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.

BACKGROUND: A simple treated fabric device for passively emanating the volatile pyrethroid transfluthrin was recently developed in Tanzania that protected against nocturnal Anopheles and Culex mosquitoes for several months. Here these transfluthrin emanators were assessed in Port-au-Prince, Haiti against outdoor-biting Aedes. METHODS: Transfluthrin emanators were distributed to participating households in poor-to-middle class urban neighbourhoods and evaluated once every two months in terms of their effects on human landing rates of wild Aedes populations. A series of three such entomological assessment experiments were conducted, to examine the influence of changing weather conditions, various transfluthrin formulations and emanator placement on protective efficacy measurements. Laboratory experiments assessed resistance of local Aedes aegypti to transfluthrin and deltamethrin, and the irritancy and repellency of the transfluthrin-treated fabric used in the field. RESULTS: Across all three entomological field assessments, little evidence of protection against wild Ae. aegypti was observed, regardless of weather conditions, transfluthrin formulation or emanator placement: A generalized linear mixed model fitted to the pooled data from all three assessment rounds (921 females caught over 5129 hours) estimated a relative landing rate [95% Confidence interval] of 0.87 [0.73, 1.04] for users of treated versus untreated emanators (P = 0.1241). Wild Ae. aegypti in this setting were clearly resistant to transfluthrin when compared to a fully susceptible colony. CONCLUSIONS: Transfluthrin emanators had little if any apparent effect upon Aedes landing rates by wild Ae. aegypti in urban Haiti, and similar results have been obtained by comparable studies in Tanzania, Brazil and Peru. In stark contrast, however, parallel sociological assessments of perspectives among these same end-users in urban Haitian communities indicate strong satisfaction in terms of perceived protection against mosquitoes. It remains unclear why the results obtained from these complementary entomological and sociological assessments in Haiti differ so much, as do those from a similar set of studies in Brazil. It is encouraging, however, that similar contrasts between the entomological and epidemiological results of a recent large-scale assessment of another transfluthrin emanator product in Peru, which indicate they provide useful protection against Aedes-borne arboviral infections, despite apparently providing only modest protection against Aedes mosquito bites.

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.

Patterns of within-host influenza A virus (IAV) diversity and evolution have been described in natural human infections, but these patterns remain poorly characterized in non-human hosts. Elucidating these dynamics is important to better understand IAV biology and the evolutionary processes that govern spillover into humans. Here, we sampled an IAV outbreak in pigs during a week-long county fair to characterize viral diversity and evolution in this important reservoir host. Nasal wipes were collected on a daily basis from all pigs present at the fair, yielding up to 421 samples per day. Subtyping of PCR-positive samples revealed the co-circulation of H1N1 and H3N2 subtype swine IAVs. PCR-positive samples with robust Ct values were deep-sequenced, yielding 506 sequenced samples from a total of 253 pigs. Based on higher-depth re-sequenced data from a subset of these initially sequenced samples (260 samples from 168 pigs), we characterized patterns of within-host IAV genetic diversity and evolution. We find that IAV genetic diversity in single-subtype infected pigs is low, with the majority of intrahost Single Nucleotide Variants (iSNVs) present at frequencies of <10%. The ratio of the number of nonsynonymous to the number of synonymous iSNVs is significantly lower than under the neutral expectation, indicating that purifying selection shapes patterns of within-host viral diversity in swine. The dynamic turnover of iSNVs and their pronounced frequency changes further indicate that genetic drift also plays an important role in shaping IAV populations within pigs. Taken together, our results highlight similarities in patterns of IAV genetic diversity and evolution between humans and swine, including the role of stochastic processes in shaping within-host IAV dynamics.

INTRODUCTION: Although adverse childhood experiences (ACEs) are associated with lifelong health harms, current surveillance data on adults' ACEs exposures are either unavailable or incomplete for many states. In this study, recent data from a nationally representative survey were used to obtain current and complete ACEs estimates at the national and state levels. METHODS: Current, complete, by-state ACEs estimates were obtained by applying small area estimation (SAE) technique to individual-level data on adults aged 18+ years from 2019-2020 Behavioral Risk Factor Surveillance System (BRFSS) survey. The standardized ACEs questions included in 2019-2020 BRFSS survey allowed for obtaining ACEs estimates consistent across states. All missing ACEs responses (state did not offer ACEs questions or offered to only some respondents; respondents skipped questions) were predicted through multilevel mixed-effects logistic (MMEL) and jackknifed MMEL SAE regressions. The analyses were conducted between October 2022 and May 2023. RESULTS: Estimated 62.8% of U.S. adults had past ACEs exposures (range: 54.9% in Connecticut; 72.5% in Maine). Emotional abuse (34.5%) was most common; household member incarceration (10.6%) was least common. Sexual abuse varied markedly between females (22.2%) and males (5.4%). Most ACEs exposures were lowest for adults who were non-Hispanic white, had the highest level of education (college degree) or income (annual income $50,000+), or had access to a personal healthcare provider. CONCLUSIONS: Current complete ACE estimates demonstrate high countrywide exposures and stark socio-demographic inequalities in ACEs burden, highlighting opportunities to prevent ACEs by focusing social, educational, medical, and public health interventions on populations disproportionately impacted.

The Gram-negative Elizabethkingia express multiple antibiotic resistance and cause severe opportunistic infections. Vancomycin is commonly used to treat Gram-positive infections and has also been used to treat Elizabethkingia infections, even though Gram-negative organisms possess a vancomycin permeability barrier. Elizabethkingia anophelis appeared relatively vancomycin-susceptible and challenge with this drug led to morphological changes indicating cell lysis. In stark contrast, vancomycin growth challenge revealed that E. anophelis populations refractory to vancomycin emerged. In addition, E. anophelis vancomycin-selected mutants arose at high frequencies and demonstrated elevated vancomycin resistance and reduced susceptibility to other antimicrobials. All mutants possessed a SNP in a gene (vsr1 = vancomycin-susceptibility regulator 1) encoding a PadR family transcriptional regulator located in the putative operon vsr1-ORF551, which is conserved in other Elizabethkingia spp as well. This is the first report linking a padR homologue (vsr1) to antimicrobial resistance in a Gram-negative organism. We provide evidence to support that vsr1 acts as a negative regulator of vsr1-ORF551 and that vsr1-ORF551 upregulation is observed in vancomycin-selected mutants. Vancomycin-selected mutants also demonstrated reduced cell length indicating that cell wall synthesis is affected. ORF551 is a membrane-spanning protein with a small phage shock protein conserved domain. We hypothesize that since vancomycin-resistance is a function of membrane permeability in Gram-negative organisms, it is likely that the antimicrobial resistance mechanism in the vancomycin-selected mutants involves altered drug permeability.

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

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

For the first time, a complete genome of an RNA virus has been sequenced in its original form. Previously, RNA was sequenced by the chemical degradation of radiolabelled RNA, a difficult method that produced only short sequences. Instead, RNA has usually been sequenced indirectly by copying it into cDNA, which is often amplified to dsDNA by PCR and subsequently analyzed using a variety of DNA sequencing methods. We designed an adapter to short highly conserved termini of the influenza virus genome to target the (-) sense RNA into a protein nanopore on the Oxford Nanopore MinION sequencing platform. Utilizing this method and total RNA extracted from the allantoic fluid of infected chicken eggs, we demonstrate successful sequencing of the complete influenza virus genome with 100% nucleotide coverage, 99% consensus identity, and 99% of reads mapped to influenza. By utilizing the same methodology we can redesign the adapter in order to expand the targets to include viral mRNA and (+) sense cRNA, which are essential to the viral life cycle. This has the potential to identify and quantify splice variants and base modifications, which are not practically measurable with current methods.

BACKGROUND: Violence against children and youth poses public health risks regarding mental health symptoms and substance use. Less studied is the relationship between violence and mental health/substance abuse in the Latin American context. This study explored sex-stratified relationships between violence and mental health/substance use among Colombian youth. METHODS: We analyzed the 2018 Colombian Violence Against Children and Youth Survey, which collected cross-sectional data from Colombian youth (13-24 years) (n = 2706). Exposure variables were (i) binary sexual, emotional, and physical victimization and (ii) poly-victimization. The outcomes were binary suicidal thoughts, self-harm, past-month psychological distress, binge drinking, smoking, and drug use. Sex-stratified, logistic regressions were adjusted for age, primary school, parental presence, relationship status, and witnessing community violence. RESULTS: For females, (i) emotional violence (compared to being unexposed) was associated with greater odds of suicidal thoughts, self-harm, and psychological distress and (ii) sexual violence was associated with suicidal thoughts and self-harm. For males, (i) emotional violence (compared to being unexposed) was associated with greater odds of suicidal thoughts and psychological distress, but not self-harm and (ii) sexual violence exposure was associated with suicidal thoughts and self-harm. Physical violence was generally not associated with internalized mental health outcomes for females/males, when emotional and sexual violence were held constant. Poly-victimization was consistently and positively associated with internalized mental health symptoms among females, and to a lesser degree for males. Substance use outcomes for males or females were not associated with violence. CONCLUSIONS: Findings highlight the internalized mental health burden of emotional and sexual violence.

BACKGROUND: Exposure to protracted public violence is increasingly referenced as a risk factor for domestic violence, but limited quantitative evidence has demonstrated this association to date. This study analyzes associations in Colombia between lifetime experiences of external violence, including the Colombia civil conflict and community interpersonal violence, and experiences of household violence, including intimate partner and caregiver violence. METHODS AND FINDINGS: We use the 2018 Colombia Violence Against Children and Youth Survey, employing multi-variable logistic regressions to determine the association between exposure to external violence and household violence victimization for females aged 13-24 (n = 1406). Adjusted models controlled for age, ever married, currently in school, and past 12-mo work experience and standard errors were adjusted to account for the multi-stage sampling design. Females who had ever witnessed community violence (39.23 %) faced increased risks of experiencing both physical violence (aOR = 2.81; 95 % CIs: 1.54-5.14; p < 0.001) and emotional violence (aOR: 2.48; 95 % CIs: 1.29-4.75; p < 0.01) from caregivers. Females who had ever witnessed internal conflict (15.99 %) had a greater likelihood of experiencing emotional violence from caregivers (aOR: 5.24; 95 % CIs: 1.86-14.76; p < 0.01) as well as physical violence perpetrated by intimate partners (aOR: 3.31; 95 % CIs: 1.22-8.95; p < 0.05). CONCLUSIONS: This study demonstrates the connection between exposure to community violence and internal conflict and household violence victimization among adolescent and young adult females in Colombia. Findings build the evidence base for more holistic and coordinated policy and programming efforts and foreground the need to identify and support vulnerable populations across socioecological domains in contexts of chronic violence.

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.

While primarily considered a respiratory pathogen, influenza A virus (IAV) is nonetheless capable of spreading to, and replicating in, numerous extrapulmonary tissues in humans. However, within-host assessments of genetic diversity during multicycle replication have been largely limited to respiratory tract tissues and specimens. As selective pressures can vary greatly between anatomical sites, there is a need to examine how measures of viral diversity may vary between influenza viruses exhibiting different tropisms in humans, as well as following influenza virus infection of cells derived from different organ systems. Here, we employed human primary tissue constructs emulative of the human airway or corneal surface, and we infected both with a panel of human- and avian-origin IAV, inclusive of H1 and H3 subtype human viruses and highly pathogenic H5 and H7 subtype viruses, which are associated with both respiratory disease and conjunctivitis following human infection. While both cell types supported productive replication of all viruses, airway-derived tissue constructs elicited greater induction of genes associated with antiviral responses than did corneal-derived constructs. We used next-generation sequencing to examine viral mutations and population diversity, utilizing several metrics. With few exceptions, generally comparable measures of viral diversity and mutational frequency were detected following homologous virus infection of both respiratory-origin and ocular-origin tissue constructs. Expansion of within-host assessments of genetic diversity to include IAV with atypical clinical presentations in humans or in extrapulmonary cell types can provide greater insight into understanding those features most prone to modulation in the context of viral tropism. IMPORTANCE Influenza A virus (IAV) can infect tissues both within and beyond the respiratory tract, leading to extrapulmonary complications, such as conjunctivitis or gastrointestinal disease. Selective pressures governing virus replication and induction of host responses can vary based on the anatomical site of infection, yet studies examining within-host assessments of genetic diversity are typically only conducted in cells derived from the respiratory tract. We examined the contribution of influenza virus tropism on these properties two different ways: by using IAV associated with different tropisms in humans, and by infecting human cell types from two different organ systems susceptible to IAV infection. Despite the diversity of cell types and viruses employed, we observed generally similar measures of viral diversity postinfection across all conditions tested; these findings nonetheless contribute to a greater understanding of the role tissue type contributes to the dynamics of virus evolution within a human host.

During the evolution of SARS-CoV-2 in humans a D614G substitution in the spike (S) protein emerged and became the predominant circulating variant (S-614G) of the COVID-19 pandemic (1) . However, whether the increasing prevalence of the S-614G variant represents a fitness advantage that improves replication and/or transmission in humans or is merely due to founder effects remains elusive. Here, we generated isogenic SARS-CoV-2 variants and demonstrate that the S-614G variant has (i) enhanced binding to human ACE2, (ii) increased replication in primary human bronchial and nasal airway epithelial cultures as well as in a novel human ACE2 knock-in mouse model, and (iii) markedly increased replication and transmissibility in hamster and ferret models of SARS-CoV-2 infection. Collectively, our data show that while the S-614G substitution results in subtle increases in binding and replication in vitro , it provides a real competitive advantage in vivo , particularly during the transmission bottle neck, providing an explanation for the global predominance of S-614G variant among the SARS-CoV-2 viruses currently circulating.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

The report “Genomic Surveillance for SARS-CoV-2 Variants: Predominance of the Delta (B.1.617.2) and Omicron (B.1.1.529) Variants — United States, June 2021–January 2022” contained several errors.

Sickle cell disease (SCD), a group of inherited disorders, is a bellwether example of a chronic, disabling, life-threatening condition that is disproportionately underserved by the medical community. SCD is arguably associated with the most significant health inequities of both the 20th and 21st centuries. Although these inequities, which are linked to historical interpersonal and institutional racism, have been long recognized, there has been scant progress in closing the healthcare gaps.1 SCD affects at least 100,000 individuals in the United States, the vast majority being Black or African American individuals.1 Abnormal hemoglobin produced in SCD causes red blood cells (RBCs) to sickle, clogging blood flow and leading to vaso-occlusive crises and potential impacts on every organ system. Persons with SCD experience numerous complications, including recurrent episodes of severe pain, pneumonia and acute chest syndrome, stroke, and organ damage. Estimated life expectancy of those with SCD in the US is >20 years shorter than the average expected and the deficit in quality-adjusted life expectancy is particularly stark (>30 years shorter).2 esting is a safe and feasible alternative to mandatory quarantine and can be used to maximize safe in-person learning time during the pandemic.

PURPOSE: There is a paucity of research examining the contextual factors that shape the violence experienced by those engaged in transactional sex, particularly among adolescent boys and young men. Recognizing the acute vulnerability among youth engaged in transactional sex, this analysis examined the associations between lifetime transactional sex and experience of violence among 13- to 24-year-old males. METHODS: Using data from two strata of the 2018 Violence Against Children and Youth Survey from Colombia, logistic regressions were used to estimate the association between engagement in transactional sex and violence exposure. Three groups of violence outcomes were examined: violence victimization, violence perpetration, and witnessing violence. RESULTS: Violence victimization and witnessing violence were widespread. Adolescent boys and young men with a lifetime history of transactional sex were significantly more likely to experience violence victimization than those without a lifetime history of transactional sex, particularly intimate partner violence (adjusted odds ratio [aOR]: 5.23 and 5.41) and caregiver emotional violence (aOR: 7.23 and 8.74). In the national and priority samples respectively, those with a lifetime history of transactional sex were also significantly more likely to witness violence within the home (aOR: 4.42 and 4.99) and outside of the home (aOR: 7.24 and 28.32). DISCUSSION: Although research is needed to determine causal pathways, our findings highlight the ubiquity of violence and the criticality of supporting this group of adolescent boys and young men. Interventions for those with a history of transactional sex should address factors that may contribute to drivers of violence and transactional sex.

Before the emergence of SARS-CoV-2, the virus that causes COVID-19, influenza activity in the United States typically began to increase in the fall and peaked in February. During the 2021-22 season, influenza activity began to increase in November and remained elevated until mid-June, featuring two distinct waves, with A(H3N2) viruses predominating for the entire season. This report summarizes influenza activity during October 3, 2021-June 11, 2022, in the United States and describes the composition of the Northern Hemisphere 2022-23 influenza vaccine. Although influenza activity is decreasing and circulation during summer is typically low, remaining vigilant for influenza infections, performing testing for seasonal influenza viruses, and monitoring for novel influenza A virus infections are important. An outbreak of highly pathogenic avian influenza A(H5N1) is ongoing; health care providers and persons with exposure to sick or infected birds should remain vigilant for onset of symptoms consistent with influenza. Receiving a seasonal influenza vaccine each year remains the best way to protect against seasonal influenza and its potentially severe consequences.

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.

In the United States, annual vaccination against seasonal influenza is recommended for all persons aged ≥6 months except when contraindicated (1). Currently available influenza vaccines are designed to protect against four influenza viruses: A(H1N1)pdm09 (the 2009 pandemic virus), A(H3N2), B/Victoria lineage, and B/Yamagata lineage. Most influenza viruses detected this season have been A(H3N2) (2). With the exception of the 2020-21 season, when data were insufficient to generate an estimate, CDC has estimated the effectiveness of seasonal influenza vaccine at preventing laboratory-confirmed, mild/moderate (outpatient) medically attended acute respiratory infection (ARI) each season since 2004-05. This interim report uses data from 3,636 children and adults with ARI enrolled in the U.S. Influenza Vaccine Effectiveness Network during October 4, 2021-February 12, 2022. Overall, vaccine effectiveness (VE) against medically attended outpatient ARI associated with influenza A(H3N2) virus was 16% (95% CI = -16% to 39%), which is considered not statistically significant. This analysis indicates that influenza vaccination did not reduce the risk for outpatient medically attended illness with influenza A(H3N2) viruses that predominated so far this season. Enrollment was insufficient to generate reliable VE estimates by age group or by type of influenza vaccine product (1). CDC recommends influenza antiviral medications as an adjunct to vaccination; the potential public health benefit of antiviral medications is magnified in the context of reduced influenza VE. CDC routinely recommends that health care providers continue to administer influenza vaccine to persons aged ≥6 months as long as influenza viruses are circulating, even when VE against one virus is reduced, because vaccine can prevent serious outcomes (e.g., hospitalization, intensive care unit (ICU) admission, or death) that are associated with influenza A(H3N2) virus infection and might protect against other influenza viruses that could circulate later in the season.

Genomic surveillance is a critical tool for tracking emerging variants of SARS-CoV-2 (the virus that causes COVID-19), which can exhibit characteristics that potentially affect public health and clinical interventions, including increased transmissibility, illness severity, and capacity for immune escape. During June 2021-January 2022, CDC expanded genomic surveillance data sources to incorporate sequence data from public repositories to produce weighted estimates of variant proportions at the jurisdiction level and refined analytic methods to enhance the timeliness and accuracy of national and regional variant proportion estimates. These changes also allowed for more comprehensive variant proportion estimation at the jurisdictional level (i.e., U.S. state, district, territory, and freely associated state). The data in this report are a summary of findings of recent proportions of circulating variants that are updated weekly on CDC's COVID Data Tracker website to enable timely public health action.(†) The SARS-CoV-2 Delta (B.1.617.2 and AY sublineages) variant rose from 1% to >50% of viral lineages circulating nationally during 8 weeks, from May 1-June 26, 2021. Delta-associated infections remained predominant until being rapidly overtaken by infections associated with the Omicron (B.1.1.529 and BA sublineages) variant in December 2021, when Omicron increased from 1% to >50% of circulating viral lineages during a 2-week period. As of the week ending January 22, 2022, Omicron was estimated to account for 99.2% (95% CI = 99.0%-99.5%) of SARS-CoV-2 infections nationwide, and Delta for 0.7% (95% CI = 0.5%-1.0%). The dynamic landscape of SARS-CoV-2 variants in 2021, including Delta- and Omicron-driven resurgences of SARS-CoV-2 transmission across the United States, underscores the importance of robust genomic surveillance efforts to inform public health planning and practice.

The Centers for Disease Control and Prevention (CDC) continues to report stark increases in sexually transmitted disease (STD) rates, as many STD programs continue to strategize regarding how to address persistent STD disparities among racial and ethnic minorities.1,2 Sexually transmitted disease disparities are complex and driven by systemic issues, including social determinants such as racism, poverty, inadequate health care access, educational inequalities, and environmental threats.2,3 Many STD prevention efforts focus on individual-level risk factors and individual-level interventions; however, moving more upstream to address social determinants that shape the foundations of society and affect STD disparities is critical.4–6 It is key that STD programs address STD disparities to move the needle in reducing disparities seen among racial and ethnic minority populations who are most impacted by STDs, particularly for HIV, gonorrhea, chlamydia, and syphilis.7

Youth violence poses a substantive public health burden in Latin America, particularly among adolescent boys and young men. Understanding predictors of youth violence perpetration among boys and young men is critical to more effectively target and tailor prevention programs, especially in Colombia, which has endured decades-long internal armed conflict. This study uses Colombia's nationally representative 2018 Violence Against Children and Youth Survey data to examine risk and protective factors associated with violence perpetration among 13- to 24-year-old male. Amongst adolescent boys and young men in Colombia, the prevalence of ever perpetrating violence against someone other than an intimate partner was approximately 23%. Multivariable logistic regression models revealed that physical violence victimization by peers, emotional violence victimization by caregivers, having lost or been separated from a mother during childhood, and witnessing community violence were all associated with lifetime perpetration of youth violence. Programs targeting reduction of youth violence among boys might consider addressing the previously identified predictors earlier in the life course and at the individual, family and community levels.

This article outlines a pathway for public health departments and practitioners to incorporate law into their efforts to advance equity in health outcomes. We assert that examining and applying law can accelerate public health efforts to mitigate structural and systemic inequities, including racism. Recent events such as the COVID-19 pandemic and the community impacts of policing have brought into sharp relief the inequities faced by many populations. These stark and explosive examples arise out of long-standing, persistent, and sometimes hidden structural and systemic inequities that are difficult to trace because they are embedded in laws and accompanying policies and practices. We emphasize this point with a case study involving a small, majority Black community in semirural Appalachia that spent almost 50 years attempting to gain access to the local public water system, despite being surrounded by water lines. We suggest that public health practitioners have a role to play in addressing these kinds of public health problems, which are so clearly tied to the ways laws and policies are developed and executed. We further suggest that public health practitioners, invoking the 10 Essential Public Health Services, can employ law as a tool to increase their capacity to craft and implement evidence-based interventions.

During the evolution of SARS-CoV-2 in humans a D614G substitution in the spike (S) protein emerged and became the predominant circulating variant (S-614G) of the COVID-19 pandemic(1). However, whether the increasing prevalence of the S-614G variant represents a fitness advantage that improves replication and/or transmission in humans or is merely due to founder effects remains elusive. Here, we generated isogenic SARS-CoV-2 variants and demonstrate that the S-614G variant has (i) enhanced binding to human host cell surface receptor angiotensin-converting enzyme 2 (ACE2), (ii) increased replication in primary human bronchial and nasal airway epithelial cultures as well as in a novel human ACE2 knock-in mouse model, and (iii) markedly increased replication and transmissibility in hamster and ferret models of SARS-CoV-2 infection. Collectively, our data show that while the S-614G substitution results in subtle increases in binding and replication in vitro, it provides a real competitive advantage in vivo, particularly during the transmission bottle neck, providing an explanation for the global predominance of S-614G variant among the SARS-CoV-2 viruses currently circulating.

CONTEXT: In the past decade, two changes have affected the pathology residency match. First, the American Osteopathic Association (AOA) Match, which did not offer pathology residency, became accredited under a single graduate medical education (GME) system with the Main Residency Match (MRM), which offers pathology residency. Second, substantially fewer United States senior-year allopathic medical students (US MD seniors) matched into pathology residency. OBJECTIVE: To determine whether there were major changes in the number and percentage of osteopathic students and physicians (DOs) matching into pathology residency programs over the past decade. METHODS: Pathology match outcomes for DOs from 2011 to 2020 were obtained by reviewing AOA Match data from the National Matching Services and MRM data from the National Resident Matching Program (NRMP). The number of DOs that filled pathology positions in the MRM was divided by the total number of pathology positions filled in the MRM to calculate the percentage of pathology positions taken by DOs. RESULTS: Over the past decade, there was a 109% increase in the total number of DOs matching into pathology residency (34 in 2011 vs. 71 in 2020). During this time, there was a 23.3% increase in the total number of pathology positions filled in the MRM (476 in 2011 vs. 587 in 2020). Thus, the percentage of pathology residency positions filled by DOs increased from 7.1% in 2011 to 12.1% in 2020. The substantial increase of DOs in pathology occurred simultaneously with a 94.2% increase in the total number of DOs filling AOA/MRM "postgraduate year 1" (PGY-1) positions (3201 in 2011 vs. 6215 in 2020). Thus, the percentage of DOs choosing pathology residency has remained steady (1.06% in 2011 and 1.14% in 2020). In 2020, pathology had the third lowest percentage of filled PGY-1 residency positions taken by DOs, out of 15 major medical specialties. CONCLUSION: The proportion of DOs choosing pathology residency was stable from 2011 to 2020 despite the move to a single GME accreditation system and the stark decline in US MD seniors choosing pathology. In 2020, a slightly higher percentage of DOs (1.14%) chose pathology residency than US MD seniors (1.13%). Overall, DOs more often choose other medical specialties, including primary care. Additional studies are needed to determine why fewer US MD seniors, but not fewer DOs, are choosing pathology residency.

BACKGROUND: At the start of the 2019-2020 influenza season, concern arose that circulating B/Victoria viruses of the globally emerging clade V1A.3 were antigenically drifted from the strain included in the vaccine. Intense B/Victoria activity was followed by circulation of genetically diverse A(H1N1)pdm09 viruses, that were also antigenically drifted. We measured vaccine effectiveness (VE) in the United States against illness from these emerging viruses. METHODS: We enrolled outpatients aged ≥6 months with acute respiratory illness at five sites. Respiratory specimens were tested for influenza by reverse-transcriptase polymerase chain reaction (RT-PCR). Using the test-negative design, we determined influenza VE by virus sub-type/lineage and genetic subclades by comparing odds of vaccination in influenza cases versus test-negative controls. RESULTS: Among 8,845 enrollees, 2,722 (31%) tested positive for influenza, including 1,209 (44%) for B/Victoria and 1,405 (51%) for A(H1N1)pdm09. Effectiveness against any influenza illness was 39% (95% confidence interval [CI]: 32-44), 45% (95%CI: 37-52) against B/Victoria and 30% (95%CI: 21-39) against A(H1N1)pdm09 associated illness. Vaccination offered no protection against A(H1N1)pdm09 viruses with antigenically drifted clade 6B.1A 183P-5A+156K HA genes (VE 7%; 95%CI: -14 to 23%) which predominated after January. CONCLUSIONS: Vaccination provided protection against influenza illness, mainly due to infections from B/Victoria viruses. Vaccine protection against illness from A(H1N1)pdm09 was lower than historically observed effectiveness of 40-60%, due to late-season vaccine mismatch following emergence of antigenically drifted viruses. The effect of drift on vaccine protection is not easy to predict and, even in drifted years, significant protection can be observed.

Human-to-swine transmission of seasonal influenza viruses has led to sustained human-like influenza viruses circulating in the United States swine population. While some reverse zoonotic-origin viruses adapt and become enzootic in swine, nascent reverse zoonoses may result in virus detections that are difficult to classify as 'swine-origin' or 'human-origin' due to the genetic similarity of circulating viruses. This is the case for human-origin influenza A(H1N1) pandemic 2009 (pdm09) viruses detected in pigs following numerous reverse zoonosis events since the 2009 pandemic. We report the identification of two human infections with A(H1N1)pdm09 viruses originating from swine hosts and classify them as 'swine-origin' variant influenza viruses based on phylogenetic analysis and sequence comparison methods. Phylogenetic analyses of viral genomes from two cases revealed these viruses were reassortants containing A(H1N1)pdm09 HA and NA genes with genetic combinations derived from the triple reassortant internal gene cassette. Follow-up investigations determined that one individual had direct exposure to swine in the week preceding illness onset, while another did not report swine exposure. The swine-origin A(H1N1) variant cases were resolved by full genome sequence comparison of the variant viruses to swine influenza genomes. However, if reassortment does not result in the acquisition of swine-associated genes and swine virus genomic sequences are not available from the exposure source future cases may not be discernible. We have developed a pipeline that performs maximum likelihood analyses, a k-mer-based set difference algorithm, and random forest algorithms to identify swine-associated sequences in the hemagglutinin gene to differentiate between human-origin and swine-origin A(H1N1)pdm09 viruses.IMPORTANCE Influenza virus infects a wide range of hosts resulting in illnesses that vary from asymptomatic cases to severe pneumonia and death. Viral transfer can occur between human and non-human hosts resulting in human and non-human origin viruses circulating in novel hosts. In this work, we have identified the first case of a swine-origin influenza A(H1N1)pdm09 virus resulting in a human infection. This shows that as these viruses not only circulate in swine hosts, but are continuing to evolve and distinguish themselves from previously circulating human-origin influenza viruses. The development of techniques for distinguishing human-origin and swine-origin viruses are necessary for the continued surveillance of influenza viruses. We show that unique genetic signatures can differentiate circulating swine-associated strains from circulating human-associated strains of influenza A(H1N1)pdm09, and these signatures can be used to enhance surveillance of swine-origin influenza.