SARS-CoV-2 variant transition dynamics are associated with vaccination rates, number of co-circulating variants, and convalescent immunity.

BACKGROUND: Throughout the COVID-19 pandemic, the SARS-CoV-2 virus has continued to evolve, with new variants outcompeting existing variants and often leading to different dynamics of disease spread. METHODS: In this paper, we performed a retrospective analysis using longitudinal sequencing data to characterize differences in the speed, calendar timing, and magnitude of 16 SARS-CoV-2 variant waves/transitions for 230 countries and sub-country regions, between October 2020 and January 2023. We then clustered geographic locations in terms of their variant behavior across several Omicron variants, allowing us to identify groups of locations exhibiting similar variant transitions. Finally, we explored relationships between heterogeneity in these variant waves and time-varying factors, including vaccination status of the population, governmental policy, and the number of variants in simultaneous competition. FINDINGS: This work demonstrates associations between the behavior of an emerging variant and the number of co-circulating variants as well as the demographic context of the population. We also observed an association between high vaccination rates and variant transition dynamics prior to the Mu and Delta variant transitions. INTERPRETATION: These results suggest the behavior of an emergent variant may be sensitive to the immunologic and demographic context of its location. Additionally, this work represents the most comprehensive characterization of variant transitions globally to date. FUNDING: Laboratory Directed Research and Development (LDRD), Los Alamos National Laboratory.

Fast increased fidelity samplers for approximate Bayesian Gaussian process regression.

Gaussian processes (GPs) are common components in Bayesian non-parametric models having a rich methodological literature and strong theoretical grounding. The use of exact GPs in Bayesian models is limited to problems containing several thousand observations due to their prohibitive computational demands. We develop a posterior sampling algorithm using H -matrix approximations that scales at O ( n log 2 n ) . We show that this approximation's Kullback-Leibler divergence to the true posterior can be made arbitrarily small. Though multidimensional GPs could be used with our algorithm, d-dimensional surfaces are modeled as tensor products of univariate GPs to minimize the cost of matrix construction and maximize computational efficiency. We illustrate the performance of this fast increased fidelity approximate GP, FIFA-GP, using both simulated and non-synthetic data sets.

Bayesian hierarchical factor regression models to infer cause of death from verbal autopsy data.

In low-resource settings where vital registration of death is not routine it is often of critical interest to determine and study the cause of death (COD) for individuals and the cause-specific mortality fraction (CSMF) for populations. Post-mortem autopsies, considered the gold standard for COD assignment, are often difficult or impossible to implement due to deaths occurring outside the hospital, expense, and/or cultural norms. For this reason, Verbal Autopsies (VAs) are commonly conducted, consisting of a questionnaire administered to next of kin recording demographic information, known medical conditions, symptoms, and other factors for the decedent. This article proposes a novel class of hierarchical factor regression models that avoid restrictive assumptions of standard methods, allow both the mean and covariance to vary with COD category, and can include covariate information on the decedent, region, or events surrounding death. Taking a Bayesian approach to inference, this work develops an MCMC algorithm and validates the FActor Regression for Verbal Autopsy (FARVA) model in simulation experiments. An application of FARVA to real VA data shows improved goodness-of-fit and better predictive performance in inferring COD and CSMF over competing methods. Code and a user manual are made available at https://github.com/kelrenmor/farva.

Multiscale influenza forecasting.

Influenza forecasting in the United States (US) is complex and challenging due to spatial and temporal variability, nested geographic scales of interest, and heterogeneous surveillance participation. Here we present Dante, a multiscale influenza forecasting model that learns rather than prescribes spatial, temporal, and surveillance data structure and generates coherent forecasts across state, regional, and national scales. We retrospectively compare Dante's short-term and seasonal forecasts for previous flu seasons to the Dynamic Bayesian Model (DBM), a leading competitor. Dante outperformed DBM for nearly all spatial units, flu seasons, geographic scales, and forecasting targets. Dante's sharper and more accurate forecasts also suggest greater public health utility. Dante placed 1st in the Centers for Disease Control and Prevention's prospective 2018/19 FluSight challenge in both the national and regional competition and the state competition. The methodology underpinning Dante can be used in other seasonal disease forecasting contexts having nested geographic scales of interest.

Improving probabilistic infectious disease forecasting through coherence.

With an estimated $10.4 billion in medical costs and 31.4 million outpatient visits each year, influenza poses a serious burden of disease in the United States. To provide insights and advance warning into the spread of influenza, the U.S. Centers for Disease Control and Prevention (CDC) runs a challenge for forecasting weighted influenza-like illness (wILI) at the national and regional level. Many models produce independent forecasts for each geographical unit, ignoring the constraint that the national wILI is a weighted sum of regional wILI, where the weights correspond to the population size of the region. We propose a novel algorithm that transforms a set of independent forecast distributions to obey this constraint, which we refer to as probabilistically coherent. Enforcing probabilistic coherence led to an increase in forecast skill for 79% of the models we tested over multiple flu seasons, highlighting the importance of respecting the forecasting system's geographical hierarchy.

BAYESIAN JOINT MODELING OF CHEMICAL STRUCTURE AND DOSE RESPONSE CURVES.

Today there are approximately 85,000 chemicals regulated under the Toxic Substances Control Act, with around 2,000 new chemicals introduced each year. It is impossible to screen all of these chemicals for potential toxic effects, either via full organism in vivo studies or in vitro high-throughput screening (HTS) programs. Toxicologists face the challenge of choosing which chemicals to screen, and predicting the toxicity of as yet unscreened chemicals. Our goal is to describe how variation in chemical structure relates to variation in toxicological response to enable in silico toxicity characterization designed to meet both of these challenges. With our Bayesian partially Supervised Sparse and Smooth Factor Analysis (BS3FA) model, we learn a distance between chemicals targeted to toxicity, rather than one based on molecular structure alone. Our model also enables the prediction of chemical dose-response profiles based on chemical structure (i.e., without in vivo or in vitro testing) by taking advantage of a large database of chemicals that have already been tested for toxicity in HTS programs. We show superior simulation performance in distance learning and modest to large gains in predictive ability compared to existing methods. Results from the high-throughput screening data application elucidate the relationship between chemical structure and a toxicity-relevant high-throughput assay. An R package for BS3FA is available online at https://github.com/kelrenmor/bs3fa.

Measuring Global Disease with Wikipedia: Success, Failure, and a Research Agenda.

Effective disease monitoring provides a foundation for effective public health systems. This has historically been accomplished with patient contact and bureaucratic aggregation, which tends to be slow and expensive. Recent internet-based approaches promise to be real-time and cheap, with few parameters. However, the question of when and how these approaches work remains open. We addressed this question using Wikipedia access logs and category links. Our experiments, replicable and extensible using our open source code and data, test the effect of semantic article filtering, amount of training data, forecast horizon, and model staleness by comparing across 6 diseases and 4 countries using thousands of individual models. We found that our minimal-configuration, language-agnostic article selection process based on semantic relatedness is effective for improving predictions, and that our approach is relatively insensitive to the amount and age of training data. We also found, in contrast to prior work, very little forecasting value, and we argue that this is consistent with theoretical considerations about the nature of forecasting. These mixed results lead us to propose that the currently observational field of internet-based disease surveillance must pivot to include theoretical models of information flow as well as controlled experiments based on simulations of disease.

A Meta-Analysis of the Association between Gender and Protective Behaviors in Response to Respiratory Epidemics and Pandemics.

Respiratory infectious disease epidemics and pandemics are recurring events that levy a high cost on individuals and society. The health-protective behavioral response of the public plays an important role in limiting respiratory infectious disease spread. Health-protective behaviors take several forms. Behaviors can be categorized as pharmaceutical (e.g., vaccination uptake, antiviral use) or non-pharmaceutical (e.g., hand washing, face mask use, avoidance of public transport). Due to the limitations of pharmaceutical interventions during respiratory epidemics and pandemics, public health campaigns aimed at limiting disease spread often emphasize both non-pharmaceutical and pharmaceutical behavioral interventions. Understanding the determinants of the public's behavioral response is crucial for devising public health campaigns, providing information to parametrize mathematical models, and ultimately limiting disease spread. While other reviews have qualitatively analyzed the body of work on demographic determinants of health-protective behavior, this meta-analysis quantitatively combines the results from 85 publications to determine the global relationship between gender and health-protective behavioral response. The results show that women in the general population are about 50% more likely than men to adopt/practice non-pharmaceutical behaviors. Conversely, men in the general population are marginally (about 12%) more likely than women to adopt/practice pharmaceutical behaviors. It is possible that factors other than pharmaceutical/non-pharmaceutical status not included in this analysis act as moderators of this relationship. These results suggest an inherent difference in how men and women respond to epidemic and pandemic respiratory infectious diseases. This information can be used to target specific groups when developing non-pharmaceutical public health campaigns and to parameterize epidemic models incorporating demographic information.

Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast.

Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection and Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. We conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting.

An initiative by midlevel providers to conduct tertiary surveys at a level I trauma center.

INTRODUCTION: Increased patient volume and residents' work hour restrictions have escalated the workload at trauma centers. Because tertiary surveys (TSs) are integral to care, midlevel providers (MLPs) can help streamline this time-consuming process. In this study, we implemented a care plan in which MLPs conduct all TSs, initiate appropriate consultations, and offload residents' work hours. METHODS: From January 2007 to December 2008, we conducted a prospective evaluation of an initiative in which MLPs performed all TSs within 48 hours of admission. A TS consisted of a complete history and physical examination, follow-up of radiologic interpretations, and appropriate consultations. Data included patient demographics, incidence of additional diagnoses noted during TSs and reduction in residents' work hours. Data are presented as mean +/- standard error. RESULTS: During the 2-year period, there were 5,143 patients admitted to the trauma service. The mean age was 36 years +/- 4.8 years, and mean Injury Severity Score (ISS) was 14.2 +/- 4.2. Overall mortality was 5%. Blunt mechanisms accounted for 85%, and penetrating mechanisms resulted in 14% of injuries. MLPs conducted TSs in 56% of patients during the first year and 76% in the second year. In 80 patients (mean age of 44 years +/- 7.1 years, mean Injury Severity Score 21.7 +/- 2.8; p < 0.05 vs. entire cohort), TSs revealed additional injuries, for an incidence of 1.5%. The majority of these diagnoses were of "minor" fractures, half requiring consultations, and 9% necessitating operative intervention. Residents' workload was reduced by 1,802 hours. CONCLUSIONS: Implementation of a MLP initiative to conduct TSs in trauma patients can achieve a consistent and comprehensive workup while offsetting residents' workload and helping to ensure compliance with the 80-hour resident work policy.

Optimal management strategy for incidental findings in trauma patients: an initiative for midlevel providers.

BACKGROUND: Increasing patient volume and residents' work hour restrictions have increased the workload at trauma centers. Further, comprehensive tertiary surveys after initial stabilization and appropriate follow-up plans for incidental findings are time consuming. Midlevel providers (MLP) can help streamline this process. We initiated a care plan in which MLPs conducted all tertiary surveys and coordinated follow-ups for incidental findings. METHODS: From November 2005 through May 2006, we implemented a MLP-driven initiative aimed at performing tertiary surveys within 48 hours of admission on all trauma patients admitted to our Level-1 trauma center. Tertiary surveys consisted of a complete history and physical, radiographic evaluations and appropriate consultations. Incidental findings were recorded and communicated to the trauma attending. A follow-up plan was devised, and the course of action was documented. Patients or family members were informed, and their acknowledgments were filed. Data are presented as mean +/- SE. RESULTS: There were 1,027 patients admitted during the study period. Blunt mechanisms accounted for 81% of the injuries (primarily motor vehicle crashes and falls). Seventy-six patients had 87 incidental findings (7.4%); 53 were men. The mean age was 51.8 years +/- 2.1 years and mean injury severity score was 18.5 +/- 1.4. Incidental findings of clinical significance included 18 pulmonary nodules or neoplasms, 9 adrenal masses (>4 mm), 7 patients with lymphadenopathy, 5 benign cystic lesions, and 3 renal masses. Other neoplastic lesions included bladder (2), thyroid (2), ovary (1), breast (1), and rectum (1). CONCLUSIONS: With prevalent medicolegal pressure and restricted residents' work hours, a MLP-initiative to streamline the tertiary survey effectively addresses incidental findings. This MLP-driven care plan can help reduce residents' workload, provides appropriate follow-up, and minimizes legal risks inherent to incidental findings on the trauma service.