Rocky Mountain spotted fever (RMSF) is a deadly tick-borne disease caused by the bacterium Rickettsia rickettsii. An ongoing epidemic of RMSF is affecting tribal communities in Arizona, with nearly 500 cases and 28 deaths since 2003. The San Carlos Apache Tribe has been consistently working to prevent RMSF using tick collars on dogs, pesticide treatments around homes, and increasing education for nearly a decade. Besides monitoring human disease levels and tick burden on dogs, we have little understanding of the long-term impact of prevention practices on tick abundance and infection rates in the peridomestic environment. We evaluated risk factors associated for tick infestation at home sites across the San Carlos Indian Reservation as well as R. rickettsii and Rickettsia massiliae prevalence in off-host ticks. Although the presence of fencing appears protective, the number of nearby structures is the most important risk factor associated with increased adult and nymphal tick abundance, highlighting the impact of a free-roaming dog population.

More than 85% of US adults had been infected with SARS-CoV-2 by the end of 2023. Continued serosurveillance of transmission and assessments of correlates of protection require robust detection of reinfections. We developed a serologic method for identifying reinfections in longitudinal blood donor data by assessing nucleocapsid (N) antibody boosting using a total immunoglobulin assay. Receiver operating characteristic curve analysis yielded an optimal ratio of >1.43 (sensitivity 87.1%, specificity 96.0%). When prioritizing specificity, a ratio of >2.33 was optimal (sensitivity 75.3%, specificity 99.3%). In donors with higher anti-N reactivity levels before reinfection, sensitivity was reduced. Sensitivity could be improved by expanding the dynamic range of the assay through dilutional testing, from 38.8% to 66.7% in the highest reactivity group (signal-to-cutoff ratio before reinfection >150). This study demonstrated that longitudinal testing for N antibodies can be used to identify reinfections and estimate total infection incidence in a blood donor cohort.

Powassan virus (POWV) and Eastern equine encephalitis virus (EEEV) are regionally endemic arboviruses in the United States that can cause neuroinvasive disease and death. Recent identification of EEEV transmission through organ transplantation and POWV transmission through blood transfusion have increased concerns about infection risk. After historically high numbers of cases of both viruses were reported in 2019, we conducted a seroprevalence survey using blood donation samples from selected endemic counties. Specimens were screened for virus-specific neutralizing antibodies, and population seroprevalence was estimated using weights calibrated to county population census data. For POWV, median county seroprevalence in 4 states was 0.84%, ranging from 0% (95% CI 0%-2.28%) to 11.5% (95% CI 0.82%-40.9%). EEEV infection was identified in a single county (estimated seroprevalence 1.62% [95% CI 0.04%-8.75%]). Although seroprevalence estimates in sampled areas were generally low, additional investigation of higher-prevalence areas could inform risk for transmission from asymptomatic blood and organ donors.

BACKGROUND: Seasonal influenza causes an estimated 120 000 to 710 000 hospitalizations annually in the United States. Treatment with antiviral medications, such as oseltamivir, can reduce risks of hospitalization among people with influenza-associated illness. The US Centers for Disease Control and Prevention recommends initiating antiviral treatment as soon as possible for outpatients with suspected or confirmed influenza who have severe or progressive illness or are at higher risk of influenza complications. METHODS: We developed a probabilistic model to estimate the impact of antiviral treatment in reducing hospitalizations among US outpatients with influenza. Parameters were informed by seasonal influenza surveillance platforms and stratified by age group and whether individuals had a condition associated with higher risk of influenza complications. We modeled different scenarios for influenza antiviral effectiveness and outpatient testing and prescribing practices, then compared our results with a baseline scenario in which antivirals were not used. RESULTS: Across the modeled scenarios, antiviral treatment resulted in 1215 to 14 184 fewer influenza-associated hospitalizations on average when compared with the baseline scenario (0.2%-2.7% reduction). The greatest effects occurred among adults aged ≥65 years and individuals with conditions associated with higher risk of influenza complications. Modeling 50% improvements in access to care, testing, prescribing, and treatment resulted in greater potential impacts, with over 71 000 (13.3%) influenza-associated hospitalizations averted on average compared to baseline. CONCLUSIONS: Our results support recommendations to prioritize outpatient antiviral treatment among older adults and others at higher risk of influenza complications. Improving access to prompt testing and treatment among outpatients with suspected influenza could reduce hospitalizations substantially.

Nirsevimab is a long-acting monoclonal antibody that protects infants and young children against severe respiratory syncytial virus (RSV) disease. Children are eligible for one 50 mg dose, one 100 mg dose, or two 100 mg doses of nirsevimab based on age, weight, time of year, maternal vaccination, and risk of severe disease. In winter 2023/2024, we developed a model to project the number of nirsevimab doses needed to immunize all eligible U.S. children during the 2024/2025 season. We grouped all births from March 2023 through March 2025 into weekly cohorts, partitioned those cohorts based on eligibility criteria, and computed eligibility for each partition. In the absence of maternal RSV vaccination, we estimated U.S. children would be eligible to receive 4.3 million nirsevimab doses in 2024/2025, of which 48% would be 100 mg doses. Projections of total eligibility can be used to inform production goals and avoid shortages of nirsevimab.

BACKGROUND: Jamestown Canyon virus, a mosquito-borne virus, can cause asymptomatic infection, febrile illness, or neuroinvasive disease in humans. Previous studies have found Jamestown Canyon virus-specific antibodies in a 4-54% of people in various U.S. regions. To understand baseline seroprevalence in regions with the highest number of reported disease cases, we performed a serosurvey among blood donors. METHODS: We randomly selected blood donation specimens collected during December 2019-April 2020 from residents of counties reporting ≥2 disease cases in 2019 or one case in 2019 and ≥1 case during 2010-2018. Specimens were screened for Jamestown Canyon virus-specific neutralizing antibodies and, if positive, tested for IgM antibodies. We estimated county population seroprevalence by calibrating sample weights to population census data. RESULTS: Fourteen counties in three states, Massachusetts, Minnesota, and Wisconsin, met the inclusion criteria. Within each state, average county seroprevalence ranged from 16.8% (95% CI: 9.3%-27.0%) to 18.8% (95% CI: 14.0%-24.4%) for Jamestown Canyon virus neutralizing antibodies and from 7.6% (95% CI: 4.2%-12.5%) to 13.5% (95% CI: 9.6%-18.3%) for both neutralizing and IgM antibodies. CONCLUSIONS: Estimated Jamestown Canyon virus seroprevalence, including for IgM antibodies, is elevated in endemic areas, complicating the interpretation of serologic testing in diagnosing acute disease in symptomatic individuals. Diagnosing Jamestown Canyon virus disease requires a high degree of clinical suspicion, ruling out other possible causes of illness, and if possible, collecting acute and convalescent samples. New assays to detect acute infection could improve diagnosis and public health surveillance for Jamestown Canyon virus disease.

The generation time, representing the interval between infections in primary and secondary cases, is essential for understanding and predicting the transmission dynamics of seasonal influenza, including the real-time effective reproduction number (Rt). However, comprehensive generation time estimates for seasonal influenza, especially since the 2009 influenza pandemic, are lacking. We estimated the generation time utilizing data from a 7-site case-ascertained household study in the United States over two influenza seasons, 2021/2022 and 2022/2023. More than 200 individuals who tested positive for influenza and their household contacts were enrolled within 7 days of the first illness in the household. All participants were prospectively followed for 10 days, completing daily symptom diaries and collecting nasal swabs, which were then tested for influenza via RT-PCR. We analyzed these data by modifying a previously published Bayesian data augmentation approach that imputes infection times of cases to obtain both intrinsic (assuming no susceptible depletion) and realized (observed within household) generation times. We assessed the robustness of the generation time estimate by varying the incubation period, and generated estimates of the proportion of transmission occurring before symptomatic onset, the infectious period, and the latent period. We estimated a mean intrinsic generation time of 3.2 (95 % credible interval, CrI: 2.9-3.6) days, with a realized household generation time of 2.8 (95 % CrI: 2.7-3.0) days. The generation time exhibited limited sensitivity to incubation period variation. Estimates of the proportion of transmission that occurred before symptom onset, the infectious period, and the latent period were sensitive to variations in the incubation period. Our study contributes to the ongoing efforts to refine estimates of the generation time for influenza. Our estimates, derived from recent data following the COVID-19 pandemic, are consistent with previous pre-pandemic estimates, and will be incorporated into real-time Rt estimation efforts.

Plague is a rare, potentially fatal flea-borne zoonosis endemic in the western United States. A previous model described interannual variation in human cases based on temperature and lagged precipitation. We recreated this model in northeastern Arizona (1960-1997) to evaluate its capacity to predict recent cases (1998-2022). In recreating the original model, we found that future instead of concurrent temperature had inadvertently been used for the presented fit. Prediction from our revised models with lagged precipitation and temporally plausible temperature relationships aligned with low observed cases in 1998-2022. Elevated precipitation associated with high cases in historical data (>6 inches combined precipitation over two previous springs) was only observed once in the last quarter century, so we could not assess if these conditions were reliably associated with elevated (four or more) human plague cases. Observed weather conditions were similar to those previously associated with low (fewer than or equal to two) case counts, suggesting "baseline" conditions in the last quarter century.

BACKGROUND: Highly pathogenic avian influenza A(H5N1) viruses have caused widespread infections in dairy cows and poultry in the United States, with sporadic human cases. We describe characteristics of human A(H5N1) cases identified from March through October 2024 in the United States. METHODS: We analyzed data from persons with laboratory-confirmed A(H5N1) virus infection using a standardized case-report form linked to laboratory results from the Centers for Disease Control and Prevention influenza A/H5 subtyping kit. RESULTS: Of 46 case patients, 20 were exposed to infected poultry, 25 were exposed to infected or presumably infected dairy cows, and 1 had no identified exposure; that patient was hospitalized with nonrespiratory symptoms, and A(H5N1) virus infection was detected through routine surveillance. Among the 45 case patients with animal exposures, the median age was 34 years, and all had mild A(H5N1) illness; none were hospitalized, and none died. A total of 42 patients (93%) had conjunctivitis, 22 (49%) had fever, and 16 (36%) had respiratory symptoms; 15 (33%) had conjunctivitis only. The median duration of illness among 16 patients with available data was 4 days (range, 1 to 8). Most patients (87%) received oseltamivir; oseltamivir was started a median of 2 days after symptom onset. No additional cases were identified among the 97 household contacts of case patients with animal exposures. The types of personal protective equipment (PPE) that were most commonly used by workers exposed to infected animals were gloves (71%), eye protection (60%), and face masks (47%). CONCLUSIONS: In the cases identified to date, A(H5N1) viruses generally caused mild illness, mostly conjunctivitis, of short duration, predominantly in U.S. adults exposed to infected animals; most patients received prompt antiviral treatment. No evidence of human-to-human A(H5N1) transmission was identified. PPE use among occupationally exposed persons was suboptimal, which suggests that additional strategies are needed to reduce exposure risk. (Funded by the Centers for Disease Control and Prevention.).

Cache Valley virus (CVV), a mosquito-borne orthobunyavirus, causes epizootics in ruminants characterized by congenital malformations and fetal death in North America. Only seven human infections have been identified; limited information exists on its potential as a human teratogen. Diagnosis of CVV infections relies on the plaque reduction neutralization test (PRNT), which requires live virus, is time-consuming, and cannot differentiate between recent and past infections. To improve diagnostics for CVV, we developed an IgM antibody capture ELISA (MAC-ELISA) for detection of anti-CVV human IgM in diagnostic specimens that can be performed faster than PRNT and is specific to IgM, which is essential to determine the timing of infection. Conjointly, a cell line constitutively expressing human-murine chimeric antibody with the variable regions of monoclonal antibody CVV-17 and constant regions of human IgM was developed to provide positive control material. The new cell line produced antibody with reactivity in the assay equivalent to that of a human serum sample positive for anti-CVV IgM. Five of seven archived human specimens diagnostically confirmed as CVV positive tested positive in the MAC-ELISA, whereas 44 specimens confirmed positive for another arboviral infection tested negative, showing good initial correlation of the CVV MAC-ELISA. Two of 27 previously collected serum samples from febrile patients in Yucatán, Mexico, who tested negative for a recent flaviviral or alphaviral infection were positive in both the MAC-ELISA and PRNT, indicating a possible recent infection with CVV or related orthobunyavirus. The MAC-ELISA described here will aid in making diagnostics more widely available for CVV in public health laboratories.

BACKGROUND: COVID-19 convalescent plasma (CCP) remains a treatment option for immunocompromised patients; however, the current FDA qualification threshold of ≥200 BAU/mL of spike antibody appears to be relatively low. We evaluated the levels of binding (bAb) and neutralizing antibodies (nAb) on serial samples from repeat blood donors who were vaccinated and/or infected to inform criteria for qualifying CCP from routinely collected plasma components. METHODS: Donors were categorized into four groups: (1) infected, then vaccinated, (2) vaccinated then infected during the delta, or (3) omicron waves, (4) vaccinated without infection. IgG Spike and total Nuclecapsid bAb were measured, along with S variants and nAb titers using reporter viral particle neutralization. RESULTS: Mean S IgG bAb peaks after infection alone were lower than after primary and booster vaccinations, and higher after delta and omicron infection in previously vaccinated donors. Half-lives for S IgG ranged from 34 to 66 days after first infection/vaccination events and up to 108 days after second events. The levels of S IgG bAb and nAb were similar across different variants, except for omicron, which were lower. Better correlations of nAb with bAb were observed at higher levels (hybrid immunity) than at the current FDA CCP qualifying threshold. DISCUSSION: Routine plasma donations from donors with hybrid immunity had high S bAb and potent neutralizing activity for 3-6 months after infection. In donations with high (>4000 BAU/mL) S IgG, >95% had high nAb titers (>500) against ancestral and variant S, regardless of COVID-19 symptoms. These findings provide the basis for test-based criteria for qualifying CCP from routine blood donations.

As part of the response to the highly pathogenic avian influenza A(H5N1) virus outbreak in U.S. cattle and poultry and the associated human cases, CDC and partners are monitoring influenza A virus levels and detection of the H5 subtype in wastewater. Among 48 states and the District of Columbia that performed influenza A testing of wastewater during May 12-July 13, 2024, a weekly average of 309 sites in 38 states had sufficient data for analysis, and 11 sites in four states reported high levels of influenza A virus. H5 subtype testing was conducted at 203 sites in 41 states, with H5 detections at 24 sites in nine states. For each detection or high level, CDC and state and local health departments evaluated data from other influenza surveillance systems and partnered with wastewater utilities and agriculture departments to investigate potential sources. Among the four states with high influenza A virus levels detected in wastewater, three states had corresponding evidence of human influenza activity from other influenza surveillance systems. Among the 24 sites with H5 detections, 15 identified animal sources within the sewershed or adjacent county, including eight milk-processing inputs. Data from these early investigations can help health officials optimize the use of wastewater surveillance during the upcoming respiratory illness season.

Understanding whether influenza vaccine promotion strategies produce community-wide indirect effects is important for establishing vaccine coverage targets and optimizing vaccine delivery. Empirical epidemiologic studies and mathematical models have been used to estimate indirect effects of vaccines but rarely for the same estimand in the same dataset. Using these approaches together could be a powerful tool for triangulation in infectious disease epidemiology because each approach is subject to distinct sources of bias. We triangulated evidence about indirect effects from a school-located influenza vaccination program using two approaches: a difference-in-difference (DID) analysis, and an age-structured, deterministic, compartmental model. The estimated indirect effect was substantially lower in the mathematical model than in the DID analysis (2.1% (95% Bayesian credible intervals 0.4 - 4.4%) vs. 22.3% (95% CI 7.6% - 37.1%)). To explore reasons for differing estimates, we used sensitivity analyses and probabilistic bias analyses. When we constrained model parameters such that projections matched the DID analysis, results only aligned with the DID analysis with substantially lower pre-existing immunity among school-age children and older adults. Conversely, DID estimates corrected for potential bias only aligned with mathematical model estimates under differential outcome misclassification. We discuss how triangulation using empirical and mathematical modelling approaches could strengthen future studies.

Isolation of symptomatic infectious persons can reduce influenza transmission. However, virus shedding that occurs without symptoms will be unaffected by such measures. Identifying effective isolation strategies for influenza requires understanding the interplay between individual virus shedding and symptom presentation. From 2017 to 2020, we conducted a case-ascertained household transmission study using influenza real-time RT-qPCR testing of nasal swabs and daily symptom diary reporting for up to 7 days after enrolment (≤14 days after index onset). We assumed real-time RT-qPCR cycle threshold (Ct) values were indicators of quantitative virus shedding and used symptom diaries to create a score that tracked influenza-like illness (ILI) symptoms (fever, cough, or sore throat). We fit phenomenological nonlinear mixed-effects models stratified by age and vaccination status and estimated two quantities influencing isolation effectiveness: shedding before symptom onset and shedding that might occur once isolation ends. We considered different isolation end points (including 24 h after fever resolution or 5 days after symptom onset) and assumptions about the infectiousness of Ct shedding trajectories. Of the 116 household contacts with ≥2 positive tests for longitudinal analyses, 105 (91%) experienced ≥1 ILI symptom. On average, children <5 years experienced greater peak shedding, longer durations of shedding, and elevated ILI symptom scores compared with other age groups. Most individuals (63/105) shed <10% of their total shed virus before symptom onset, and shedding after isolation varied substantially across individuals, isolation end points, and infectiousness assumptions. Our results can inform strategies to reduce transmission from symptomatic individuals infected with influenza.

Asymptomatic influenza virus infection occurs but may vary by factors such as age, influenza vaccination status, or influenza season. We examined the frequency of influenza virus infection and associated symptoms using data from two case-ascertained household transmission studies (conducted from 2017-2023) with prospective, systematic collection of respiratory specimens and symptoms. From the 426 influenza virus infected household contacts that met our inclusion criteria, 8% were asymptomatic, 6% had non-respiratory symptoms, 23% had acute respiratory symptoms, and 62% had influenza-like illness symptoms. Understanding the prevalence of asymptomatic and mildly symptomatic influenza cases is important for implementing effective influenza prevention strategies and enhancing the effectiveness of symptom-based surveillance systems.

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.

Nucleocapsid antibody assays can be used to estimate SARS-CoV-2 infection prevalence in regions implementing spike-based COVID-19 vaccines. However, poor sensitivity of nucleocapsid antibody assays in detecting infection after vaccination has been reported. We derived a lower cutoff for identifying previous infections in a large blood donor cohort (N = 142,599) by using the Ortho VITROS Anti-SARS-CoV-2 Total-N Antibody assay, improving sensitivity while maintaining specificity >98%. We validated sensitivity in samples donated after self-reported swab-confirmed infections diagnoses. Sensitivity for first infections in unvaccinated donors was 98.1% (95% CI 98.0-98.2) and for infection after vaccination was 95.6% (95% CI 95.6-95.7) based on the standard cutoff. Regression analysis showed sensitivity was reduced in the Delta compared with Omicron period, in older donors, in asymptomatic infections, <30 days after infection, and for infection after vaccination. The standard Ortho N antibody threshold demonstrated good sensitivity, which was modestly improved with the revised cutoff.

Group testing is the process of testing items as an amalgamation, rather than separately, to determine the binary status for each item. Its use was especially important during the COVID-19 pandemic through testing specimens for SARS-CoV-2. The adoption of group testing for this and many other applications is because members of a negative testing group can be declared negative with potentially only one test. This subsequently leads to significant increases in laboratory testing capacity. Whenever a group testing algorithm is put into practice, it is critical for laboratories to understand the algorithm's operating characteristics, such as the expected number of tests. Our paper presents the binGroup2 package that provides the statistical tools for this purpose. This R package is the first to address the identification aspect of group testing for a wide variety of algorithms. We illustrate its use through COVID-19 and chlamydia/gonorrhea applications of group testing.

BACKGROUND: Novel influenza viruses pose a potential pandemic risk, and rapid detection of infections in humans is critical to characterizing the virus and facilitating the implementation of public health response measures. METHODS: We use a probabilistic framework to estimate the likelihood that novel influenza virus cases would be detected through testing in different community and healthcare settings (urgent care, emergency department, hospital, and intensive care unit [ICU]) while at low frequencies in the United States. Parameters were informed by data on seasonal influenza virus activity and existing testing practices. RESULTS: In a baseline scenario reflecting the presence of 100 novel virus infections with similar severity to seasonal influenza viruses, the median probability of detecting at least one infection per month was highest in urgent care settings (72%) and when community testing was conducted at random among the general population (77%). However, urgent care testing was over 15 times more efficient (estimated as the number of cases detected per 100,000 tests) due to the larger number of tests required for community testing. In scenarios that assumed increased clinical severity of novel virus infection, median detection probabilities increased across all healthcare settings, particularly in hospitals and ICUs (up to 100%) where testing also became more efficient. CONCLUSIONS: Our results suggest that novel influenza virus circulation is likely to be detected through existing healthcare surveillance, with the most efficient testing setting impacted by the disease severity profile. These analyses can help inform future testing strategies to maximize the likelihood of novel influenza detection.

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.

We reviewed the tools that have been developed to characterize and communicate seasonal influenza activity in the United States. Here we focus on systematic surveillance and applied analytics, including seasonal burden and disease severity estimation, short-term forecasting, and longer-term modeling efforts. For each set of activities, we describe the challenges and opportunities that have arisen because of the COVID-19 pandemic. In conclusion, we highlight how collaboration and communication have been and will continue to be key components of reliable and actionable influenza monitoring, forecasting, and modeling activities.

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.

BACKGROUND: Alpha-gal syndrome (AGS) is an allergy to galactose-α-1,3-galactose (alpha-gal), a carbohydrate found in most mammals. Evidence indicates that AGS develops following a tick bite, and in the United States, AGS is most associated with bites from Amblyomma americanum (lone star tick); however, not all persons bitten by ticks develop clinical AGS. OBJECTIVE: This study investigated intrinsic risk factors associated with the development of AGS. METHODS: We performed a case-control study among adults presenting for diagnosis or management of AGS at an allergy clinic in North Carolina during 2019-2020 and compared them to controls enrolled from two nearby internal medicine clinics. A questionnaire gathered epidemiologic and tick exposure data and blood was obtained for alpha-gal specific IgE (sIgE) and other testing. RESULTS: The 82 enrolled case patients and 191 controls did not differ significantly by age or sex. Case patients were more likely than controls to have A or O blood types (non-B-antigen), have experienced childhood allergies, and have a family history of AGS and other food allergies. Case patients were also more likely to report experiencing long healing times for insect bites or stings and a family history of allergy to stinging or biting insects. CONCLUSION: This study suggests that intrinsic factors contribute to risk of developing AGS. Some traits are genetic, but common behaviors among households and family units likely also contribute. Identification of these risk factors can inform personal risk, aid healthcare providers in understanding susceptible populations, and contribute to ongoing understanding of AGS epidemiology.

Before the COVID-19 pandemic, the role of asymptomatic influenza virus infections in influenza transmission was uncertain. However, the importance of asymptomatic infection with SARS-CoV-2 for onward transmission of COVID-19 has led experts to question whether the role of asymptomatic influenza virus infections in transmission had been underappreciated. We discuss the existing evidence on the frequency of asymptomatic influenza virus infections, the extent to which they contribute to infection transmission, and remaining knowledge gaps. We propose priority areas for further evaluation, study designs, and case definitions to address existing knowledge gaps.

BACKGROUND: Antiviral chemoprophylaxis is recommended for use during influenza outbreaks in nursing homes to prevent transmission and severe disease among non-ill residents. Centers for Disease Control and Prevention (CDC) guidance recommends prophylaxis be initiated for all non-ill residents once an influenza outbreak is detected and be continued for at least 14 days and until seven days after the last laboratory-confirmed influenza case is identified. However, not all facilities strictly adhere to this guidance and the impact of such partial adherence is not fully understood. METHODS: We developed a stochastic compartmental framework to model influenza transmission within an average-sized U.S. nursing home. We compared the number of symptomatic illnesses and hospitalizations under varying prophylaxis implementation strategies, in addition to different levels of prophylaxis uptake and adherence by residents and healthcare personnel (HCP). RESULTS: Prophylaxis implemented according to current guidance reduced total symptomatic illnesses and hospitalizations among residents by an average of 12% and 36%, respectively, compared with no prophylaxis. We did not find evidence that alternative implementations of prophylaxis were more effective: compared to full adoption of current guidance, partial adoption resulted in increased symptomatic illnesses and/or hospitalizations, and longer or earlier adoption offered no additional improvements. In addition, increasing uptake and adherence among nursing home residents was effective in reducing resident illnesses and hospitalizations, but increasing HCP uptake had minimal indirect impacts for residents. CONCLUSIONS: The greatest benefits of influenza prophylaxis during nursing home outbreaks will likely be achieved through increasing uptake and adherence among residents and following current CDC guidance.

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

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

During the 2022-23 influenza season, early increases in influenza activity, co-circulation of influenza with other respiratory viruses, and high influenza-associated hospitalization rates, particularly among children and adolescents, were observed. This report describes the 2022-23 influenza season among children and adolescents aged <18 years, including the seasonal severity assessment; estimates of U.S. influenza-associated medical visits, hospitalizations, and deaths; and characteristics of influenza-associated hospitalizations. The 2022-23 influenza season had high severity among children and adolescents compared with thresholds based on previous seasons' influenza-associated outpatient visits, hospitalization rates, and deaths. Nationally, the incidences of influenza-associated outpatient visits and hospitalization for the 2022-23 season were similar for children aged <5 years and higher for children and adolescents aged 5-17 years compared with previous seasons. Peak influenza-associated outpatient and hospitalization activity occurred in late November and early December. Among children and adolescents hospitalized with influenza during the 2022-23 season in hospitals participating in the Influenza Hospitalization Surveillance Network, a lower proportion were vaccinated (18.3%) compared with previous seasons (35.8%-41.8%). Early influenza circulation, before many children and adolescents had been vaccinated, might have contributed to the high hospitalization rates during the 2022-23 season. Among symptomatic hospitalized patients, receipt of influenza antiviral treatment (64.9%) was lower than during pre-COVID-19 pandemic seasons (80.8%-87.1%). CDC recommends that all persons aged ≥6 months without contraindications should receive the annual influenza vaccine, ideally by the end of October.