Near real time monitoring and forecasting for COVID-19 situational awareness.

In the opening months of the pandemic, the need for situational awareness was urgent. Forecasting models such as the Susceptible-Infectious-Recovered (SIR) model were hampered by limited testing data and key information on mobility, contact tracing, and local policy variations would not be consistently available for months. New case counts from sources like John Hopkins University and the NY Times were systematically reliable. Using these data, we developed the novel COVID County Situational Awareness Tool (CCSAT) for reliable monitoring and decision support. In CCSAT, we developed a retrospective seven-day moving window semantic map of county-level disease magnitude and acceleration that smoothed noisy daily variations. We also developed a novel Bayesian model that reliably forecasted county-level magnitude and acceleration for the upcoming week based on population and new case count data. Together these formed a robust operational update including county-level maps of new case rate changes, estimates of new cases in the upcoming week, and measures of model reliability. We found CCSAT provided stable, reliable estimates across the seven-day time window, with the greatest errors occurring in cases of anomalous, single day spikes. In this paper, we provide CCSAT details and apply it to a single week in June 2020.

Sociodemographic Determinants of Acute Myocardial Infarction Hospitalization Risks in Florida.

Background Identifying social determinants of myocardial infarction (MI) hospitalizations is crucial for reducing/eliminating health disparities. Therefore, our objectives were to identify sociodemographic determinants of MI hospitalization risks and to assess if the impacts of these determinants vary by geographic location in Florida. Methods and Results This is a retrospective ecologic study at the county level. We obtained data for principal and secondary MI hospitalizations for Florida residents for the 2005-2014 period and calculated age- and sex-adjusted MI hospitalization risks. We used a multivariable negative binomial model to identify sociodemographic determinants of MI hospitalization risks and a geographically weighted negative binomial model to assess if the strength of associations vary by location. There were 645 935 MI hospitalizations (median age, 72 years; 58.1%, men; 73.9%, white). Age- and sex-adjusted risks ranged from 18.49 to 69.48 cases/10 000 persons, and they were significantly higher in counties with low education levels (risk ratio [RR]=1.033, P<0.0001) and high divorce rate (RR, 0.995; P=0.018). However, they were significantly lower in counties with high proportions of rural (RR, 0.996; P<0.0001), black (RR, 1.026; P=0.032), and uninsured populations (RR, 0.983; P=0.040). Associations of MI hospitalization risks with education level and uninsured rate varied geographically (P for non-stationarity test=0.001 and 0.043, respectively), with strongest associations in southern Florida (RR for

Disparities in Temporal and Geographic Patterns of Myocardial Infarction Hospitalization Risks in Florida.

Knowledge of geographical disparities in myocardial infarction (MI) is critical for guiding health planning and resource allocation. The objectives of this study were to identify geographic disparities in MI hospitalization risks in Florida and assess temporal changes in these disparities between 2005 and 2014. This study used retrospective data on MI hospitalizations that occurred among Florida residents between 2005 and 2014. We identified spatial clusters of hospitalization risks using Kulldorff's circular and Tango's flexible spatial scan statistics. Counties with persistently high or low MI hospitalization risks were identified. There was a 20% decline in hospitalization risks during the study period. However, we found persistent clustering of high risks in the Big Bend region, South Central and southeast Florida, and persistent clustering of low risks primarily in the South. Risks decreased by 7%-21% in high-risk clusters and by 9%-28% in low-risk clusters. The risk decreased in the high-risk cluster in the southeast but increased in the Big Bend area during the last four years of the study. Overall, risks in low-risk clusters were ahead those for high-risk clusters by at least 10 years. Despite MI risk declining over the study period, disparities in MI risks persist. Eliminating/reducing those disparities will require prioritizing high-risk clusters for interventions.

Geographic disparities and temporal changes in risk of death from myocardial infarction in Florida, 2000-2014.

BACKGROUND: Identifying disparities in myocardial infarction (MI) burden and assessing its temporal changes are critical for guiding resource allocation and policies geared towards reducing/eliminating health disparities. Our objectives were to: (a) investigate the spatial distribution and clusters of MI mortality risk in Florida; and (b) assess temporal changes in geographic disparities in MI mortality risks in Florida from 2000 to 2014. METHODS: This is a retrospective ecologic study with county as the spatial unit of analysis. We obtained data for MI deaths occurring among Florida residents between 2000 and 2014 from the Florida Department of Health, and calculated county-level age-adjusted MI mortality risks and Spatial Empirical Bayesian smoothed MI mortality risks. We used Kulldorff's circular spatial scan statistics and Tango's flexible spatial scan statistics to identify spatial clusters. RESULTS: There was an overall decline of 48% in MI mortality risks between 2000 and 2014. However, we found substantial, persistent disparities in MI mortality risks, with high-risk clusters occurring primarily in rural northern counties and low-risk clusters occurring exclusively in urban southern counties. MI mortality risks declined in both low- and high-risk clusters, but the latter showed more dramatic decreases during the first nine years of the study period. Consequently, the risk difference between the high- and low-risk clusters was smaller at the end than at the beginning of the study period. However, the rates of decline levelled off during the last six years of the study, and there are signs that the risks may be on an upward trend in parts of North Florida. Moreover, MI mortality risks for high-risk clusters at the end of the study period were on par with or above those for low-risk clusters at the beginning of the study period. Thus, high-risk clusters lagged behind low-risk clusters by at least 1.5 decades. CONCLUSION: Myocardial infarction mortality risks have decreased substantially during the last 15 years, but persistent disparities in MI mortality burden still exist across Florida. Efforts to reduce these disparities will need to target prevention programs to counties in the high-risk clusters.

Assessing pretreatment reactor scaling through empirical analysis.

BACKGROUND: Pretreatment is a critical step in the biochemical conversion of lignocellulosic biomass to fuels and chemicals. Due to the complexity of the physicochemical transformations involved, predictively scaling up technology from bench- to pilot-scale is difficult. This study examines how pretreatment effectiveness under nominally similar reaction conditions is influenced by pretreatment reactor design and scale using four different pretreatment reaction systems ranging from a 3 g batch reactor to a 10 dry-ton/days continuous reactor. The reactor systems examined were an automated solvent extractor (ASE), steam explosion reactor (SER), ZipperClave®Reactor (ZCR), and large continuous horizontal screw reactor (LHR). To our knowledge, this is the first such study performed on pretreatment reactors across a range of reaction conditions and at different reactor scales. RESULTS: The comparative pretreatment performance results obtained for each reactor system were used to develop response surface models for total xylose yield after pretreatment and total sugar yield after pretreatment followed by enzymatic hydrolysis. Near- and very-near-optimal regions were defined as the set of conditions that the model identified as producing yields within one and two standard deviations of the optimum yield. Optimal conditions identified in the smallest scale system (the ASE) were within the near-optimal region of the largest scale reactor system evaluated. The maximum total sugar yields for the ASE and LHR were [Formula: see text], while [Formula: see text] was the optimum observed in the ZipperClave. CONCLUSIONS: The optimum condition identified using the automated and less costly to operate ASE system was within the very-near-optimal space for the total xylose yield of both the ZCR and the LHR, and was within the near-optimal space for total sugar yield for the LHR. This indicates that the ASE is a good tool for cost effectively finding near-optimal conditions for operating pilot-scale systems. Additionally, using a severity factor approach to optimization was found to be inadequate compared to a multivariate optimization method. Finally, the ASE and the LHR were able to enable significantly higher total sugar yields after enzymatic hydrolysis relative to the ZCR, despite having similar optimal conditions and total xylose yields. This underscores the importance of mechanical disruption during pretreatment to improvement of enzymatic digestibility.

The relationship of age to personal network size, relational multiplexity, and proximity to alters in the Western United States.

OBJECTIVES: This study examines the association of age and other sociodemographic variables with properties of personal networks; using samples of individuals residing in the rural western United States and the City of Los Angeles, we evaluate the degree to which these associations vary with geographical context. For both samples, we test the hypothesis that age is negatively associated with network size (i.e., degree) and positively associated with network multiplexity (the extent of overlap) on 6 different relations: core discussion members, social activity participants, emergency contacts, neighborhood safety contacts, job informants, and kin. We also examine the relationship between age and spatial proximity to alters. METHOD: Our data consist of a large-scale, spatially stratified egocentric network survey containing information about respondents and those to whom they are tied. We use Poisson regression to test our hypothesis regarding degree while adjusting for covariates, including education, gender, race, and self-reported sense of neighborhood belonging. We use multiple linear regression to test our hypotheses on multiplexity and distance to alters. RESULTS: For both rural and urban populations, we find a nonmonotone association between age and numbers of core discussants and emergency contacts, with rural populations also showing nonmonotone associations for social activity partners and kin. These nonmonotone relationships show a peak in expected degree at midlife, followed by an eventual decline. We find a decline in degree among the elderly for all relations in both populations. Age is positively associated with distance to nonhousehold alters for the rural population, although residential tenure is associated with shorter ego-alter distances in both rural and urban settings. Additionally, age is negatively associated with network multiplexity for both populations. DISCUSSION: Although personal network size ultimately declines with age, we find that increases for some relations extend well into late-midlife and most elders still maintain numerous contacts across diverse relations. The evidence we present suggests that older people tap into an wider variety of different network members for different types of relations than do younger people. This is true even for populations in rural settings, for whom immediate access to potential alters is more limited.

Patterns and causes of uncertainty in the American Community Survey.

In 2010 the American Community Survey (ACS) replaced the long form of the United States decennial census. The ACS is now the principal source of high-resolution geographic information about the U.S. population. The margins of error on ACS census tract-level data are on average 75 percent larger than those of the corresponding 2000 long-form estimate. The practical implications of this increase is that data are sometimes so imprecise that they are difficult to use. This paper explains why the ACS tract and block group estimates have large margins of error. Statistical concepts are explained in plain English. ACS margins of error are attributed to specific methodological decisions made by the Census Bureau. These decisions are best seen as compromises that attempt to balance financial constraints against concerns about data quality, timeliness, and geographic precision. In addition, demographic and geographic patterns in ACS data quality are identified. These patterns are associated with demographic composition of census tracts. Understanding the fundamental causes of uncertainty in the survey suggests a number of geographic strategies for improving the usability and quality ACS.

The pattern of association between U.S. economic indicators and infant mortality rates at the state level.

This cross-sectional ecological study examines the pattern of association of state income and income inequality (measured by Gini coefficient) with state infant mortality rates (IMRs) in the U.S. Scatter plots and correlation coefficients were used to examine bivariate associations and bubble plots to examine three-way relationships. Infant mortality rate was positively associated with Gini (R=0.397, p=.004) and negatively with income (R=-0.482, p <.001). However using Black and White IMRs, the associations with Gini were non-significant, but with income remained significant. The bubble plot of Gini versus White IMR (income represented by bubble size) showed increasing IMR as Gini increases and income decreases, except for a subgroup of high-gini, high-income states with low IMRs. State income appears to be a stronger and more consistent predictor of U.S. IMRs for both Black and White races and can explain the pattern of association of White IMR with state Gini coefficient.

Dasymetric Modeling and Uncertainty.

Dasymetric models increase the spatial resolution of population data by incorporating related ancillary data layers. The role of uncertainty in dasymetric modeling has not been fully addressed as of yet. Uncertainty is usually present because most population data are themselves uncertain, and/or the geographic processes that connect population and the ancillary data layers are not precisely known. A new dasymetric methodology - the Penalized Maximum Entropy Dasymetric Model (P-MEDM) - is presented that enables these sources of uncertainty to be represented and modeled. The P-MEDM propagates uncertainty through the model and yields fine-resolution population estimates with associated measures of uncertainty. This methodology contains a number of other benefits of theoretical and practical interest. In dasymetric modeling, researchers often struggle with identifying a relationship between population and ancillary data layers. The PEDM model simplifies this step by unifying how ancillary data are included. The P-MEDM also allows a rich array of data to be included, with disparate spatial resolutions, attribute resolutions, and uncertainties. While the P-MEDM does not necessarily produce more precise estimates than do existing approaches, it does help to unify how data enter the dasymetric model, it increases the types of data that may be used, and it allows geographers to characterize the quality of their dasymetric estimates. We present an application of the P-MEDM that includes household-level survey data combined with higher spatial resolution data such as from census tracts, block groups, and land cover classifications.

Characterization of pilot-scale dilute acid pretreatment performance using deacetylated corn stover.

BACKGROUND: Dilute acid pretreatment is a promising process technology for the deconstruction of low-lignin lignocellulosic biomass, capable of producing high yields of hemicellulosic sugars and enhancing enzymatic yields of glucose as part of a biomass-to-biofuels process. However, while it has been extensively studied, most work has historically been conducted at relatively high acid concentrations of 1 - 4% (weight/weight). Reducing the effective acid loading in pretreatment has the potential to reduce chemical costs both for pretreatment and subsequent neutralization. Additionally, if acid loadings are sufficiently low, capital requirements associated with reactor construction may be significantly reduced due to the relaxation of requirements for exotic alloys. Despite these benefits, past efforts have had difficulty obtaining high process yields at low acid loadings without supplementation of additional unit operations, such as mechanical refining. RESULTS: Recently, we optimized the dilute acid pretreatment of deacetylated corn stover at low acid loadings in a 1-ton per day horizontal pretreatment reactor. This effort included more than 25 pilot-scale pretreatment experiments executed at reactor temperatures ranging from 150 - 170°C, residence times of 10 - 20 minutes and hydrolyzer sulfuric acid concentrations between 0.15 - 0.30% (weight/weight). In addition to characterizing the process yields achieved across the reaction space, the optimization identified a pretreatment reaction condition that achieved total xylose yields from pretreatment of 73.5% ± 1.5% with greater than 97% xylan component balance closure across a series of five runs at the same condition. Feedstock reactivity at this reaction condition after bench-scale high solids enzymatic hydrolysis was 77%, prior to the inclusion of any additional conversion that may occur during subsequent fermentation. CONCLUSIONS: This study effectively characterized a range of pretreatment reaction conditions using deacetylated corn stover at low acid loadings and identified an optimum reaction condition was selected and used in a series of integrated pilot scale cellulosic ethanol production campaigns. Additionally, several issues exist to be considered in future pretreatment experiments in continuous reactor systems, including the formation of char within the reactor, as well as practical issues with feeding herbaceous feedstock into pressurized systems.

Networks, space, and residents' perception of cohesion.

Community scholars increasingly focus on the linkage between residents' sense of cohesion with the neighborhood and their own social networks in the neighborhood. A challenge is that whereas some research only focuses on residents' social ties with fellow neighbors, such an approach misses out on the larger constellation of individuals' relationships and the spatial distribution of those relationships. Using data from the Twin Communities Network Study, the current project is one of the first studies to examine the actual spatial distribution of respondents' networks for a variety of relationships and the consequences of these for neighborhood and city cohesion. We also examine how a perceived structural measure of cohesion-triangle degree-impacts their perceptions of neighborhood and city cohesion. Our findings suggest that perceptions of cohesion within the neighborhood and the city depend on the number of neighborhood safety contacts as well as on the types of people with which they discuss important matters. On the other hand, kin and social friendship ties do not impact cohesion. A key finding is that residents who report more spatially dispersed networks for certain types of ties report lower levels of neighborhood and city cohesion. Residents with higher triangle degree within their neighborhood safety networks perceived more neighborhood cohesion.

A laboratory-scale pretreatment and hydrolysis assay for determination of reactivity in cellulosic biomass feedstocks.

BACKGROUND: The rapid determination of the release of structural sugars from biomass feedstocks is an important enabling technology for the development of cellulosic biofuels. An assay that is used to determine sugar release for large numbers of samples must be robust, rapid, and easy to perform, and must use modest amounts of the samples to be tested.In this work we present a laboratory-scale combined pretreatment and saccharification assay that can be used as a biomass feedstock screening tool. The assay uses a commercially available automated solvent extraction system for pretreatment followed by a small-scale enzymatic hydrolysis step. The assay allows multiple samples to be screened simultaneously, and uses only ~3 g of biomass per sample. If the composition of the biomass sample is known, the results of the assay can be expressed as reactivity (fraction of structural carbohydrate present in the biomass sample released as monomeric sugars). RESULTS: We first present pretreatment and enzymatic hydrolysis experiments on a set of representative biomass feedstock samples (corn stover, poplar, sorghum, switchgrass) in order to put the assay in context, and then show the results of the assay applied to approximately 150 different feedstock samples covering 5 different materials. From the compositional analysis data we identify a positive correlation between lignin and structural carbohydrates, and from the reactivity data we identify a negative correlation between both carbohydrate and lignin content and total reactivity. The negative correlation between lignin content and total reactivity suggests that lignin may interfere with sugar release, or that more mature samples (with higher structural sugars) may have more recalcitrant lignin. CONCLUSIONS: The assay presented in this work provides a robust and straightforward method to measure the sugar release after pretreatment and saccharification that can be used as a biomass feedstock screening tool. We demonstrated the utility of the assay by identifying correlations between feedstock composition and reactivity in a population of 150 samples.

Enzymatic conversion of xylan residues from dilute acid-pretreated corn stover.

Enzymatic conversion of oligomeric xylose and insoluble xylan remaining after effective pretreatment offers significant potential to improve xylan-to-xylose yields while minimizing yields of degredation products and fermentation inhibitors. In this work, a commercial enzyme cocktail is demonstrated to convert up to 70 % of xylo-oligomers found in dilute acid-pretreated hydrolyzate liquor at varying levels of dilution when supplemented with accessory enzymes targeting common side chains. Commercial enzyme cocktails are also shown to convert roughly 80 % of insoluble xylan remaining after effective high-solids, dilute acid pretreatment.

High xylose yields from dilute acid pretreatment of corn stover under process-relevant conditions.

Pretreatment experiments were carried out to demonstrate high xylose yields at high solids loadings in two different batch pretreatment reactors under process-relevant conditions. Corn stover was pretreated with dilute sulfuric acid using a 4-l Steam Digester and a 4-l stirred ZipperClave reactor. Solids were loaded at 45% dry matter (wt/wt) after sulfuric acid catalyst impregnation using nominal particle sizes of either 6 or 18 mm. Pretreatment was carried out at temperatures between 180 and 200 degrees C at residence times of either 90 or 105 s. Results demonstrate an ability to achieve high xylose yields (>80%) over a range of pretreatment conditions, with performance showing little dependence on particle size or pretreatment reactor type. The high xylose yields are attributed to effective catalyst impregnation and rapid rates of heat transfer during pretreatment.

Modeling sucrose hydrolysis in dilute sulfuric acid solutions at pretreatment conditions for lignocellulosic biomass.

Agricultural and herbaceous feedstocks may contain appreciable levels of sucrose. The goal of this study was to evaluate the survivability of sucrose and its hydrolysis products, fructose and glucose, during dilute sulfuric acid processing at conditions typically used to pretreat lignocellulose biomass. Solutions containing 25g/l sucrose with 0.1-2.0% (w/w) sulfuric acid concentrations were treated at temperatures of 160-200 degrees C for 3-12min. Sucrose was observed to completely hydrolyze at all treatment conditions. However, appreciable concentrations of fructose and glucose were detected and glucose was found to be significantly more stable than fructose. Different mathematical approaches were used to fit the kinetic parameters for acid-catalyzed thermal degradation of these sugars. Since both sugars may survive dilute acid pretreatment, they could provide an additional carbon source for production of ethanol and other bio-based products.

Conversion of distiller's grain into fuel alcohol and a higher-value animal feed by dilute-acid pretreatment.

Over the past three decades ethanol production in the United States has increased more than 10-fold, to approx 2.9 billion gal/yr (mid-2003), with ethanol production expected to reach 5 billion gal/yr by 2005. The simultaneous coproduction of 7 million t/yr of distiller's grain (DG) may potentially drive down the price of DG as a cattle feed supplement. The sale of residual DG for animal feed is an important part of corn dry-grind ethanol production economics; therefore, dry-grind ethanol producers are seeking ways to improve the quality of DG to increase market penetration and help stabilize prices. One possible improvement is to increase the protein content of DG by converting the residual starch and fiber into ethanol. We have developed methods for steam explosion, SO2, and dilute-sulfuric acid pretreatment of DG for evaluation as a feedstock for ethanol production. The highest soluble sugar yields (approximately 77% of available carbohydrate) were obtained by pretreatment of DG at 140 degrees C for 20 min with 3.27 wt% H2SO4. Fermentation protocols for pretreated DG were developed at the bench scale and scaled to a working volume of 809 L for production of hydrolyzed distiller's grain (HDG) for feeding trials. The pretreated DG was fermented with Saccharomyces cerevisiae D5A, with ethanol yields of 73% of theoretical from available glucans. The HDG was air-dried and used for turkey-feeding trials. The inclusion of HDG into turkey poult (as a model non-ruminant animal) diets at 5 and 10% levels, replacing corn and soybean meal, showed weight gains in the birds similar to controls, whereas 15 and 20% inclusion levels showed slight decreases (-6%) in weight gain. At the conclusion of the trial, no negative effects on internal organs or morphology, and no mortality among the poults, was found. The high protein levels (58-61%) available in HDG show promising economics for incorporation of this process into corn dry-grind ethanol plants.

Efficacy of a hot washing process for pretreated yellow poplar to enhance bioethanol production.

Cost reductions for pretreatment and bioconversion processes are key objectives necessary to the successful deployment of a bioethanol industry. These unit operations have long been recognized for their impact on the production cost of ethanol. One strategy to achieve this objective is to improve the pretreatment process to produce a pretreated substrate resulting in reduced bioconversion time, lower cellulase enzyme usage, and/or higher ethanol yields. Previous research produced a highly digestible pretreated yellow poplar substrate using a multistage, continuously flowing, very dilute sulfuric acid (0.07% (w/v)) pretreatment. This process reduced the time required for the bioconversion of pretreated yellow poplar sawdust to ethanol. This resulted in a substantially improved yield of ethanol from cellulose. However, the liquid volume requirements, steam demand, and complexity of the flow-through reactor configuration were determined to be serious barriers to commercialization of that process. A reconfigured process to achieve similar performance has been developed using a single-stage batch pretreatment followed by a separation of solids and liquids and washing of the solids at a temperatures between 130 and 150 degrees C. Separation and washing at the elevated temperature is believed to prevent a large fraction of the solubilized lignin and xylan from reprecipitating and/or reassociating with the pretreated solids. This washing of the solids at elevated temperature resulted in both higher recovered yields of soluble xylose sugars and a more digestible pretreated substrate for enzymatic hydrolysis. Key operating variables and process performance indicators included acid concentration, temperature, wash volume, wash temperature, soluble xylose recovery, and performance of the washed, pretreated solids in bioconversion via simultaneous saccharification and fermentation (SSF). Initial results indicated over a 50% increase in ethanol yield at 72 h for the hot washed material as compared to the control (no washing, no separation) and a 43% reduction of in the bioconversion time required for a high ethanol yield from cellulose