Ice machines can harbor water-related organisms, and the use of ice or tap water for clinical care activities has been associated with infections in health care settings. During 2021-2022, a total of 23 cases of infection by Burkholderia multivorans (sequence type ST659) were reported at two southern California hospitals and linked to contaminated ice and water from ice machines. In addition to these 23 cases, this report also includes 23 previously unreported cases of B. multivorans ST659 infections that occurred during 2020-2024: 13 at a northern California hospital, eight at a hospital in Colorado, and two additional cases at one of the southern California hospitals. The same brand of ice machine and brands of filters, descaling, and sanitizing products were used by all four hospitals; B. multivorans was isolated from samples collected from ice machines in two of the hospitals. Whole genome sequencing indicated that all clinical and ice machine isolates were highly genetically similar (0-14 single nucleotide variant differences across 81% of the selected reference genome). Recommendations from public health officials to halt the outbreak included avoiding ice and tap water during clinical care activities. An investigation is ongoing to determine possible sources of ice machine contamination. During outbreaks of water-related organisms in health care facilities, health care personnel should consider avoiding the use of tap water, including ice and water from ice machines, for patient care.

Consultations with the Centers for Disease Control and Prevention's Division of Healthcare Quality Promotion revealed patient harms associated with ophthalmic care. Adherence to core infection prevention and control principles, tailored guidance for ophthalmic settings, and compliance with manufacturing and compounding standards could decrease adverse events and patient exposures to contaminated products.

The first dengue "endgame" summit was held in Syracuse, NY over August 9 and 10, 2023. Organized and hosted by the Institute for Global Health and Translational Sciences at SUNY Upstate Medical University, the gathering brought together researchers, clinicians, drug and vaccine developers, government officials, and other key stakeholders in the dengue field for a highly collaborative and discussion-oriented event. The objective of the gathering was to discuss the current state of dengue around the world, what dengue "control" might look like, and what a potential roadmap might look like to achieve functional dengue control. Over the course of 7 sessions, speakers with a diverse array of expertise highlighted both current and historic challenges associated with dengue control, the state of dengue countermeasure development and deployment, as well as fundamental virologic, immunologic, and medical barriers to achieving dengue control. While sustained eradication of dengue was considered challenging, attendees were optimistic that significant reduction in the burden of dengue can be achieved by integration of vector control with effective application of therapeutics and vaccines.

Intimate partner violence (IPV) against women is a global public health problem. Conceptual frameworks suggest misperceived norms around IPV might drive perpetration of violence against women in southern and eastern Africa. We conducted a cross-sectional, population-based survey of all men residing in a rural parish in southwest Uganda, eliciting their endorsement of IPV in five hypothetical scenarios and their reported frequency of perpetration of violence against their wife/main partner. They also reported their perceptions about the extent to which most other men in their villages endorsed and/or perpetrated IPV, which we compared against the population data to measure the primary explanatory variable of interest: whether individuals misperceived norms around IPV. We fitted multivariable Poisson regression models specifying personal IPV endorsement and IPV perpetration as the outcomes. Overall, 765 men participated in the study (90% response rate): 182 (24%) personally endorsed IPV, and 78 of 456 partnered men (17%) reported perpetrating one or more acts of IPV at least once per month. Although most men neither endorsed nor reported perpetrating IPV, 342 (45%) men mistakenly thought that most other men in their villages endorsed IPV and 365 (48%) men mistakenly thought that most other men perpetrate IPV at least monthly. In multivariable regression models, men who misperceived most men to endorse IPV were more likely to endorse IPV themselves (adjusted relative risk [aRR] = 2.44; 95% CI [1.66, 3.59]; p < .001). Among partnered men, those who misperceived IPV perpetration to be normative were more likely to perpetrate IPV themselves (aRR = 4.38; [2.53, 7.59]; p < .001). Interventions to correct misperceived norms about IPV may be a promising method for reducing violence against women in rural Uganda.

Mycobacterium abscessus is an intrinsically drug-resistant, rapidly growing, nontuberculous mycobacterium; extrapulmonary infections have been reported in association with medical tourism (1). During November-December 2022, two Colorado hospitals (hospitals A and B) treated patient A, a Colorado woman aged 30-39 years, for M. abscessus meningitis. In October 2022, she had received intrathecal donor embryonic stem cell injections in Baja California, Mexico to treat multiple sclerosis and subsequently experienced headaches and fevers, consistent with meningitis. Her cerebrospinal fluid revealed neutrophilic pleocytosis and grew M. abscessus in culture at hospital A. Hospital A's physicians consulted hospital B's infectious diseases (ID) physicians to co-manage this patient (2).

OBJECTIVES: The Enduring Consensus Cervical Cancer Screening and Management Guidelines Committee developed recommendations for dual stain (DS) testing with CINtec PLUS Cytology for use of DS to triage high-risk human papillomavirus (HPV)-positive results. METHODS: Risks of cervical intraepithelial neoplasia grade 3 or worse were calculated according to DS results among individuals testing HPV-positive using data from the Kaiser Permanente Northern California cohort and the STudying Risk to Improve DisparitiES study in Mississippi. Management recommendations were based on clinical action thresholds developed for the 2019 American Society for Colposcopy and Cervical Pathology Risk-Based Management Consensus Guidelines. Resource usage metrics were calculated to support decision-making. Risk estimates in relation to clinical action thresholds were reviewed and used as the basis for draft recommendations. After an open comment period, recommendations were finalized and ratified through a vote by the Consensus Stakeholder Group. RESULTS: For triage of positive HPV results from screening with primary HPV testing (with or without genotyping) or with cytology cotesting, colposcopy is recommended for individuals testing DS-positive. One-year follow-up with HPV-based testing is recommended for individuals testing DS-negative, except for HPV16- and HPV18-positive results, or high-grade cytology in cotesting, where immediate colposcopy referral is recommended. Risk estimates were similar between the Kaiser Permanente Northern California and STudying Risk to Improve DisparitiES populations. In general, resource usage metrics suggest that compared with cytology, DS requires fewer colposcopies and detects cervical intraepithelial neoplasia grade 3 or worse earlier. CONCLUSIONS: Dual stain testing with CINtec PLUS Cytology is acceptable for triage of HPV-positive test results. Risk estimates are portable across different populations.

OBJECTIVES: The Enduring Consensus Cervical Cancer Screening and Management Guidelines (Enduring Guidelines) effort is a standing committee to continuously evaluate new technologies and approaches to cervical cancer screening, management, and surveillance. METHODS AND RESULTS: The Enduring Guidelines process will selectively incorporate new technologies and approaches with adequate supportive data to more effectively improve cancer prevention for high-risk individuals and decrease unnecessary procedures in low-risk individuals. This manuscript describes the structure, process, and methods of the Enduring Guidelines effort. Using systematic literature reviews and primary data sources, risk of precancer will be estimated and recommendations will be made based on risk estimates in the context of established risk-based clinical action thresholds. The Enduring Guidelines process will consider health equity and health disparities by assuring inclusion of diverse populations in the evidence review and risk assessment and by developing recommendations that provide a choice of well-validated strategies that can be adapted to different settings. CONCLUSIONS: The Enduring Guidelines process will allow updating existing cervical cancer screening and management guidelines rapidly when new technologies are approved or new scientific evidence becomes available.

During the COVID-19 vaccination rollout from March 2021- December 2022, the Centers for Disease Control and Prevention funded 110 primary and 1051 subrecipient partners at the national, state, local, and community-based level to improve COVID-19 vaccination access, confidence, demand, delivery, and equity in the United States. The partners implemented evidence-based strategies among racial and ethnic minority populations, rural populations, older adults, people with disabilities, people with chronic illness, people experiencing homelessness, and other groups disproportionately impacted by COVID-19. CDC also expanded existing partnerships with healthcare professional societies and other core public health partners, as well as developed innovative partnerships with organizations new to vaccination, including museums and libraries. Partners brought COVID-19 vaccine education into farm fields, local fairs, churches, community centers, barber and beauty shops, and, when possible, partnered with local healthcare providers to administer COVID-19 vaccines. Inclusive, hyper-localized outreach through partnerships with community-based organizations, faith-based organizations, vaccination providers, and local health departments was critical to increasing COVID-19 vaccine access and building a broad network of trusted messengers that promoted vaccine confidence. Data from monthly and quarterly REDCap reports and monthly partner calls showed that through these partnerships, more than 295,000 community-level spokespersons were trained as trusted messengers and more than 2.1 million COVID-19 vaccinations were administered at new or existing vaccination sites. More than 535,035 healthcare personnel were reached through outreach strategies. Quality improvement interventions were implemented in healthcare systems, long-term care settings, and community health centers resulting in changes to the clinical workflow to incorporate COVID-19 vaccine assessments, recommendations, and administration or referrals into routine office visits. Funded partners' activities improved COVID-19 vaccine access and addressed community concerns among racial and ethnic minority groups, as well as among people with barriers to vaccination due to chronic illness or disability, older age, lower income, or other factors.

This Research Letter summarizes all updates to the 2019 Guidelines through September 2023, including: endorsement of the 2021 Opportunistic Infections guidelines for HIV+ or immunosuppressed patients; clarification of use of human papillomavirus testing alone for patients undergoing observation for cervical intraepithelial neoplasia 2; revision of unsatisfactory cytology management; clarification that 2012 guidelines should be followed for patients aged 25 years and older screened with cytology only; management of patients for whom colposcopy was recommended but not completed; clarification that after treatment for cervical intraepithelial neoplasia 2+, 3 negative human papillomavirus tests or cotests at 6, 18, and 30 months are recommended before the patient can return to a 3-year testing interval; and clarification of postcolposcopy management of minimally abnormal results.

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

OBJECTIVE: To learn about the perceptions of healthcare personnel (HCP) on the barriers they encounter when performing infection prevention and control (IPC) practices in labor and delivery to help inform future IPC resources tailored to this setting. DESIGN: Qualitative focus groups. SETTING: Labor and delivery units in acute-care settings. PARTICIPANTS: A convenience sample of labor and delivery HCP attending the Infectious Diseases Society for Obstetrics and Gynecology 2022 Annual Meeting. METHODS: Two focus groups, each lasting 45 minutes, were conducted by a team from the Centers for Disease Control and Prevention. A standardized script facilitated discussion around performing IPC practices during labor and delivery. Coding was performed by 3 reviewers using an immersion-crystallization technique. RESULTS: In total, 18 conference attendees participated in the focus groups: 67% obstetrician-gynecologists, 17% infectious disease physicians, 11% medical students, and 6% an obstetric anesthesiologist. Participants described the difficulty of consistently performing IPC practices in this setting because they often respond to emergencies, are an entry point to the hospital, and frequently encounter bodily fluids. They also described that IPC training and education is not specific to labor and delivery, and personal protective equipment is difficult to locate when needed. Participants observed a lack of standardization of IPC protocols in their setting and felt that healthcare for women and pregnant people is not prioritized on a larger scale and within their hospitals. CONCLUSIONS: This study identified barriers to consistently implementing IPC practices in the labor and delivery setting. These barriers should be addressed through targeted interventions and the development of obstetric-specific IPC resources.

In this summary of US Centers for Disease Control and Prevention (CDC) consultations with state and local health departments concerning their bronchoscope-associated investigations from 2014 through 2022, bronchoscope reprocessing gaps and exposure to nonsterile water sources appeared to be the major routes of transmission of infectious pathogens, which were primarily water-associated bacteria.

When new pathogens emerge, numerous questions arise about their future spread, some of which can be addressed with probabilistic forecasts. The many uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among model structures and assumptions, however. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance of a suite of 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about the role of human mobility in driving transmission, spatiotemporal variation in transmission potential, and the number of times the virus was introduced. All models used the same core transmission model and the same iterative data assimilation algorithm to generate forecasts. By assessing forecast performance through time using logarithmic scoring with ensemble weighting, we found that which model assumptions had the most ensemble weight changed through time. In particular, spatially coupled models had higher ensemble weights in the early and late phases of the epidemic, whereas non-spatial models had higher ensemble weights at the peak of the epidemic. We compared forecast performance of the equally-weighted ensemble model to each individual model and identified a trade-off whereby certain individual models outperformed the ensemble model early in the epidemic but the ensemble model outperformed all individual models on average. On balance, our results suggest that suites of models that span uncertainty across alternative assumptions are necessary to obtain robust forecasts in the context of emerging infectious diseases.Competing Interest StatementThe authors have declared no competing interest.Funding StatementRJO acknowledges support from an Eck Institute for Global Health Fellowship, GLOBES grant, Arthur J. Schmitt Fellowship, and the UNICEF Office of Innovation. MUGK is supported by The Branco Weiss Fellowship - Society in Science, administered by the ETH Zurich and acknowledges funding from the Oxford Martin School and the European Union Horizon 2020 project MOOD (\#874850). SCH is supported by the Wellcome Trust (220414/Z/20/Z).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:No IRB approvals were necessary.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe mobile phone data set used in this study is proprietary and subject to strict privacy regulations. The access to this data set was granted after reaching a non-disclosure agreement with the proprietor, who anonymized and aggregated the original data before giving access to the authors. The mobile phone is available on request after negotiation of a non-disclosure agreement with the company. The contact person is Enrique Frias-Martinez (efm{at}tid.es). Epidemiological, meteorological, and demographic data are available from Siraj et al.2018 and additionally available on https://github.com/roidtman/eid_ensemble_forecasting. https://www.github.com/roidtman/eid_ensemble_forecasting

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

An effective and widely used vaccine could reduce the burden of dengue virus (DENV) around the world. DENV is endemic in Puerto Rico, where the dengue vaccine CYD-TDV is currently under consideration as a control measure. CYD-TDV has demonstrated efficacy in clinical trials in vaccinees who had prior dengue infection. However, in vaccinees who had no prior dengue infection, the vaccine had a modestly elevated risk of hospitalization and severe disease. The WHO therefore recommended a strategy of pre-vaccination screening and vaccination of seropositive persons. To estimate the cost-effectiveness and benefits of this intervention (i.e., screening and vaccination of seropositive persons) in Puerto Rico, we simulated 10 years of the intervention in 9-year-olds using an agent-based model. Across the entire population, we found that 5.5% (4.6%-6.3%) of dengue hospitalizations could be averted. However, we also found that 1.6 (1.3 - 2.1) additional hospitalizations could occur for every 1,000 DENV-naïve children who were vaccinated following a false-positive test results for prior exposure. The ratio of the averted hospitalizations among all vaccinees to additional hospitalizations among DENV-naïve vaccinees was estimated to be 19 (13-24). At a base case cost of vaccination of 382 USD, we found an incremental cost-effectiveness ratio of 122,000 USD per QALY gained. Our estimates can provide information for considerations to introduce the CYD-TDV vaccine in Puerto Rico.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis publication was made possible with partial support from Grant Numbers TL1 TR002531 and UL1 TR002529 (A. Shekhar, PI) from the National Institutes of Health, National Center for Advancing Translational Sciences, Clinical and Translational Sciences Award. The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This study does not require ethical guidelines as it's a simulation experimentAll 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.YesThe data is publicly available

Time series data provide a crucial window into infectious disease dynamics, yet their utility is often limited by the spatially aggregated form in which they are presented. When working with time series data, violating the implicit assumption of homogeneous dynamics below the scale of spatial aggregation could bias inferences about underlying processes. We tested this assumption in the context of the 2015-2016 Zika epidemic in Colombia, where time series of weekly case reports were available at national, departmental, and municipal scales. First, we performed a descriptive analysis, which showed that the timing of departmental-level epidemic peaks varied by three months and that departmental-level estimates of the time-varying reproduction number, R(t), showed patterns that were distinct from a national-level estimate. Second, we applied a classification algorithm to six features of proportional cumulative incidence curves, which showed that variability in epidemic duration, the length of the epidemic tail, and consistency with a cumulative normal density curve made the greatest contributions to distinguishing groups. Third, we applied this classification algorithm to data simulated with a stochastic transmission model, which showed that group assignments were consistent with simulated differences in the basic reproduction number, R0. This result, along with associations between spatial drivers of transmission and group assignments based on observed data, suggests that the classification algorithm is capable of detecting differences in temporal patterns that are associated with differences in underlying drivers of incidence patterns. Overall, this diversity of temporal patterns at local scales underscores the value of spatially disaggregated time series data.

As emerging and re-emerging infectious diseases like dengue, Ebola, chikungunya, and Zika threaten new populations worldwide, officials scramble to assess local severity and transmissibility, with little to no epidemiological history to draw upon. Standard methods for assessing autochthonous (local) transmission risk make either indirect estimates based on ecological suitability or direct estimates only after local cases accumulate. However, an overlooked source of epidemiological data that can meaningfully inform risk assessments prior to outbreak emergence is the absence of transmission by imported cases. Here, we present a method for updating a priori ecological estimates of transmission risk using real-time importation data. We demonstrate our method using Zika importation and transmission data from Texas in 2016, a high-risk region in the southern United States. Our updated risk estimates are lower than previously reported, with only six counties in Texas likely to sustain a Zika epidemic, and consistent with the number of autochthonous cases detected in 2017. Importation events can thereby provide critical, early insight into local transmission risks as infectious diseases expand their global reach.

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

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

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

One in six nursing home residents and staff with positive SARS-CoV-2 tests 90 days after initial infection had specimen cycle thresholds (Ct) <30. Individuals with specimen Ct<30 were more likely to report symptoms but were not different from individuals with high Ct value specimens by other clinical and testing data.

Racial and ethnic minority patients are disproportionately affected by healthcare-associated infections (HAIs).Reference Argamany, Delgado and Reveles1–Reference Fortin-Leung and Wiley4 Patients may be at increased risk due to the underlying influence of several factors such as demographics and comorbidities. Studies of patient-level risk factors for specific HAIs have focused on racial and ethnic inequities,Reference Argamany, Delgado and Reveles1–Reference Fortin-Leung and Wiley4 but less is known about other patient-level characteristics that may place patients at greater risk of HAIs during an outbreak or facility-level factors that may place a facility at greater risk of experiencing HAI outbreaks. Additional data are needed beyond what is currently routinely collected in outbreak investigations. Are certain patients more likely to experience harm (eg, increased exposure to pathogens, infection, morbidity, or mortality) if they are in a facility experiencing an HAI outbreak? Are certain facilities (eg, based on populations served, geography, or facility type) more likely to experience HAI outbreaks? Further research is needed to better understand which patient and facility-level factors play a role in differential risk of HAI outbreaks, and collecting these additional data can help elucidate these factors.

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

In this study, we report the detection of a G6P[14] rotavirus strain from a human stool sample within the United States. The full genotype constellation of the G6P[14] strain was identified as G6-P[14]-I2-R2-C2-M2-A11-N2-T6-E2-H3.

Global sequencing and surveillance capacity for SARS-CoV-2 must be strengthened and combined with multidisciplinary studies of infectivity, virulence and immune escape, in order to track the unpredictable evolution of the ongoing COVID-19 pandemic. | | In June 2020, the World Health Organization (WHO) SARS-CoV-2 evolution working group was established to track SARS-CoV-2 variants and their specific genetic changes and to monitor viral characteristics and their impact on medical and non-medical countermeasures, including vaccines against COVID-19. In November 2021, this working group transitioned to a formal WHO Technical Advisory Group on Virus Evolution (TAG-VE), with the aim of developing and implementing a global risk-monitoring framework for SARS-CoV-2 variants, based on a multidisciplinary approach that includes in silico, virological, clinical and epidemiological data.

BACKGROUND: Beginning in October 2016, the Centers for Medicare & Medicaid Services (CMS) issued expanded guidance requiring accrediting organizations and state survey agencies to report serious infection control breaches to relevant state health departments. This project sought to characterize and summarize The Joint Commission's early experiences and findings in applying this guidance to facilities accredited under the ambulatory and office-based surgery programs in 2017. METHODS: Surveyor notes were retrospectively reviewed to identify individual breaches, and then the Centers for Disease Control and Prevention's Infection Prevention Checklist for Outpatient Settings was used to categorize and code documented breaches. RESULTS: Of 845 ambulatory organizations, 39 (4.6%) had breaches observed during the survey process and reported to health departments. Within these organizations, surveyors documented 356 breaches, representing 52 different breach codes. Common breach domains were sterilization of reusable devices, device reprocessing observation, device reprocessing, disinfection of reusable devices, and infection control program and infrastructure. Eight of the 39 facilities (20.5%) were cited for not performing the minimum level of reprocessing based on the items' intended use, reusing single-use devices, and/or not using aseptic technique to prepare injections. CONCLUSION: The CMS infection control breach reporting requirement has helped highlight some of the challenges faced by ambulatory facilities in providing a safe care environment for their patients. This analysis identified numerous opportunities for improved staff training and competencies as well as leadership oversight and investment in necessary resources. More systematic assessments of infection control practices, extending to both accredited and nonaccredited ambulatory facilities, are needed to inform oversight and prevention efforts.

The intent of this document is to highlight practical recommendations in a concise format designed to assist acute-care hospitals in implementing and prioritizing their surgical-site infection (SSI) prevention efforts. This document updates the Strategies to Prevent Surgical Site Infections in Acute Care Hospitals published in 2014. This expert guidance document is sponsored by the Society for Healthcare Epidemiology of America (SHEA). It is the product of a collaborative effort led by SHEA, the Infectious Diseases Society of America (IDSA), the Association for Professionals in Infection Control and Epidemiology (APIC), the American Hospital Association (AHA), and The Joint Commission, with major contributions from representatives of a number of organizations and societies with content expertise.

OBJECTIVE: Clinical practice guidelines are often created through collaboration among organizations. Use of inconsistent terminology may cause poor communication and delays. This study aimed to develop a glossary of terms related to collaboration in guideline development. STUDY DESIGN AND SETTING: A literature review of collaborative guidelines was performed to develop an initial list of terms related to guideline collaboration. The list of terms was presented to the members of the Guideline International Network Guidelines Collaboration Working Group, who provided presumptive definitions for each term and proposed additional terms to be included. The revised list was subsequently reviewed by an international, multidisciplinary panel of expert stakeholders. Recommendations received during this pre-Delphi review were implemented to augment an initial draft glossary. The glossary was then critically evaluated and refined through two rounds of Delphi surveys and a virtual consensus meeting with all panel members as Delphi participants. RESULTS: Forty-nine experts participated in the pre-Delphi survey and 44 participated in the two-round Delphi process. Consensus was reached for 37 terms and definitions. CONCLUSION: Uptake and utilization of this guideline collaboration glossary by key organizations and stakeholder groups may facilitate collaboration among guideline-producing organizations by improving communication, minimizing conflicts, and increasing guideline development efficiency.