outcomes. We used modified Poisson regression to estimate the association of gestational exposure to OPEs with preterm birth. We also explored effect modification by infant sex and the potential mediation effect by the highest maternal blood pressure and glucose levels. We found that bis(2-chloroethyl) phosphate (BCEP) at 16 weeks and diphenyl phosphate at 26 weeks of pregnancy were positively associated with gestational age and inversely associated with preterm birth. In female newborns, BCEP at 16 weeks was inversely related to birth weight and length z-scores. In male newborns, we observed negative associations of 26-week di-n-butyl phosphate with the ponderal index at birth. No mediation by the highest maternal blood pressure or glucose levels during pregnancy was identified. In this cohort, gestational exposure to some OPEs was associated with gestational age, preterm birth, and neonatal anthropometric measures. Certain associations tended to be window- and infant sex-specific.