Daily US Research Articles

Daily Fine Resolution Estimates of the Influence of Wildfires on Fine Particulate Matter in California, 2011–2020

Worsening wildfire seasons in recent years are reversing decadal progress on the reduction of harmful air pollutants in the US, particularly in Western states. Measurements of the contributions of wildfire smoke to ambient air pollutants, such as fine particulate matter (PM2.5), at fine resolution scales would be valuable to public health research on climate vulnerable populations and compound climate risks. We estimate the influence of wildfire smoke emissions on daily PM2.5 at fine-resolution, 3 km, for California 2011–2020, using a geostatistical modeled ambient PM2.5 estimate and wildfire smoke plume data from NOAA Hazard Mapping System. Additionally, we compare this product with the US Environmental Protection Agency (EPA) daily and annual standards for PM2.5 exposure. Our results show wildfires significantly influence PM2.5 in California and nearly all exceedances of the daily US EPA PM2.5 standard were influenced by wildfire smoke, while annual exceedances were increasingly attributed to wildfire smoke influence in recent years. This wildfire-influenced PM2.5 product can be applied to public health research to better understand source-specific air pollution impacts and assess the combination of multiple climate hazard risks.

Non-linear relationships between daily temperature extremes and US agricultural yields uncovered by global gridded meteorological datasets

Global agricultural commodity markets are highly integrated among major producers. Prices are driven by aggregate supply rather than what happens in individual countries in isolation. Estimating the effects of weather-induced shocks on production, trade patterns and prices hence requires a globally representative weather data set. Recently, two data sets that provide daily or hourly records, GMFD and ERA5-Land, became available. Starting with the US, a data rich region, we formally test whether these global data sets are as good as more fine-scaled country-specific data in explaining yields and whether they estimate similar response functions. While GMFD and ERA5-Land have lower predictive skill for US corn and soybeans yields than the fine-scaled PRISM data, they still correctly uncover the underlying non-linear temperature relationship. All specifications using daily temperature extremes under any of the weather data sets outperform models that use a quadratic in average temperature. Correctly capturing the effect of daily extremes has a larger effect than the choice of weather data. In a second step, focusing on Sub Saharan Africa, a data sparse region, we confirm that GMFD and ERA5-Land have superior predictive power to CRU, a global weather data set previously employed for modeling climate effects in the region.

Analyzing the volatility spillover and cointegration relationship between daily spot West Texas intermediate crude oil price and US dollar

In the study, it is aimed to analyze the diffusion and cointegration relationship between WTI and US Dollar in the period of 2016-2021. In the study, after a comprehensive literature review of the theoretical review, the econometric analysis section was started. In the first part of the analysis, the short and long-term relationships between the variables were examined with the autoregressive distributed lag methodology and the existence of a cointegration relationship was reached. According to the findings, the effect of WTI on foreign exchange volatility in the long run is statistically significant and negative. In the short-term evaluation, ECT is negative and significant within expectations. In this context, the changes between the variables approach the long-term equilibrium level. According to the results obtained in the causality and variance causality analyzes applied in the last part of the analysis, it is understood that there is a volatility spillover effect from WTI to foreign currency.

Information flow between global financial market stress and African equity markets: An EEMD-based transfer entropy analysis

The flow of information between markets is important to guide investors and policymakers in the effective allocation of assets and proactive market regulation, respectively. This study examines the impact of information flow from global financial market stress on the African stock markets using the daily US financial stress index (USFSI) and other advanced economies' financial stress index (OAEFSI) to proxy the global financial stress index. To understand the information flow dynamics across various investment horizons, the ensemble empirical mode decomposition (EEMD)-based transfer entropy is employed. Our findings reveal that African equity markets are highly risky for information flow from global financial market stress. However, we identify diversification prospects based on market conditions for Ghana and Egypt in the short term and Tanzania, Cote D'Ivoire, and Egypt in the medium term. Empirical results also show that the information flow from global financial stress to African stock markets depends on time scales, economic relations, and the state of global financial markets. The findings are important for investors, portfolio managers, practitioners, and policymakers.

Early postoperative duplex ultrasound findings of the hepatic artery in postoperative vascular complications from paediatric liver transplantation.

To evaluate duplex US findings of the HA in all three postoperative vascular (HA, PV, HV and IVC) complications of paediatric LT for early detection and some helpful secondary signs to determine these vascular complications. We collected data from 44 post-LT paediatric patients who underwent daily duplex US for seven consecutive days and three months after LT during January 2017-June 2020. Four duplex US parameters of the HA (extrahepatic PSV, intrahepatic PSV, RI and AT) were compared in patients with and without complications. The PSV of the extrahepatic HA in patients with HA complications was higher than that in patients without complications (P value = 0.019). The PSV at 107.7cm/s is the optimal cut-off parameter associated with HA complications [a sensitivity of 88.9% and a specificity of 80.0% (ROC area is 0.84)]. The intrahepatic RI was higher on the first day than on the last day and gradually decreased in patients without vascular complications (P value = 0.000). The intrahepatic PSV significantly decreased with time when comparing the first and last days in patients without PV and HV-IVC complications (P value = 0.014 and 0.038). In contrast, patients with vascular complications showed no significant decrease. The extrahepatic PSV relates to HA complications after paediatric LT but not PV and HV-IVC complications. Non-significantly decreased intrahepatic RI and PSV from the first day to the day of complication diagnosis may correlate with the occurrence of vascular complications.

Forecasting Exchange Rate of Pakistan Using Time Series Analysis

Exchange rates are crucial in regulating the foreign exchange market's dynamics. Because of the unpredictability and volatility of currency rates, the exchange rate prediction has become one of the most challenging applications of financial time series forecasting. This study aims to build and compare the accuracy of various methods. The time series model Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) are utilized to forecast the daily US dollar to Pakistan rupee currency exchange rates (USD/PKR). Lagged observations of the data series and moving average technical analysis are used in both models. Explanatory factors were used as indicators, and the prediction performance was assessed using a variety of commonly known statistical metrics. These statistical metrics suggested the presence of conditional heteroscedasticity. Thus, the process turns to capture the volatility effect of conditional heteroscedasticity through GARCH modeling. It may be inferred based on the results of tentative models; that the ARCH model outperforms the GARCH model in terms of predicting the USD/PKR exchange rate.

Clustering-enhanced stock price prediction using deep learning

In recent years, artificial intelligence technologies have been successfully applied in time series prediction and analytic tasks. At the same time, a lot of attention has been paid to financial time series prediction, which targets the development of novel deep learning models or optimize the forecasting results. To optimize the accuracy of stock price prediction, in this paper, we propose a clustering-enhanced deep learning framework to predict stock prices with three matured deep learning forecasting models, such as Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU). The proposed framework considers the clustering as the forecasting pre-processing, which can improve the quality of the training models. To achieve the effective clustering, we propose a new similarity measure, called Logistic Weighted Dynamic Time Warping (LWDTW), by extending a Weighted Dynamic Time Warping (WDTW) method to capture the relative importance of return observations when calculating distance matrices. Especially, based on the empirical distributions of stock returns, the cost weight function of WDTW is modified with logistic probability density distribution function. In addition, we further implement the clustering-based forecasting framework with the above three deep learning models. Finally, extensive experiments on daily US stock price data sets show that our framework has achieved excellent forecasting performance with overall best results for the combination of Logistic WDTW clustering and LSTM model using 5 different evaluation metrics.

Socio-economic and demographic factors influencing the spatial spread of COVID-19 in the USA

As the COVID-19 pandemic progressed in the USA, 'hotspots' shifted geographically over time to suburban and rural counties showing a high prevalence of the disease. We analyse population-adjusted confirmed case rates based on daily US county-level variations in COVID-19 confirmed case counts during the first several months of the pandemic (1 March 2020 through 23 May 2020) to evaluate the spatial dependence between neighbouring counties and quantify the overall spatial effect of socio-economic and demographic factors on the prevalence of COVID-19. We indeed find strong evidence of county-level socio-economic and demographic factors influencing the spatial spread such as sex, race, ethnicity, population density, pollution, health conditions, and income. The relevance of the spatial factors suggests that neighbouring counties have a significant and positive effect on the prevalence of COVID-19.

Tracking Self-reported Symptoms and Medical Conditions on Social Media During the COVID-19 Pandemic: Infodemiological Study.

BackgroundHarnessing health-related data posted on social media in real time can offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time.ObjectiveThis study aimed to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the COVID-19 pandemic, to determine how discussion of these symptoms and medical conditions changed over time, and to identify correlations between frequency of the top 5 commonly mentioned symptoms post and daily COVID-19 statistics (new cases, new deaths, new active cases, and new recovered cases) in the United States.MethodsWe used natural language processing (NLP) algorithms to identify symptom- and medical condition–related topics being discussed on social media between June 14 and December 13, 2020. The sample posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of posts. We also assessed the frequency of health-related discussions on social media over time during the study period, and used Pearson correlation coefficients to identify statistically significant correlations between the frequency of the 5 most commonly mentioned symptoms and fluctuation of daily US COVID-19 statistics.ResultsWithin a total of 9,807,813 posts (nearly 70% were sourced from the United States), we identified a discussion of 120 symptom-related topics and 1542 medical condition–related topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). Changes in posts in the frequency of anxiety, generalized pain, and weight loss were significant but negatively correlated with daily new COVID-19 cases in the United States (r=-0.49, r=-0.46, and r=-0.39, respectively; P<.05). Posts on the frequency of anxiety, generalized pain, weight loss, fatigue, and the changes in fatigue positively and significantly correlated with daily changes in both new deaths and new active cases in the United States (r ranged=0.39-0.48; P<.05).ConclusionsCOVID-19 and symptoms of anxiety were the 2 most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population’s mental health status and enhance public health surveillance for infectious disease.

Is the COVID-19 vaccine effective on the US financial market?

ObjectivesCOVID-19 is the most devastating pandemic that affected humanity and the world economy. This paper aimed to study the time-varying connectedness between the COVID-19 vaccination, infection rate (INFR), and the case fatality ratio (CFR) in the United States and the stock market returns. Study designWe used COVID-19 daily confirmed number of infections, deaths, and vaccinations and the daily US stock market index return. MethodsA wavelet coherence approach was used to assess the co-movement of the US stock market with the COVID-19 vaccination, INFR, and the CFR. ResultsThe COVID-19 vaccination, INFR, and CFR have a positive and significant influence on S&P 500 returns at the majority of business cycle frequencies with an in-phase relation. ConclusionsThe wavelet coherence analysis uncovers strong and significant connectedness between COVID-19 vaccination rate and S&P 500 return. From an economic perspective, the US government should continue its intervention with their vaccination strategy, as it is beneficial for fighting the pandemic. This may lead to the recovery of the stock market as well as to the whole economy.

MO755SPONTANEOUS SOFT TISSUE HEMATOMAS IN ESRD PATIENTS WITH SYMPTOMATIC SARS-CO-V2 INFECTION – THE EXPERIENCE OF AN EMERGENCY NEPHROLOGY AND DIALYSIS DEPARTMENT

Background and AimsWhile actual proofs plead for an increased thrombotic risk in SARS-Co-V2 infection and need for anticoagulant therapy in severe cases, we report suspicious occurrence of hemorrhagic events in a series of ESRD patients.Method59 patients with stage 5 CKD were hospitalized in a two months interval, since our hospital was designated for the admission of mild-severe SARS-Co-V2 patients exclusive. 8 patients were admitted for RRT initiation and 51 were maintenance dialysis patients admitted with SARS-Co-V2-associated complications. Beside hemodialysis and treatment of chronic complications of ESRD, all patients were treated, according to the stage and gravity of SARS-Co-V2 infection and under continuous supervision of an infectious disease physician, with antivirals, antibiotics, anticoagulants and IL-antagonists. Daily or at 2 days laboratory monitoring was performed and consisted in evaluation of inflammatory markers, clotting tests, complete hemogram, D-dimer testing, IL-1 and IL-6 serum levels, serum urea, creatinine and albumin.ResultsIn 6 cases spontaneous soft tissue hematomas were noted, totalizing a number of 9 hematomas documented through ultrasound and CT-scan. In order of frequency, the locations were: 4 rectus abdominalis hematoma, 2 in the external oblique, 2 in gluteus maximus muscle, and one in the right psoas muscle. The source of the bleeding could not be identified in any patient. The occurrence of spontaneous hematomas was correlated with the D-dimer values (p=0.02), prolonged anticoagulation (over 7 days), albumin levels (p=0.01), platelets count (p=0.05). We found no correlation with the serum urea, creatinine, hemoglobin, INR, or with the dialysis age. In all these cases cough was severe. A conservative approach was instituted, with daily US monitoring, withdrawal of anticoagulants and antiplatelet medication, and blood transfusions. 2 patients necessitated surgical procedures and two deaths were recorded.ConclusionIn this brief report, we observed that in chronic hemodialysis patients infected with SARS-Co-V2, risk of hemorrhagic manifestations may overcome the benefits of anticoagulant routine therapy. There is a need to explore further this risk in the future; until then, a careful individualized assessment of the safety and indications for anticoagulant therapy regimens is mandatory for ESRD patients with symptomatic new Coronavirus infection.

Construction of minimum spanning trees from financial returns using rank correlation

The construction of minimum spanning trees (MSTs) from correlation matrices is an often used method to study relationships in the financial markets. However most of the work on this topic tends to use the Pearson correlation coefficient, which relies on the assumption of normality and can be brittle to the presence of outliers, neither of which is ideal for the study of financial returns. In this paper we study the inference of MSTs from daily US, UK and German financial returns using Pearson and two rank correlation methods, Spearman and Kendall’s τ. MSTs constructed using these rank methods tend to be more stable and maintain more edges over the dataset than those constructed using Pearson correlation. The edge agreement between the Pearson and rank MSTs varies significantly depending on the state of the markets, but the rank MSTs generally show strong agreement at all times. Deviation from univariate normality can be related to changes in the correlation matrices but is more difficult to connect to changes in the MSTs. Irrelevant of coefficient, the trees tend to have similar topologies. Portfolios constructed from the MST correlation matrices have a smaller turnover than those from the full covariance matrix for the larger markets, but not for the smaller German market. Using a bootstrap method we find that the correlation matrices constructed using the rank correlations are more robust, but there is little difference between the robustness of the MSTs.

Nutritional Analysis of Foods and Beverages Depicted in Top-Grossing US Movies, 1994-2018

Many countries now restrict advertisements for unhealthy foods. However, movies depict foods and beverages with nutritional quality that is unknown, unregulated, and underappreciated as a source of dietary influence. To compare nutritional content depicted in top-grossing US movies with established nutrition rating systems, dietary recommendations, and US individuals' actual consumption. In this qualitative study, a content analysis was performed from April 2019 to May 2020 of the 250 top-grossing US movies released from 1994 to 2018. The proportion of movies with less healthy nutrition ratings using the Nutrient Profile Index, the proportion of movies with medium or high food nutrition ratings according to the United Kingdom's "traffic light" guidelines (in which green is low and indicates the healthiest foods; amber, medium; and red is high and indicates the least healthy foods), and how the movie-depicted nutritional content compared with US Food and Drug Administration-recommended daily levels and US individuals' actual consumption according to National Health and Nutrition Examination Survey 2015-2016 data. Secondary outcomes compared branded and nonbranded items and tested whether outcomes changed over time or for movies targeting youths. Across 9198 foods and 5748 beverages, snacks and sweets (2173 [23.6%]) and alcoholic beverages (2303 [40.1%]) were most commonly depicted. Alcohol comprised 23 of 127 beverages (18.1%) in G-rated movies, 268 of 992 beverages (27.0%) in PG-rated movies, 1503 of 3592 beverages (41.8%) in PG-13-rated movies, and 509 of 1037 beverages (49.1%) in R-rated movies. Overall, 178 of 245 movies (72.7%) earned less healthy Nutrient Profile Index food ratings and 222 of 246 movies (90.2%) earned less healthy beverage ratings, which would be unhealthy enough to fail legal limits for advertising to youths in the United Kingdom. Among foods, most movies depicted medium or high (amber or red traffic light) levels of sugar (229 of 245 [93.5%]), saturated fat (208 of 245 [84.9%]), total fat (228 of 245 [93.1%]), and, to a lesser extent, sodium (123 of 245 [50.2%]). Only 1721 foods and beverages (11.5%) were visibly branded, but branded items received less healthy nutrition ratings than nonbranded items. Overall, movies failed recommended levels of saturated fat per 2000 kcal by 25.0% (95% CI, 20.6%-29.9%), sodium per 2000 kcal by 3.9% (95% CI, 0.2%-7.9%), and fiber per 2000 kcal by 45.1% (95% CI, 42.9%-47.0%). Movies also depicted 16.5% (95% CI, 12.3%-21.0%) higher total sugar content per 2000 kcal and 313% (95% CI, 298%-329%) higher alcohol content per 2000 kcal than US individuals consume. Neither food nor beverage nutrition scores improved over time or among movies targeting youths. This study suggests that popular US movies depict an unhealthy diet that fails national dietary recommendations, akin to US individuals' actual diets. Depicting unhealthy consumption in media is a sociocultural problem that extends beyond advertisements and branded product placements.

Do Indices Matter? The Influence of Exchange Volatility on Turkish Export Index (TIMEX)

This study examines the effects of daily US dollar returns on the short-term spill of TEA (Turkish Exporters Assembly) export index (TIMEX) returns. The uniqueness of this empirical paper is investigating the influence of indices of that are specifically designed for exporting companies. First, we concluded that there is no asymmetric spread using the modified general autoregressive conditional heteroscedasticity (GARCH) (1,1)-M model. Then, the existence of asymmetric spread was investigated with GJR-GARCH (1,1)-M model and we obtained strong evidence that there is an asymmetric spread from dollar returns to TEA export index returns.

Tell Me a Story: Quantifying Economic Narratives and Their Role during COVID-19

This paper establishes a methodology to elicit and quantify narratives from survey data using textual analysis. We extract thirteen narratives from daily US stockholder questionnaires conducted during the first-wave COVID-19 period and measure their prevalence over time. Survey-based narratives positively correlate with their news and social media analogs, but equivalence is rejected. This discrepancy between sources, arising from social transmission, increases markedly when uncertainty prevails and enforces a negative bias in narrative tone. Information percolation between sources is unidirectional, but heterogeneous across narratives. Narrative importance is evident from their relation to political identity, physical mobility, risk premia, and economic fluctuations.

The impact of high ambient temperatures on delivery timing and gestational lengths

Evidence suggests that heat exposure increases delivery risk for pregnant women. Acceleration of childbirth leads to shorter gestation, which has been linked to later health and cognitive outcomes. However, estimates of the aggregate gestational losses resulting from hot weather are lacking in the literature. Here, we use estimated shifts in daily county birth rates to quantify the gestational losses associated with heat in the United States from 1969 to 1988. We find that extreme heat causes an increase in deliveries on the day of exposure and on the following day and show that the additional births were accelerated by up to two weeks. We estimate that an average of 25,000 infants per year were born earlier as a result of heat exposure, with a total loss of more than 150,000 gestational days annually. Absent adaptation, climate projections suggest additional losses of 250,000 days of gestation per year by the end of the century. Hot weather can cause early childbirth, meaning shorter gestation. Daily US birth-rate data from 1969 to 1988 show that deliveries increased on hot days and that those births occurred up to two weeks early. Around 25,000 infants were born early each year, representing over 150,000 gestational days lost annually.

Estimation of effective doses from MDCT and CBCT imaging of extremities

Little research has been published on the radiation burden of cone beam computed tomography (CBCT) in comparison with multi-row detector computed tomography (MDCT) for extremity imaging. This manuscript reports effective doses produced by standard and reduced dose imaging protocols for a 128-slice MDCT unit and a dedicated extremity imaging device using CBCT technology. Anthropomorphic phantoms with x-ray attenuation characteristics simulating adult hand-wrist, foot-ankle, and knee areas were modified to receive optically stimulated luminescent dosimeters (OSL). Standard and lower kVp reduced dose (Lite) exposures were compared. Effective doses for four age groups (five years old to adult) were calculated following 2007 recommendations of the ICRP. Standard CBCT imaging protocols produced effective doses (1.3–21.1 μSv) that were on the order of daily US ubiquitous background radiation doses. CBCT doses were on average 90% less than comparable MDCT views of the same anatomy (9.1–204 μSv). Child doses were significantly greater than adult doses (p = 0.0001). CBCT doses could be further reduced by 36%–51% with Lite exposure protocols. These protocols can be recommended for children and in cases where increased image noise will not interfere with the diagnostic task.

Sparse-Group Independent Component Analysis with application to yield curves prediction

We propose a Sparse-Group Independent Component Analysis (SG-ICA) method to extract independent factors from high dimensional multivariate data. The method provides a unified and flexible framework that automatically identifies the number of factors and simultaneously estimates a sparse loading matrix, enables us to discover important features and offers improved interpretability of the estimators. We establish the consistency and asymptotic normality of the loading matrix estimator, demonstrate its finite sample performance with simulation studies, and illustrate its application using the daily US Overnight Index Swap rates from Oct 2011 to Mar 2015 with 15 maturities ranging from 1 week to 30 years. With higher efficiency of extracting factors, the forecasting performance of the SG-ICA is remarkably better than the popular parametric DNS model in an era of quantitative easing with short-term interest rate being close to zero.

TIPS Liquidity Premium and Quantitative Easing

In the context of a state-space model for nominal and TIPS yields, we identify the liquidity premium in the TIPS market as the common component in TIPS yields that is unspanned by nominal yields. Using daily US yields, we find that the TIPS liquidity premium explains up to 22% of the variation in TIPS yields and that it sharply spiked during the 2008 financial crisis. We also find evidence of a flight to liquidity effect in the US Treasury market, as yields on nominal bonds decrease following a shock to the liquidity premium in the TIPS market. In addition, a counterfactual exercise shows that the QE2 programme had only limited effect on the liquidity premium in the TIPS market.

Asymmetric and Symmetric Volatility Models for Exchange Rates in India – The Impact of the Net Purchase of US Dollars by the Central Bank and Net Inflows by Foreign Institutional Investors

In this study we model the monthly and the daily US, Euro Zone, UK and Australian exchange rates in India using the symmetric (sGARCH) and the asymmetric (GJR-GARCH and EGARCH) volatility models with the normal, the student t and the skewed student t error distributions. We also investigate the effect of the net US dollars (USD) purchase/ sale by the central bank, the net foreign institutional investor (FII) inflows and the one month forward spot differential on the monthly US exchange rate. Furthermore, we also test the presence of the calendar effect such as the monthly effect and the day-of-week effect on these exchange rates. We find that the models with the normal error distribution tend to fit the monthly log returns of the exchange rates better than those with the non-normal error distribution and the converse is true for the daily log returns. The calendar effects in the mean model are pronounced and Fridays on an average witness an appreciation of the Indian rupee against all the currencies we tested. Month of August has significant impact on the US exchange rate and June on the other three exchange rates. The leverage effect is not pronounced in all the exchange rates. The EGARCH models with the calendar effect dummies in the volatility model are not parsimonious. The net purchase/ sale of USD in a given month by the central bank and the one month forward spot differential do not have any significant impact on the monthly US exchange rate. But the net inflow of USD from foreign institutional investors leads to an appreciation of Indian rupee against USD. Thus the concern that capital inflows, especially the easily repatriable ones, could appreciate the Indian rupee seems to be correct but the net purchase/ sale of USD by the RBI does not seem to be abating the impact.