IMPORTANCE Efforts to track the severity and public health impact of coronavirus disease 2019 (COVID-19) in the United States have been hampered by state-level differences in diagnostic test availability, differing strategies for prioritization of individuals for testing, and delays between testing and reporting. Evaluating unexplained increases in deaths due to all causes or attributed to nonspecific outcomes, such as pneumonia and influenza, can provide a more complete picture of the burden of COVID-19. OBJECTIVE To estimate the burden of all deaths related to COVID-19 in the United States from March to May 2020. DESIGN, SETTING, AND POPULATION This observational study evaluated the numbers of US deaths from any cause and deaths from pneumonia, influenza, and/or COVID-19 from March 1 through May 30, 2020, using public data of the entire US population from the National Center for Health Statistics (NCHS). These numbers were compared with those from the same period of previous years. All data analyzed were accessed on June 12, 2020. MAIN OUTCOMES AND MEASURES Increases in weekly deaths due to any cause or deaths due to pneumonia/influenza/COVID-19 above a baseline, which was adjusted for time of year, influenza activity, and reporting delays. These estimates were compared with reported deaths attributed to COVID-19 and with testing data. RESULTS There were approximately 781 000 total deaths in the United States from March 1 to May 30, 2020, representing 122 300 (95% prediction interval, 116 800-127 000) more deaths than would typically be expected at that time of year. There were 95 235 reported deaths officially attributed to COVID-19 from March 1 to May 30, 2020. The number of excess all-cause deaths was 28% higher than the official tally of COVID-19-reported deaths during that period. In several states, these deaths occurred before increases in the availability of COVID-19 diagnostic tests and were not counted in official COVID-19 death records. There was substantial variability between states in the difference between official COVID-19 deaths and the estimated burden of excess deaths. CONCLUSIONS AND RELEVANCE Excess deaths provide an estimate of the full COVID-19 burden and indicate that official tallies likely undercount deaths due to the virus. The mortality burden and the completeness of the tallies vary markedly between states.

We introduce a conceptual bridge between the previously unlinked fields of phylogenetics and mathematical spatial ecology, which enables the spatial parameters of an emerging epidemic to be directly estimated from sampled pathogen genome sequences. By using phylogenetic history to correct for spatial autocorrelation, we illustrate how a fundamental spatial variable, the diffusion coefficient, can be estimated using robust nonparametric statistics, and how heterogeneity in dispersal can be readily quantified. We apply this framework to the spread of the West Nile virus across North America, an important recent instance of spatial invasion by an emerging infectious disease. We demonstrate that the dispersal of West Nile virus is greater and far more variable than previously measured, such that its dissemination was critically determined by rare, long-range movements that are unlikely to be discerned during field observations. Our results indicate that, by ignoring this heterogeneity, previous models of the epidemic have substantially overestimated its basic reproductive number. More generally, our approach demonstrates that easily obtainable genetic data can be used to measure the spatial dynamics of natural populations that are otherwise difficult or costly to quantify.phylogeny | phylogeography | transmission T he explanation of spatial patterns of infectious disease, particularly those of emerging pathogens, has remained a central problem of epidemiology since its inception (1). The existence and nature of traveling waves of infection were first explained in theoretical models (2, 3) and later quantified in empirical studies of rabies and the Black Death (4, 5). These and other studies highlighted the fundamental problem of spatial autocorrelation: observations of infection are statistically dependent due to transmission among proximate individuals, greatly complicating the analysis of spatiotemporal incidence. Consequently, many recent analyses of spatial epidemic behavior use detailed mathematical models of spatial structure to account for autocorrelation (6). Entirely independently, in the field of evolutionary biology there has developed a separate body of work, now termed phylogeography, which focuses on reconstructing past movement events from the genome sequences of sampled organisms (7-10). However, these evolutionary tools typically generate descriptive results that, though informative, remain divorced from epidemiological theory. Crucially neither approach can be considered complete when applied to rapidly evolving viruses, whose spatial, epidemic, and evolutionary dynamics occur on the same timescale (11), necessitating the development of methods that consider all these processes together.Here we introduce a unique approach that integrates the disciplines of spatial epidemiology and phylogenetics. To illustrate the utility of this approach, we show how, from pathogen genomes alone, it can estimate the diffusion coefficient (D) of an epidemic as well as variation in the process of spatial spread. D is...

BackgroundUpcoming vaccination efforts against typhoid fever require an assessment of the baseline burden of disease in countries at risk. There are no typhoid incidence data from most low- and middle-income countries (LMICs), so model-based estimates offer insights for decision-makers in the absence of readily available data.MethodsWe developed a mixed-effects model fit to data from 32 population-based studies of typhoid incidence in 22 locations in 14 countries. We tested the contribution of economic and environmental indices for predicting typhoid incidence using a stochastic search variable selection algorithm. We performed out-of-sample validation to assess the predictive performance of the model.ResultsWe estimated that 17.8 million cases of typhoid fever occur each year in LMICs (95% credible interval: 6.9–48.4 million). Central Africa was predicted to experience the highest incidence of typhoid, followed by select countries in Central, South, and Southeast Asia. Incidence typically peaked in the 2–4 year old age group. Models incorporating widely available economic and environmental indicators were found to describe incidence better than null models.ConclusionsRecent estimates of typhoid burden may under-estimate the number of cases and magnitude of uncertainty in typhoid incidence. Our analysis permits prediction of overall as well as age-specific incidence of typhoid fever in LMICs, and incorporates uncertainty around the model structure and estimates of the predictors. Future studies are needed to further validate and refine model predictions and better understand year-to-year variation in cases.

Most individuals are stigmatized at some point. However, research often examines stigmas separately, thus underestimating the overall impact of stigma and precluding comparisons across stigmatized identities and conditions. In their classic text, Social Stigma: The Psychology of Marked Relationships, Edward Jones and colleagues laid the groundwork for unifying the study of different stigmas by considering the shared dimensional features of stigmas: aesthetics, concealability, course, disruptiveness, origin, peril. Despite the prominence of this framework, no study has documented the extent to which stigmas differ along these dimensions, and the implications of this variation for health and well-being. We reinvigorated this framework to spur a comprehensive account of stigma's impact by classifying 93 stigmas along these dimensions. With the input of expert and general public raters, we then located these stigmas in a six-dimensional space and created discrete clusters organized around these dimensions. Next, we linked this taxonomy to health and stigma-related mechanisms. This quantitative taxonomy offers parsimonious insights into the relationship among the numerous qualities of numerous stigmas and health.

Purpose Glioblastoma Multiforme (GBM) is the most common and lethal primary brain tumor in adults. The goal of this study was to test the predictive value of MR parameters in relation to the survival of patients with newly diagnosed GBM who were scanned prior to receiving adjuvant radiation and chemotherapy. Methods The study population comprised 68 patients who had surgical resection and were to be treated with fractionated external beam radiation therapy and chemotherapy. Imaging scans included anatomical MRI, diffusion and perfusion weighted imaging and 1H MRSI. The MR data were acquired 3–5 weeks after surgery and approximately 1 week before treatment with radiation therapy. The diffusion, perfusion and spectroscopic parameter values were quantified and subjected to proportional hazards analysis that was adjusted for age and scanner field strength. Results The patients with larger lesion burden based upon volumes of anatomic lesions, volume of CNI2 (number of voxels within the T2 lesion having choline to NAA index >2), volume of CBV3 (number of pixels within the T2 lesion having relative cerebral blood volume >3), and volume of nADC1.5 (number of pixels within the T2 lesion having normalized apparent diffusion coefficient <1.5) had a higher risk for poor outcome. High intensities of combined measures of lactate and lipid in the T2 and CNI2 regions were also associated with poor survival. Conclusions Our study indicated that several pre-treatment anatomic, physiological and metabolic MR parameters are predictive of survival. This information may be important for stratifying patients to specific treatment protocols and for planning focal therapy.