Multimodeling approach to evaluating the efficacy of layering pharmaceutical and nonpharmaceutical interventions for influenza pandemics.

When an influenza pandemic emerges, temporary school closures and antiviral treatment may slow virus spread, reduce the overall disease burden, and provide time for vaccine development, distribution, and administration while keeping a larger portion of the general population infection free. The impact of such measures will depend on the transmissibility and severity of the virus and the timing and extent of their implementation. To provide robust assessments of layered pandemic intervention strategies, the Centers for Disease Control and Prevention (CDC) funded a network of academic groups to build a framework for the development and comparison of multiple pandemic influenza models. Research teams from Columbia University, Imperial College London/Princeton University, Northeastern University, the University of Texas at Austin/Yale University, and the University of Virginia independently modeled three prescribed sets of pandemic influenza scenarios developed collaboratively by the CDC and network members. Results provided by the groups were aggregated into a mean-based ensemble. The ensemble and most component models agreed on the ranking of the most and least effective intervention strategies by impact but not on the magnitude of those impacts. In the scenarios evaluated, vaccination alone, due to the time needed for development, approval, and deployment, would not be expected to substantially reduce the numbers of illnesses, hospitalizations, and deaths that would occur. Only strategies that included early implementation of school closure were found to substantially mitigate early spread and allow time for vaccines to be developed and administered, especially under a highly transmissible pandemic scenario.

Point Prevalence Estimates of Activity-Limiting Long-term Symptoms Among United States Adults ≥1 Month After Reported Severe Acute Respiratory Syndrome Coronavirus 2 Infection, 1 November 2021.

BACKGROUND: Although most adults infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) fully recover, a proportion have ongoing symptoms, or post-COVID conditions (PCC), after infection. The objective of this analysis was to estimate the number of United States (US) adults with activity-limiting PCC on 1 November 2021. METHODS: We modeled the prevalence of PCC using reported infections occurring from 1 February 2020 to 30 September 2021, and population-based, household survey data on new activity-limiting symptoms ≥1 month following SARS-CoV-2 infection. From these data sources, we estimated the number and proportion of US adults with activity-limiting PCC on 1 November 2021 as 95% uncertainty intervals, stratified by sex and age. Sensitivity analyses adjusted for underascertainment of infections and uncertainty about symptom duration. RESULTS: On 1 November 2021, at least 3.0-5.0 million US adults, or 1.2%-1.9% of the US adult population, were estimated to have activity-limiting PCC of ≥1 month's duration. Population prevalence was higher in females (1.4%-2.2%) than males. The estimated prevalence after adjusting for underascertainment of infections was 1.7%-3.8%. CONCLUSIONS: Millions of US adults were estimated to have activity-limiting PCC. These estimates can support future efforts to address the impact of PCC on the US population.

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.

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.

Modeling Effectiveness of Testing Strategies to Prevent Coronavirus Disease 2019 (COVID-19) in Nursing Homes-United States, 2020.

BACKGROUND: Identifying asymptomatic individuals early through serial testing is recommended to control coronavirus disease 2019 (COVID-19) in nursing homes, both in response to an outbreak ("outbreak testing" of residents and healthcare personnel) and in facilities without outbreaks ("nonoutbreak testing" of healthcare personnel). The effectiveness of outbreak testing and isolation with or without nonoutbreak testing was evaluated. METHODS: Using published SARS-CoV-2 transmission parameters, the fraction of SARS-CoV-2 transmissions prevented through serial testing (weekly, every 3 days, or daily) and isolation of asymptomatic persons compared with symptom-based testing and isolation was evaluated through mathematical modeling using a Reed-Frost model to estimate the percentage of cases prevented (ie, "effectiveness") through either outbreak testing alone or outbreak plus nonoutbreak testing. The potential effect of simultaneous decreases (by 10%) in the effectiveness of isolating infected individuals when instituting testing strategies was also evaluated. RESULTS: Modeling suggests that outbreak testing could prevent 54% (weekly testing with 48-hour test turnaround) to 92% (daily testing with immediate results and 50% relative sensitivity) of SARS-CoV-2 infections. Adding nonoutbreak testing could prevent up to an additional 8% of SARS-CoV-2 infections (depending on test frequency and turnaround time). However, added benefits of nonoutbreak testing were mostly negated if accompanied by decreases in infection control practice. CONCLUSIONS: When combined with high-quality infection control practices, outbreak testing could be an effective approach to preventing COVID-19 in nursing homes, particularly if optimized through increased test frequency and use of tests with rapid turnaround.

Genomic Surveillance for SARS-CoV-2 Variants Circulating in the United States, December 2020-May 2021.

SARS-CoV-2, the virus that causes COVID-19, is constantly mutating, leading to new variants (1). Variants have the potential to affect transmission, disease severity, diagnostics, therapeutics, and natural and vaccine-induced immunity. In November 2020, CDC established national surveillance for SARS-CoV-2 variants using genomic sequencing. As of May 6, 2021, sequences from 177,044 SARS-CoV-2-positive specimens collected during December 20, 2020-May 6, 2021, from 55 U.S. jurisdictions had been generated by or reported to CDC. These included 3,275 sequences for the 2-week period ending January 2, 2021, compared with 25,000 sequences for the 2-week period ending April 24, 2021 (0.1% and 3.1% of reported positive SARS-CoV-2 tests, respectively). Because sequences might be generated by multiple laboratories and sequence availability varies both geographically and over time, CDC developed statistical weighting and variance estimation methods to generate population-based estimates of the proportions of identified variants among SARS-CoV-2 infections circulating nationwide and in each of the 10 U.S. Department of Health and Human Services (HHS) geographic regions.* During the 2-week period ending April 24, 2021, the B.1.1.7 and P.1 variants represented an estimated 66.0% and 5.0% of U.S. SARS-CoV-2 infections, respectively, demonstrating the rise to predominance of the B.1.1.7 variant of concern† (VOC) and emergence of the P.1 VOC in the United States. Using SARS-CoV-2 genomic surveillance methods to analyze surveillance data produces timely population-based estimates of the proportions of variants circulating nationally and regionally. Surveillance findings demonstrate the potential for new variants to emerge and become predominant, and the importance of robust genomic surveillance. Along with efforts to characterize the clinical and public health impact of SARS-CoV-2 variants, surveillance can help guide interventions to control the COVID-19 pandemic in the United States.

Population-Level Human Secretor Status Is Associated With Genogroup 2 Type 4 Norovirus Predominance.

BACKGROUND: Noroviruses are a leading cause of acute gastroenteritis. Genogroup 2 type 4 (GII.4) has been the dominant norovirus genotype worldwide since its emergence in the mid-1990s. Individuals with a functional fucosyltransferase-2 gene, known as secretors, have increased susceptibility to GII.4 noroviruses. We hypothesized that this individual-level trait may drive GII.4 norovirus predominance at the human population level. METHODS: We conducted a systematic review for studies reporting norovirus outbreak or sporadic case genotypes and merged this with data on proportions of human secretor status in various countries from a separate systematic review. We used inverse variance-weighted linear regression to estimate magnitude of the population secretor-GII.4 proportion association. RESULTS: Two hundred nineteen genotype and 112 secretor studies with data from 38 countries were included in the analysis. Study-level GII.4 proportion among all noroviruses ranged from 0% to 100%. Country secretor proportion ranged from 43.8% to 93.9%. We observed a 0.69% (95% confidence interval, 0.19-1.18) increase in GII.4 proportion for each percentage increase in human secretor proportion, controlling for Human Development Index. CONCLUSIONS: Norovirus evolution and diversity may be driven by local population human host genetics. Our results may have vaccine development implications including whether specific antigenic formulations would be required for different populations.

Characterizing Norovirus Transmission from Outbreak Data, United States.

Norovirus is the leading cause of acute gastroenteritis outbreaks in the United States. We estimated the basic (R0) and effective (Re) reproduction numbers for 7,094 norovirus outbreaks reported to the National Outbreak Reporting System (NORS) during 2009-2017 and used regression models to assess whether transmission varied by outbreak setting. The median R0 was 2.75 (interquartile range [IQR] 2.38-3.65), and median Re was 1.29 (IQR 1.12-1.74). Long-term care and assisted living facilities had an R0 of 3.35 (95% CI 3.26-3.45), but R0 did not differ substantially for outbreaks in other settings, except for outbreaks in schools, colleges, and universities, which had an R0 of 2.92 (95% CI 2.82-3.03). Seasonally, R0 was lowest (3.11 [95% CI 2.97-3.25]) in summer and peaked in fall and winter. Overall, we saw little variability in transmission across different outbreaks settings in the United States.

The Population-Level Impacts of Excluding Norovirus-Infected Food Workers From the Workplace: A Mathematical Modeling Study.

Norovirus is the leading cause of acute gastroenteritis and foodborne disease in the United States. The Food and Drug Administration recommends that food workers infected with norovirus be excluded from the workplace while symptomatic and for 48 hours after their symptoms subside. Compliance with this recommendation is not ideal, and the population-level impacts of changes in food-worker compliance have yet to be quantified. We aimed to assess the population impacts of varying degrees of compliance with the current recommendation through the use of a compartmental model. We modeled the number and proportion of symptomatic norovirus cases averted annually in the US population (using data from 1983-2014) in specific age groups (children aged <5 years, children aged 5-17 years, adults aged 18-64 years, and adults aged ≥65 years) under various scenarios of food-worker exclusion (i.e., proportion compliant and days of postsymptomatic exclusion) in comparison with a referent scenario which assumed that 66.6¯% of norovirus-symptomatic food workers and 0% of postsymptomatic food workers were excluded from work. Overall, we estimated that 6.0 million cases of norovirus have already been avoided annually under the referent scenario and that 6.7 million (28%) more cases might be avoided through 100% compliance with the current recommendations. Substantial population-level benefits were predicted from improved compliance in exclusion of norovirus-infected food workers from the workplace-benefits that may be realized through policies or programs incentivizing self-exclusion.

Extrapolating Sentinel Surveillance Information to Estimate National COVID Hospital Admission Rates: A Bayesian Modeling Approach.

The COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) was established in March 2020 to monitor trends in hospitalizations associated with SARS-CoV-2 infection. COVID-NET is a geographically diverse population-based surveillance system for laboratory-confirmed COVID-19-associated hospitalizations with a combined catchment area covering approximately 10% of the US population. Data collected in COVID-NET includes monthly counts of hospitalizations for persons with confirmed SARS-CoV-2 infection who reside within the defined catchment area. A Bayesian modeling approach is proposed to estimate US national COVID-associated hospital admission rates based on information reported in the COVID-NET system. A key component of the approach is the ability to estimate uncertainty resulting from extrapolation of hospitalization rates observed within COVID-NET to the US population. In addition, the proposed model enables estimation of other contributors to uncertainty including temporal dependence among reported COVID-NET admission counts, the impact of unmeasured site-specific factors, and the frequency and accuracy of testing for SARS-CoV-2 infection. Based on the proposed model, an estimated 6.3 million (95% uncertainty interval (UI) 5.4-7.3 million) COVID-19-associated hospital admissions occurred in the United States from September 2020 through December 2023. Between April 2020 and December 2023, model-based monthly admission rate estimates ranged from a minimum of 1 per 10,000 population (95% UI 0.7-1.2) in June of 2023 to a highest monthly level of 16 per 10,000 (95% UI 13-19) in January 2022.

Estimating COVID-19 Hospitalizations in the United States With Surveillance Data Using a Bayesian Hierarchical Model: Modeling Study.

BACKGROUND: In the United States, COVID-19 is a nationally notifiable disease, meaning cases and hospitalizations are reported by states to the Centers for Disease Control and Prevention (CDC). Identifying and reporting every case from every facility in the United States may not be feasible in the long term. Creating sustainable methods for estimating the burden of COVID-19 from established sentinel surveillance systems is becoming more important. OBJECTIVE: We aimed to provide a method leveraging surveillance data to create a long-term solution to estimate monthly rates of hospitalizations for COVID-19. METHODS: We estimated monthly hospitalization rates for COVID-19 from May 2020 through April 2021 for the 50 states using surveillance data from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) and a Bayesian hierarchical model for extrapolation. Hospitalization rates were calculated from patients hospitalized with a lab-confirmed SARS-CoV-2 test during or within 14 days before admission. We created a model for 6 age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) separately. We identified covariates from multiple data sources that varied by age, state, and month and performed covariate selection for each age group based on 2 methods, Least Absolute Shrinkage and Selection Operator (LASSO) and spike and slab selection methods. We validated our method by checking the sensitivity of model estimates to covariate selection and model extrapolation as well as comparing our results to external data. RESULTS: We estimated 3,583,100 (90% credible interval [CrI] 3,250,500-3,945,400) hospitalizations for a cumulative incidence of 1093.9 (992.4-1204.6) hospitalizations per 100,000 population with COVID-19 in the United States from May 2020 through April 2021. Cumulative incidence varied from 359 to 1856 per 100,000 between states. The age group with the highest cumulative incidence was those aged ≥85 years (5575.6; 90% CrI 5066.4-6133.7). The monthly hospitalization rate was highest in December (183.7; 90% CrI 154.3-217.4). Our monthly estimates by state showed variations in magnitudes of peak rates, number of peaks, and timing of peaks between states. CONCLUSIONS: Our novel approach to estimate hospitalizations for COVID-19 has potential to provide sustainable estimates for monitoring COVID-19 burden as well as a flexible framework leveraging surveillance data.

Modeling strategies for the allocation of SARS-CoV-2 vaccines in the United States.

The Advisory Committee on Immunization Practices (ACIP) recommended phased allocation of SARS-CoV-2 vaccines in December 2020. To support the development of this guidance, we used a mathematical model of SARS-CoV-2 transmission to evaluate the relative impact of three vaccine allocation strategies on infections, hospitalizations, and deaths. All three strategies initially prioritized healthcare personnel (HCP) for vaccination. Strategies of subsequently prioritizing adults aged ≥65 years, or a combination of essential workers and adults aged ≥75 years, prevented the most deaths. Meanwhile, prioritizing adults with high-risk medical conditions immediately after HCP prevented the most infections. All three strategies prevented a similar fraction of hospitalizations. While no model is capable of fully capturing the complex social dynamics which shape epidemics, exercises such as this one can be a useful way for policy makers to formalize their assumptions and explore the key features of a problem before making decisions.

Estimated Number of COVID-19 Infections, Hospitalizations, and Deaths Prevented Among Vaccinated Persons in the US, December 2020 to September 2021.

Importance: The number of SARS-CoV-2 infections and COVID-19-associated hospitalizations and deaths prevented among vaccinated persons, independent of the effect of reduced transmission, is a key measure of vaccine impact. Objective: To estimate the number of SARS-CoV-2 infections and COVID-19-associated hospitalizations and deaths prevented among vaccinated adults in the US. Design, Setting, and Participants: In this modeling study, a multiplier model was used to extrapolate the number of SARS-CoV-2 infections and COVID-19-associated deaths from data on the number of COVID-19-associated hospitalizations stratified by state, month, and age group (18-49, 50-64, and ≥65 years) in the US from December 1, 2020, to September 30, 2021. These estimates were combined with data on vaccine coverage and effectiveness to estimate the risks of infections, hospitalizations, and deaths. Risks were applied to the US population 18 years or older to estimate the expected burden in that population without vaccination. The estimated burden in the US population 18 years or older given observed levels of vaccination was subtracted from the expected burden in the US population 18 years or older without vaccination (ie, counterfactual) to estimate the impact of vaccination among vaccinated persons. Exposures: Completion of the COVID-19 vaccination course, defined as 2 doses of messenger RNA (BNT162b2 or mRNA-1273) vaccines or 1 dose of JNJ-78436735 vaccine. Main Outcomes and Measures: Monthly numbers and percentages of SARS-CoV-2 infections and COVID-19-associated hospitalizations and deaths prevented were estimated among those who have been vaccinated in the US. Results: COVID-19 vaccination was estimated to prevent approximately 27 million (95% uncertainty interval [UI], 22 million to 34 million) infections, 1.6 million (95% UI, 1.4 million to 1.8 million) hospitalizations, and 235 000 (95% UI, 175 000-305 000) deaths in the US from December 1, 2020, to September 30, 2021, among vaccinated adults 18 years or older. From September 1 to September 30, 2021, vaccination was estimated to prevent 52% (95% UI, 45%-62%) of expected infections, 56% (95% UI, 52%-62%) of expected hospitalizations, and 58% (95% UI, 53%-63%) of expected deaths in adults 18 years or older. Conclusions and Relevance: These findings indicate that the US COVID-19 vaccination program prevented a substantial burden of morbidity and mortality through direct protection of vaccinated individuals.

Impact of an automated multiple emitter whole-room ultraviolet-C disinfection system on hospital acquired infections: A quasi-experimental study.

A quasi-experimental study performed in a pediatric hematology-oncology unit demonstrated that whole-room ultraviolet-C disinfection was associated with a significant reduction in hospital-onset Clostridioides difficile infection (P< .01, trend and level), but not healthcare-associated viral respiratory infections (P= .06 for trend, P= .36 for level) or central line-associated bloodstream infections (P> 0.75, trend and level).

SARS-CoV-2 Transmission From People Without COVID-19 Symptoms.

Importance: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiology of coronavirus disease 2019 (COVID-19), is readily transmitted person to person. Optimal control of COVID-19 depends on directing resources and health messaging to mitigation efforts that are most likely to prevent transmission, but the relative importance of such measures has been disputed. Objective: To assess the proportion of SARS-CoV-2 transmissions in the community that likely occur from persons without symptoms. Design, Setting, and Participants: This decision analytical model assessed the relative amount of transmission from presymptomatic, never symptomatic, and symptomatic individuals across a range of scenarios in which the proportion of transmission from people who never develop symptoms (ie, remain asymptomatic) and the infectious period were varied according to published best estimates. For all estimates, data from a meta-analysis was used to set the incubation period at a median of 5 days. The infectious period duration was maintained at 10 days, and peak infectiousness was varied between 3 and 7 days (-2 and +2 days relative to the median incubation period). The overall proportion of SARS-CoV-2 was varied between 0% and 70% to assess a wide range of possible proportions. Main Outcomes and Measures: Level of transmission of SARS-CoV-2 from presymptomatic, never symptomatic, and symptomatic individuals. Results: The baseline assumptions for the model were that peak infectiousness occurred at the median of symptom onset and that 30% of individuals with infection never develop symptoms and are 75% as infectious as those who do develop symptoms. Combined, these baseline assumptions imply that persons with infection who never develop symptoms may account for approximately 24% of all transmission. In this base case, 59% of all transmission came from asymptomatic transmission, comprising 35% from presymptomatic individuals and 24% from individuals who never develop symptoms. Under a broad range of values for each of these assumptions, at least 50% of new SARS-CoV-2 infections was estimated to have originated from exposure to individuals with infection but without symptoms. Conclusions and Relevance: In this decision analytical model of multiple scenarios of proportions of asymptomatic individuals with COVID-19 and infectious periods, transmission from asymptomatic individuals was estimated to account for more than half of all transmissions. In addition to identification and isolation of persons with symptomatic COVID-19, effective control of spread will require reducing the risk of transmission from people with infection who do not have symptoms. These findings suggest that measures such as wearing masks, hand hygiene, social distancing, and strategic testing of people who are not ill will be foundational to slowing the spread of COVID-19 until safe and effective vaccines are available and widely used.

Estimating under-recognized COVID-19 deaths, United States, march 2020-may 2021 using an excess mortality modelling approach.

BACKGROUND: In the United States, Coronavirus Disease 2019 (COVID-19) deaths are captured through the National Notifiable Disease Surveillance System and death certificates reported to the National Vital Statistics System (NVSS). However, not all COVID-19 deaths are recognized and reported because of limitations in testing, exacerbation of chronic health conditions that are listed as the cause of death, or delays in reporting. Estimating deaths may provide a more comprehensive understanding of total COVID-19-attributable deaths. METHODS: We estimated COVID-19 unrecognized attributable deaths, from March 2020-April 2021, using all-cause deaths reported to NVSS by week and six age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) for 50 states, New York City, and the District of Columbia using a linear time series regression model. Reported COVID-19 deaths were subtracted from all-cause deaths before applying the model. Weekly expected deaths, assuming no SARS-CoV-2 circulation and predicted all-cause deaths using SARS-CoV-2 weekly percent positive as a covariate were modelled by age group and including state as a random intercept. COVID-19-attributable unrecognized deaths were calculated for each state and age group by subtracting the expected all-cause deaths from the predicted deaths. FINDINGS: We estimated that 766,611 deaths attributable to COVID-19 occurred in the United States from March 8, 2020-May 29, 2021. Of these, 184,477 (24%) deaths were not documented on death certificates. Eighty-two percent of unrecognized deaths were among persons aged ≥65 years; the proportion of unrecognized deaths were 0•24-0•31 times lower among those 0-17 years relative to all other age groups. More COVID-19-attributable deaths were not captured during the early months of the pandemic (March-May 2020) and during increases in SARS-CoV-2 activity (July 2020, November 2020-February 2021). INTERPRETATION: Estimating COVID-19-attributable unrecognized deaths provides a better understanding of the COVID-19 mortality burden and may better quantify the severity of the COVID-19 pandemic. FUNDING: None.

Cost-effectiveness of pediatric norovirus vaccination in daycare settings.

OBJECTIVE: Noroviruses are the leading cause of acute gastroenteritis in the United States and outbreaks frequently occur in daycare settings. Results of norovirus vaccine trials have been promising, however there are open questions as to whether vaccination of daycare children would be cost-effective. We investigated the incremental cost-effectiveness of a hypothetical norovirus vaccination for children in daycare settings compared to no vaccination. METHODS: We conducted a model-based cost-effectiveness analysis using a disease transmission model of children attending daycare. Vaccination with a 90% coverage rate in addition to the observed standard of care (exclusion of symptomatic children from daycare) was compared to the observed standard of care. The main outcomes measures were infections and deaths averted, quality-adjusted life years (QALYs), costs, and incremental cost-effectiveness ratio (ICER). Cost-effectiveness was analyzed from a societal perspective, including medical costs to children as well as productivity losses of parents, over a two-year time horizon. Data sources included outbreak surveillance data and published literature. RESULTS: A 50% efficacious norovirus vaccine averts 571.83 norovirus cases and 0.003 norovirus-related deaths per 10,000 children compared to the observed standard of care. A $200 norovirus vaccine that is 50% efficacious has a net cost increase of $178.10 per child and 0.025 more QALYs, resulting in an ICER of $7,028/QALY. Based on the probabilistic sensitivity analysis, we estimated that a $200 vaccination with 50% efficacy was 94.0% likely to be cost-effective at a willingness-to-pay of $100,000/QALY threshold and 95.3% likely at a $150,000/QALY threshold. CONCLUSION: Due to the large disease burden associated with norovirus, it is likely that vaccinating children in daycares could be cost-effective, even with modest vaccine efficacy and a high per-child cost of vaccination. Norovirus vaccination of children in daycare has a cost-effectiveness ratio similar to other commonly recommended childhood vaccines.

Disease burden and seasonal impact of improving rotavirus vaccine coverage in the United States: A modeling study.

BACKGROUND: Prior to vaccine introduction in 2006, rotavirus was the leading cause of severe diarrhea in children under five years of age in the U.S. Vaccination of infants has led to major reductions in disease burden, a shift in the seasonal peak and the emergence of a biennial pattern of disease. However, rotavirus vaccine coverage has remained relatively low (70-75%) compared to other infant immunizations in the U.S. Part of the reason for this lower coverage is that children whose care is provided by family practitioners (FP) have considerably lower probability of being vaccinated compared to those seen be pediatricians (PE). We used a dynamic transmission model to assess the impact of improving rotavirus vaccine coverage by FP and/or PE on rotavirus gastroenteritis (RVGE) incidence and seasonal patterns. METHODS: A deterministic age-structured dynamic model with susceptible, infectious, and recovered compartments (SIRS model) was used to simulate rotavirus transmission and vaccination. We estimated the reduction of RVGE cases by 2 doses of rotavirus vaccine with three vaccination scenarios: (Status Quo: 85% coverage by pediatricians and 45% coverage by family practitioners; Improved FP: 85% coverage by pediatricians and family practitioners; Improved FP+PE: 95% coverage by pediatricians and family practitioners). In addition, we tested the sensitivity of the model to the assumption of random mixing patterns between children visiting pediatricians and children visiting family practitioners. RESULTS: In this model, higher vaccine coverage provided by family practitioners and pediatricians leads to lower incidence of severe RVGE cases (23% averted in Improved FP and 57% averted in Improved FP+PE compared to Status Quo) including indirect effects. One critical impact of higher total vaccine coverage is the effect on rotavirus epidemic patterns in the U.S.; the biennial rotavirus epidemic patterns shifted to reduced annual epidemic patterns. Additionally, assortative mixing patterns in children visiting pediatricians and family practitioners amplify the impact of increasing vaccine coverage. CONCLUSION: Other high-income countries that introduced vaccine have not experienced biennial patterns, like the U.S. Our results suggest that increasing overall vaccine coverage to 85% among infants would lead to an overall reduction in incidence with annual epidemic patterns.

Snow Mountain Virus recovery by synthetic human histo-blood group antigens is heavily influenced by matrix effects.

Noroviruses are known to bind to histo-blood group antigens (HBGAs) and the specific binding patterns depend on the virus genotype. However, the development of point-of-care diagnostic assays based on this binding has been challenging due to low assay sensitivity. This study utilized a well-defined stool collection from a GII.2 Snow Mountain Virus (SMV) human challenge study to investigate virus recovery from stool and emesis samples using HBGA-coated beads. SMV was recovered from H type III-coated beads for 13 stool specimens out of 27 SMV-positive specimens tested. After adjusting for non-specific binding to PEG-coated beads, the mean percent recovery by H type III-coated beads was 308.11% +/- 861.61. Recovery by H type III ligands was subject-specific and weakly correlated with stool consistency. Input virus titer was not correlated with SMV recovery. The results suggest that the generally low virus recovery we observed may be due to bead saturation or hindrance by existing glycans in the matrix that precluded the virus from being captured by the synthetic glycans. These results indicate a strong role for subject-specific and matrix effects in HBGA binding by SMV. Further investigation of the nature of this interference is needed to facilitate development of high sensitivity diagnostic assays.

Mutagenic and recombinagenic responses to defective DNA polymerase delta are facilitated by the Rev1 protein in pol3-t mutants of Saccharomyces cerevisiae.

Defective DNA replication can result in substantial increases in the level of genome instability. In the yeast Saccharomyces cerevisiae, the pol3-t allele confers a defect in the catalytic subunit of replicative DNA polymerase delta that results in increased rates of mutagenesis, recombination, and chromosome loss, perhaps by increasing the rate of replicative polymerase failure. The translesion polymerases Pol eta, Pol zeta, and Rev1 are part of a suite of factors in yeast that can act at sites of replicative polymerase failure. While mutants defective in the translesion polymerases alone displayed few defects, loss of Rev1 was found to suppress the increased rates of spontaneous mutation, recombination, and chromosome loss observed in pol3-t mutants. These results suggest that Rev1 may be involved in facilitating mutagenic and recombinagenic responses to the failure of Pol delta. Genome stability, therefore, may reflect a dynamic relationship between primary and auxiliary DNA polymerases.

Caution Needed: Molecular Diagnosis of Pediatric Group A Streptococcal Pharyngitis.

Among throat swabs processed in the microbiology laboratory as back-up for negative rapid antigen detection test results, we found a significant increase in the proportion that tested positive for group A streptococci after changing from throat culture to a molecular test.For group A streptococcus testing, our hospital laboratory replaced throat cultures with a stand-alone molecular diagnostic test that takes no more than 1 hour to perform. The prevalence of positive laboratory test results increased significantly (P < .0001) after the change to molecular testing, probably because of the extreme sensitivity of the molecular test.