Model misspecification misleads inference of the spatial dynamics of disease outbreaks.

Epidemiology has been transformed by the advent of Bayesian phylodynamic models that allow researchers to infer the geographic history of pathogen dispersal over a set of discrete geographic areas [1, 2]. These models provide powerful tools for understanding the spatial dynamics of disease outbreaks, but contain many parameters that are inferred from minimal geographic information (i.e., the single area in which each pathogen was sampled). Consequently, inferences under these models are inherently sensitive to our prior assumptions about the model parameters. Here, we demonstrate that the default priors used in empirical phylodynamic studies make strong and biologically unrealistic assumptions about the underlying geographic process. We provide empirical evidence that these unrealistic priors strongly (and adversely) impact commonly reported aspects of epidemiological studies, including: 1) the relative rates of dispersal between areas; 2) the importance of dispersal routes for the spread of pathogens among areas; 3) the number of dispersal events between areas, and; 4) the ancestral area in which a given outbreak originated. We offer strategies to avoid these problems, and develop tools to help researchers specify more biologically reasonable prior models that will realize the full potential of phylodynamic methods to elucidate pathogen biology and, ultimately, inform surveillance and monitoring policies to mitigate the impacts of disease outbreaks.

PrioriTree: a utility for improving phylodynamic analyses in BEAST.

SUMMARY: Phylodynamic methods are central to studies of the geographic and demographic history of disease outbreaks. Inference under discrete-geographic phylodynamic models-which involve many parameters that must be inferred from minimal information-is inherently sensitive to our prior beliefs about the model parameters. We present an interactive utility, PrioriTree, to help researchers identify and accommodate prior sensitivity in discrete-geographic inferences. Specifically, PrioriTree provides a suite of functions to generate input files for-and summarize output from-BEAST analyses for performing robust Bayesian inference, data-cloning analyses and assessing the relative and absolute fit of candidate discrete-geographic (prior) models to empirical datasets. AVAILABILITY AND IMPLEMENTATION: PrioriTree is distributed as an R package available at https://github.com/jsigao/prioritree, with a comprehensive user manual provided at https://bookdown.org/jsigao/prioritree_manual/.

New Phylogenetic Models Incorporating Interval-Specific Dispersal Dynamics Improve Inference of Disease Spread.

Phylodynamic methods reveal the spatial and temporal dynamics of viral geographic spread, and have featured prominently in studies of the COVID-19 pandemic. Virtually all such studies are based on phylodynamic models that assume-despite direct and compelling evidence to the contrary-that rates of viral geographic dispersal are constant through time. Here, we: (1) extend phylodynamic models to allow both the average and relative rates of viral dispersal to vary independently between pre-specified time intervals; (2) implement methods to infer the number and timing of viral dispersal events between areas; and (3) develop statistics to assess the absolute fit of discrete-geographic phylodynamic models to empirical datasets. We first validate our new methods using simulations, and then apply them to a SARS-CoV-2 dataset from the early phase of the COVID-19 pandemic. We show that: (1) under simulation, failure to accommodate interval-specific variation in the study data will severely bias parameter estimates; (2) in practice, our interval-specific discrete-geographic phylodynamic models can significantly improve the relative and absolute fit to empirical data; and (3) the increased realism of our interval-specific models provides qualitatively different inferences regarding key aspects of the COVID-19 pandemic-revealing significant temporal variation in global viral dispersal rates, viral dispersal routes, and the number of viral dispersal events between areas-and alters interpretations regarding the efficacy of intervention measures to mitigate the pandemic.

A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes.

Transcriptomes are key to understanding the relationship between genotype and phenotype. The ability to infer the expression state (active or inactive) of genes in the transcriptome offers unique benefits for addressing this issue. For example, qualitative changes in gene expression may underly the origin of novel phenotypes, and expression states are readily comparable between tissues and species. However, inferring the expression state of genes is a surprisingly difficult problem, owing to the complex biological and technical processes that give rise to observed transcriptomic datasets. Here, we develop a hierarchical Bayesian mixture model that describes this complex process and allows us to infer expression state of genes from replicate transcriptomic libraries. We explore the statistical behavior of this method with analyses of simulated datasets-where we demonstrate its ability to correctly infer true (known) expression states-and empirical-benchmark datasets, where we demonstrate that the expression states inferred from RNA-sequencing (RNA-seq) datasets using our method are consistent with those based on independent evidence. The power of our method to correctly infer expression states is generally high and remarkably, approaches the maximum possible power for this inference problem. We present an empirical analysis of primate-brain transcriptomes, which identifies genes that have a unique expression state in humans. Our method is implemented in the freely available R package zigzag.

A Bayesian Approach for Inferring the Impact of a Discrete Character on Rates of Continuous-Character Evolution in the Presence of Background-Rate Variation.

Understanding how and why rates of character evolution vary across the Tree of Life is central to many evolutionary questions; for example, does the trophic apparatus (a set of continuous characters) evolve at a higher rate in fish lineages that dwell in reef versus nonreef habitats (a discrete character)? Existing approaches for inferring the relationship between a discrete character and rates of continuous-character evolution rely on comparing a null model (in which rates of continuous-character evolution are constant across lineages) to an alternative model (in which rates of continuous-character evolution depend on the state of the discrete character under consideration). However, these approaches are susceptible to a "straw-man" effect: the influence of the discrete character is inflated because the null model is extremely unrealistic. Here, we describe MuSSCRat, a Bayesian approach for inferring the impact of a discrete trait on rates of continuous-character evolution in the presence of alternative sources of rate variation ("background-rate variation"). We demonstrate by simulation that our method is able to reliably infer the degree of state-dependent rate variation, and show that ignoring background-rate variation leads to biased inferences regarding the degree of state-dependent rate variation in grunts (the fish group Haemulidae). [Bayesian phylogenetic comparative methods; continuous-character evolution; data augmentation; discrete-character evolution.].

Critically evaluating the theory and performance of Bayesian analysis of macroevolutionary mixtures.

Bayesian analysis of macroevolutionary mixtures (BAMM) has recently taken the study of lineage diversification by storm. BAMM estimates the diversification-rate parameters (speciation and extinction) for every branch of a study phylogeny and infers the number and location of diversification-rate shifts across branches of a tree. Our evaluation of BAMM reveals two major theoretical errors: (i) the likelihood function (which estimates the model parameters from the data) is incorrect, and (ii) the compound Poisson process prior model (which describes the prior distribution of diversification-rate shifts across branches) is incoherent. Using simulation, we demonstrate that these theoretical issues cause statistical pathologies; posterior estimates of the number of diversification-rate shifts are strongly influenced by the assumed prior, and estimates of diversification-rate parameters are unreliable. Moreover, the inability to correctly compute the likelihood or to correctly specify the prior for rate-variable trees precludes the use of Bayesian approaches for testing hypotheses regarding the number and location of diversification-rate shifts using BAMM.

TESS: an R package for efficiently simulating phylogenetic trees and performing Bayesian inference of lineage diversification rates.

UNLABELLED: Many fundamental questions in evolutionary biology entail estimating rates of lineage diversification (speciation-extinction) that are modeled using birth-death branching processes. We leverage recent advances in branching-process theory to develop a flexible Bayesian framework for specifying diversification models-where rates are constant, vary continuously, or change episodically through time-and implement numerical methods to estimate parameters of these models from molecular phylogenies, even when species sampling is incomplete. We enable both statistical inference and efficient simulation under these models. We also provide robust methods for comparing the relative and absolute fit of competing branching-process models to a given tree, thereby providing rigorous tests of biological hypotheses regarding patterns and processes of lineage diversification. AVAILABILITY AND IMPLEMENTATION: The source code for TESS is freely available at http://cran.r-project.org/web/packages/TESS/ CONTACT: Sebastian.Hoehna@gmail.com.

How Well Can We Detect Lineage-Specific Diversification-Rate Shifts? A Simulation Study of Sequential AIC Methods.

Evolutionary biologists have long been fascinated by the extreme differences in species numbers across branches of the Tree of Life. This has motivated the development of statistical methods for detecting shifts in the rate of lineage diversification across the branches of phylogenic trees. One of the most frequently used methods, MEDUSA, explores a set of diversification-rate models, where each model assigns branches of the phylogeny to a set of diversification-rate categories. Each model is first fit to the data, and the Akaike information criterion (AIC) is then used to identify the optimal diversification model. Surprisingly, the statistical behavior of this popular method is uncharacterized, which is a concern in light of: (1) the poor performance of the AIC as a means of choosing among models in other phylogenetic contexts; (2) the ad hoc algorithm used to visit diversification models, and; (3) errors that we reveal in the likelihood function used to fit diversification models to the phylogenetic data. Here, we perform an extensive simulation study demonstrating that MEDUSA (1) has a high false-discovery rate (on average, spurious diversification-rate shifts are identified [Formula: see text] of the time), and (2) provides biased estimates of diversification-rate parameters. Understanding the statistical behavior of MEDUSA is critical both to empirical researchers-in order to clarify whether these methods can make reliable inferences from empirical datasets-and to theoretical biologists-in order to clarify the specific problems that need to be solved in order to develop more reliable approaches for detecting shifts in the rate of lineage diversification. [Akaike information criterion; extinction; lineage-specific diversification rates; phylogenetic model selection; speciation.].

RevBayes: Bayesian Phylogenetic Inference Using Graphical Models and an Interactive Model-Specification Language.

Programs for Bayesian inference of phylogeny currently implement a unique and fixed suite of models. Consequently, users of these software packages are simultaneously forced to use a number of programs for a given study, while also lacking the freedom to explore models that have not been implemented by the developers of those programs. We developed a new open-source software package, RevBayes, to address these problems. RevBayes is entirely based on probabilistic graphical models, a powerful generic framework for specifying and analyzing statistical models. Phylogenetic-graphical models can be specified interactively in RevBayes, piece by piece, using a new succinct and intuitive language called Rev. Rev is similar to the R language and the BUGS model-specification language, and should be easy to learn for most users. The strength of RevBayes is the simplicity with which one can design, specify, and implement new and complex models. Fortunately, this tremendous flexibility does not come at the cost of slower computation; as we demonstrate, RevBayes outperforms competing software for several standard analyses. Compared with other programs, RevBayes has fewer black-box elements. Users need to explicitly specify each part of the model and analysis. Although this explicitness may initially be unfamiliar, we are convinced that this transparency will improve understanding of phylogenetic models in our field. Moreover, it will motivate the search for improvements to existing methods by brazenly exposing the model choices that we make to critical scrutiny. RevBayes is freely available at http://www.RevBayes.com [Bayesian inference; Graphical models; MCMC; statistical phylogenetics.].

A critical appraisal of the use of microRNA data in phylogenetics.

Recent progress in resolving the tree of life continues to expose relationships that resist resolution, which drives the search for novel sources of information to solve these difficult phylogenetic problems. A recent example, the presence and absence of microRNA families, has been vigorously promoted as an ideal source of phylogenetic data and has been applied to several perennial phylogenetic problems. The utility of such data for phylogenetic inference hinges critically both on developing stochastic models that provide a reasonable description of the process that give rise to these data, and also on the careful validation of those models in real inference scenarios. Remarkably, however, the statistical behavior and phylogenetic utility of microRNA data have not yet been rigorously characterized. Here we explore the behavior and performance of microRNA presence/absence data under a variety of evolutionary models and reexamine datasets from several previous studies. We find that highly heterogeneous rates of microRNA gain and loss, pervasive secondary loss, and sampling error collectively render microRNA-based inference of phylogeny difficult. Moreover, our reanalyses fundamentally alter the conclusions for four of the five studies that we reexamined. Our results indicate that the capacity of miRNA data to resolve the tree of life has been overstated, and we urge caution in their application and interpretation.

Heterostyly accelerates diversification via reduced extinction in primroses.

The exceptional species diversity of flowering plants, exceeding that of their sister group more than 250-fold, is especially evident in floral innovations, interactions with pollinators and sexual systems. Multiple theories, emphasizing flower-pollinator interactions, genetic effects of mating systems or high evolvability, predict that floral evolution profoundly affects angiosperm diversification. However, consequences for speciation and extinction dynamics remain poorly understood. Here, we investigate trajectories of species diversification focusing on heterostyly, a remarkable floral syndrome where outcrossing is enforced via cross-compatible floral morphs differing in placement of their respective sexual organs. Heterostyly evolved at least 20 times independently in angiosperms. Using Darwin's model for heterostyly, the primrose family, we show that heterostyly accelerates species diversification via decreasing extinction rates rather than increasing speciation rates, probably owing to avoidance of the negative genetic effects of selfing. However, impact of heterostyly appears to differ over short and long evolutionary time-scales: the accelerating effect of heterostyly on lineage diversification is manifest only over long evolutionary time-scales, whereas recent losses of heterostyly may prompt ephemeral bursts of speciation. Our results suggest that temporal or clade-specific conditions may ultimately determine the net effects of specific traits on patterns of species diversification.

Bayesian analysis of biogeography when the number of areas is large.

Historical biogeography is increasingly studied from an explicitly statistical perspective, using stochastic models to describe the evolution of species range as a continuous-time Markov process of dispersal between and extinction within a set of discrete geographic areas. The main constraint of these methods is the computational limit on the number of areas that can be specified. We propose a Bayesian approach for inferring biogeographic history that extends the application of biogeographic models to the analysis of more realistic problems that involve a large number of areas. Our solution is based on a "data-augmentation" approach, in which we first populate the tree with a history of biogeographic events that is consistent with the observed species ranges at the tips of the tree. We then calculate the likelihood of a given history by adopting a mechanistic interpretation of the instantaneous-rate matrix, which specifies both the exponential waiting times between biogeographic events and the relative probabilities of each biogeographic change. We develop this approach in a Bayesian framework, marginalizing over all possible biogeographic histories using Markov chain Monte Carlo (MCMC). Besides dramatically increasing the number of areas that can be accommodated in a biogeographic analysis, our method allows the parameters of a given biogeographic model to be estimated and different biogeographic models to be objectively compared. Our approach is implemented in the program, BayArea.

A Bayesian approach for evaluating the impact of historical events on rates of diversification.

Evolutionary biologists often wish to explore the impact of a particular historical event (e.g., the origin of a novel morphological trait, an episode of biogeographic dispersal, or the onset of an ecological association) on rates of diversification (speciation minus extinction). We describe a Bayesian approach for evaluating the correlation between such events and differential rates of diversification that relies on cross-validation predictive densities. This approach exploits estimates of the marginal posterior probability for the rate of diversification (in the unaffected part of the tree) and the marginal probability for the timing of the event to generate a predictive distribution of species diversity that would be expected had the event not occurred. The realized species diversity can then be compared to this predictive diversity distribution to assess whether rates of diversification associated with the event are significantly higher or lower than expected. Although simple, this Bayesian approach provides a robust inference framework that accommodates various sources of uncertainty, including error associated with estimates of divergence times, diversification-rate parameters, and event history. Furthermore, the proposed approach is relatively flexible, allowing exploration of various types of events (including changes in discrete morphological traits, episodes of biogeographic movement, etc.) under both hypothesis-testing and data-exploration inference scenarios. Importantly, the cross-validation predictive densities approach facilitates evaluation of both replicated and unique historical events. We demonstrate this approach with empirical examples concerning the impact of morphological and biogeographic events on rates of diversification in Adoxaceae and Lupinus, respectively.

Language barriers as a reported cause of prehospital care delay in Minnesota.

OBJECTIVE: Although anecdotal reports exist, the frequency of language barriers encountered between EMS providers and patients/families in the prehospital environment remains unknown. The purpose of this study was to determine the frequency of EMS provider-reported perceived delays in care due to language barrier and to characterize the nature of calls involved. METHODS: Retrospective analysis of the Minnesota State Ambulance Reporting system (MNSTAR) database, a mandated statewide EMS data collection tool. All EMS run reports submitted between January 1, 2004, and June 30, 2005, were reviewed to identify instances of reported treatment delay secondary to a language barrier. RESULTS: During the 18-month study period, 629,738 patient encounter reports were submitted to MNSTAR, of which 2,052 identified treatment delays secondary to language. The rate of language barrier care delays in the state of Minnesota is 3.3 per 1,000 prehospital patient encounters. CONCLUSION: EMS responses troubled by delays in care secondary to language barriers represent a small percentage of total runs in Minnesota. However, approximately 1,370 cases per year occur.

Rapid Global Spread of wRi-like Wolbachia across Multiple Drosophila.

Maternally transmitted Wolbachia, Spiroplasma, and Cardinium bacteria are common in insects [1], but their interspecific spread is poorly understood. Endosymbionts can spread rapidly within host species by manipulating host reproduction, as typified by the global spread of wRi Wolbachia observed in Drosophila simulans [2, 3]. However, because Wolbachia cannot survive outside host cells, spread between distantly related host species requires horizontal transfers that are presumably rare [4-7]. Here, we document spread of wRi-like Wolbachia among eight highly diverged Drosophila hosts (10-50 million years) over only about 14,000 years (5,000-27,000). Comparing 110 wRi-like genomes, we find ≤0.02% divergence from the wRi variant that spread rapidly through California populations of D. simulans. The hosts include both globally invasive species (D. simulans, D. suzukii, and D. ananassae) and narrowly distributed Australian endemics (D. anomalata and D. pandora) [8]. Phylogenetic analyses that include mtDNA genomes indicate introgressive transfer of wRi-like Wolbachia between closely related species D. ananassae, D. anomalata, and D. pandora but no horizontal transmission within species. Our analyses suggest D. ananassae as the Wolbachia source for the recent wRi invasion of D. simulans and D. suzukii as the source of Wolbachia in its sister species D. subpulchrella. Although six of these wRi-like variants cause strong cytoplasmic incompatibility, two cause no detectable reproductive effects, indicating that pervasive mutualistic effects [9, 10] complement the reproductive manipulations for which Wolbachia are best known. "Super spreader" variants like wRi may be particularly useful for controlling insect pests and vector-borne diseases with Wolbachia transinfections [11].

Correlates of diversification in the plant clade Dipsacales: geographic movement and evolutionary innovations.

We explore patterns of diversification in the plant clades Adoxaceae and Valerianaceae (within Dipsacales), evaluating correlations between biogeographic change (i.e., movements into new areas), morphological change (e.g., the origin of putative key innovations associated with vegetative and reproductive characters), and shifts in rates of diversification. Our findings indicate that rates of diversification in these plants tend to be less tightly correlated with the evolution of morphological innovations but instead exhibit a pronounced correlation with movement into new geographic areas, particularly the dispersal of lineages into new mountainous regions. The interdependence among apparent novelties (arising from their nested phylogenetic distribution) and the correlation between morphological and biogeographic change suggests a complex history of diversification in Dipsacales. Overall, these findings highlight the importance of incorporating biogeographic history in studies of diversification rates and in the study of geographic gradients in species richness. Furthermore, these results argue against a simple deterministic relationship between dispersal and diversification: like other factors that may influence the probability of speciation and/or extinction, the impact of dispersal on diversification rates depends on being in the right place at the right time.

A likelihood framework for inferring the evolution of geographic range on phylogenetic trees.

At a time when historical biogeography appears to be again expanding its scope after a period of focusing primarily on discerning area relationships using cladograms, new inference methods are needed to bring more kinds of data to bear on questions about the geographic history of lineages. Here we describe a likelihood framework for inferring the evolution of geographic range on phylogenies that models lineage dispersal and local extinction in a set of discrete areas as stochastic events in continuous time. Unlike existing methods for estimating ancestral areas, such as dispersal-vicariance analysis, this approach incorporates information on the timing of both lineage divergences and the availability of connections between areas (dispersal routes). Monte Carlo methods are used to estimate branch-specific transition probabilities for geographic ranges, enabling the likelihood of the data (observed species distributions) to be evaluated for a given phylogeny and parameterized paleogeographic model. We demonstrate how the method can be used to address two biogeographic questions: What were the ancestral geographic ranges on a phylogenetic tree? How were those ancestral ranges affected by speciation and inherited by the daughter lineages at cladogenesis events? For illustration we use hypothetical examples and an analysis of a Northern Hemisphere plant clade (Cercis), comparing and contrasting inferences to those obtained from dispersal-vicariance analysis. Although the particular model we implement is somewhat simplistic, the framework itself is flexible and could readily be modified to incorporate additional sources of information and also be extended to address other aspects of historical biogeography.

Performance of a decision rule for radiographs of pediatric knee injuries.

Although decision rules for radiographs of pediatric knee injuries have been suggested from retrospective studies, prospective evaluations of such rules have been limited. We sought to prospectively assess the performance of a rule in children presenting with acute knee injuries. Eligible participants were children aged 3-18 years with an acute knee injury. The settings for the study were a tertiary pediatric emergency department (ED), a community hospital ED, and a pediatric urgent care center. All of the participants received standard knee radiographs. Before radiography, each patient was assessed by a pediatrician or pediatric emergency physician for presence of the following: 1) inability to bear weight, 2) inability to flex the knee to 90( degrees ), 3) presence of bony tenderness. The radiographs were interpreted by a radiologist blinded to the study; those with findings reported as consistent with acute fracture were considered positive. A total of 146 patients were enrolled (65% male, mean age 11.6 years). Of these, 15 (10.3%) had a fracture on their radiograph, 6 of which were related to trampoline use. Seventy-seven (53%) were negative for criterion 1 (i.e., able to bear weight immediately after the accident and in the ED), none (0%) of whom had fractures. The negative predictive value of this criterion was 1.0 (95% CI 0.94-1.0). The positive predictive value was 0.22 (95% CI 0.13-0.34). The sensitivity was 1.0 (95% CI 0.82-1.0). The specificity was 0.59 (95% CI 0.50-0.67). Three patients negative for criterion 3 were found to have fractures. The proximal tibia was the most common fracture site (47%). In conclusion, assessment of the ability to bear weight would have decreased the use of radiography by 53% without missing any fractures in our study population. No additional value to the rule was found by adding assessment of the ability to flex the knee or bony tenderness.

Pediatric emergencies on a US-based commercial airline.

OBJECTIVES: The purpose of this investigation was to determine the incidence and character of pediatric emergencies on a US-based commercial airline and to evaluate current in-flight medical kits. METHODS: In-flight consultations to a major US airline by a member of our staff are recorded in an institutional database. In this observational retrospective review, the database was queried for consultations for all passengers up to 18 years old between January 1, 1995, and December 31, 2002. Consultations were reviewed for type of emergency, use of the medical kit, and unscheduled landings. RESULTS: Two hundred twenty-two pediatric consultations were identified, representing 1 pediatric call per 20,775 flights. The mean age of patients was 6.8 years. Fifty-three emergencies were preflight calls, and 169 were in-flight pediatric consultations. The most common in-flight consultations concerned infectious disease (45 calls, 27%), neurological (25 calls, 15%), and respiratory tract (22 calls, 13%) emergencies. The emergency medical kit was used for 60 emergencies. Nineteen consultations (11%) resulted in flight diversions (1/240,000 flights), most commonly because of in-flight neurological (9) and respiratory tract (5) emergencies. International flights had a higher incidence than domestic flights of consultations and diversions for pediatric emergencies. CONCLUSIONS: The most common in-flight pediatric emergencies involved infectious diseases and neurological and respiratory tract problems. Emergency medical kits should be expanded to include pediatric medications.

A reexamination of the feasibility of the administration of routine childhood vaccines in emergency departments in the era of electronic vaccine registries.

OBJECTIVES: To determine if electronic vaccine records facilitate successful routine childhood vaccination in the emergency department (ED). METHODS: We sampled consecutively over 2 calendar months children younger than 24 months presenting to the ED. Parents and legal guardians of eligible children were offered enrollment. Those consenting completed a parental survey after a nurse conducted an initial assessment of eligibility. Attending physicians then completed the assessment, and after the visit, the electronic vaccination records, when available, were accessed. No actual routine childhood vaccines were given during the study. RESULTS: Three hundred thirty-four were approached: 17 (5.1%) declined participation; 10 (3.0%) were enrolled, but the data were lost, and 7 (2.1%) were excluded. Of the 300 remaining, 235 (78.3%) had available electronic vaccine records. Only 38 (16.2%) of the 235 were late for at least 1 vaccine. Of note, physicians assessed 22 (57.9%) of the 38 as medically appropriate for vaccination in the ED. The overwhelming majority (81.8%) of the 22 parents and guardians would have assented to vaccination in the ED. Of the 38 patients found late for vaccination, 31 (81.6%) of parents incorrectly reported their children to be up-to-date on their immunizations. CONCLUSIONS: Assuming that the electronic vaccination record performed such as an online vaccine registry, the effort to access the registry might find a substantial number of children late for a routine childhood vaccination. In this setting, we found that approximately one sixth of the children with electronic vaccine records would be found late for vaccination, and based on physician assessment and parental survey, one half of those children would receive that vaccination if available in the ED. These rates offer health care planners a sense of the magnitude of the vaccination rates in the ED as we move toward regional vaccination registries with online capabilities to be accessed by EDs.