Short-term Trends Research Articles

Climate variability in the northern Levant from the highly resolved Qadisha record (Lebanon) during the Holocene optimum

AbstractNew stalagmites from Qadisha Cave (Lebanon) located at 1720 m above sea level provide a high-resolution and well-dated record for northern Mount Lebanon. The stalagmites grew discontinuously from 9.2 to 5.7 and at 3.5 ka, and they show a tendency to move from a more negative oxygen isotope signal at ~9.1 ka to a more positive signal at ~5.8 ka. Such a trend reflects a change from a wetter to a drier climate at high altitudes. The δ13C signal shows rapid shifts throughout the record and a decreasing trend toward more negative values in the mid-Holocene, suggesting enhanced soil activity. In the short-term trend, Qadisha stalagmites record rapid dry/wet changes on centennial scales, with a tendency to more rapid dry events toward the mid-Holocene. Such changes are characterized by overall good agreement between both geochemical proxies and stalagmite growth and might be affected by the seasonal variations in snow cover. The Qadisha record is in good agreement with other Levantine records, showing more humid conditions from 9 to 7 ka. After 7 ka, a drier climate seems to affect sites at both low- and high-altitude areas. The Qadisha record reflects uniquely mountainous climate characteristics compared with other records, specifically the effect of snow cover and its duration regulating the effective infiltration.

Marketing in the Metaverse

The aim of the artice. The purpose of the article is to research marketing tools that can be used by brands in the Metaverse, and to develop a scheme for their use, taking into account the characteristics of the generations that the company is targeting. Analysis results. Currently, the Metaverse is in its nascent stage, but there are already huge opportunities for marketers in the future because the Metaverse is not just a new short-term trend, it is a platform of the future, a new step in the Internet era. Although augmented reality technologies provide an opportunity to make business decisions not in the usual corporate time, but in the so-called "Internet time", the formation of the concept of the communication model in the context of the commercialization of digital assets of the metaspace is insufficiently defined. This forces marketers to think about how they can adapt their strategies to compete in a "virtual" future in the metaspace. The article defines the constructs of metalanguage in the system of recommendations. The considered features of marketing strategies and tools in the Metaverse are illustrated with examples of their successful application depending on the conditions of working with consumers. Generalized information is given on the dependence of the use of certain marketing tools on the behavior characteristics of specific generations of target consumers and the level of integration of the Metaverse into their lives, as well as a model of the choice of marketing tools depending on the level of integration of the Metaverse has been developed. Conclusions and directions for futher research. Due to the right selection of marketing tools in the Metaverse, potential consumers will receive a new exciting experience. In turn, businesses will be able to sell digital products and NFTs at exorbitant prices while increasing their brand awareness. Although the main marketing strategies that can be used by marketers have already been highlighted, their direct use is still ambiguous, and therefore opens up a lot of opportunities for further scientific research on this topic.

Link of the short-term temporal trends of Sr and Nd isotopic composition of aeolian dust over the Arabian Sea with the source emissions

Aeolian transport of continental dust from the Middle East and South Asia to the Arabian Sea (AS) is an important route for delivering key trace metals and nutrients. Despite being surrounded by several deserts, it is not clear which dust source is most likely contributing to mineral aerosols over this marine basin in winter. Substantial information on dust source emissions and transport pathways over the AS is, thus, needed for better constraining the biogeochemical effects in the sunlit surface waters. Here, we investigated the Sr and Nd isotopic composition (87Sr/86Sr and εNd(0)), respectively) of dust samples collected over the AS during a GEOTRACES-India expedition (GI-10: 13 January-10 February 2020). Both tracers, 87Sr/86Sr (0.70957–0.72495) and εNd(0) (−24.0 to −9.3), showed pronounced spatial variability. These proxies were further tagged with their source profiles of surrounding land masses based on the origin of air mass back trajectories (AMBTs). We also encountered two dust storms (DS), one on 27 January 2020 (87Sr/86Sr: 0.70957; εNd(0): −9.3) and the second one on 10 February 2020 (87Sr/86Sr: 0.71474, εNd(0):-12.5), which showed distinct isotopic signatures. AMBTs and satellite imagery together revealed that DS1 is from the Arabian Peninsula and DS2 is from Iran and/or the Indo-Gangetic Plain. Notably, the Sr and Nd isotope composition of DS1 is further consistent with other dust samples collected over the pelagic waters, suggesting the impact of dust outbreaks from the Arabian Peninsula during winter season. Such documentation based on the 87Sr/86Sr and εNd(0) over the Arabian Sea, hitherto, is lacking in literature and, thus, highlights the need for more measurements.

Ethiopian-Somali Conflict 1977—1978: According to US Central Intelligence Agency

The analysis by the US Central Intelligence Agency of the military and political aspects of the Ogaden War (1977—1978) is considered. On the basis of declassified and published documents, the main issues related to the Somali-Ethiopian conflict that worried the CIA are revealed. It establishes the degree of awareness of the Office about certain events related to the war in the Ogaden, and also determines how successful the forecasts made during the conflict were. With the involvement of domestic, American, Ethiopian and Somali historiography, it is shown that the CIA, despite the lack of information, established certain facts quite accurately, but regularly made mistakes in their judgments about further events. The forecasts of the Office regarding a possible conflict between Ethiopia and Somalia in January — July 1977, which turned out to be correct only in small things or in obvious facts, are highlighted. It is shown that the CIA correctly identified the conflict potential in the region, but did not determine exactly where the war would begin. Regarding the role of the CIA in covering the fighting in the Ogaden in July 1977 — March 1978 it is noted that the Office was successful in identifying short-term trends, but did not establish long-term ones.

Monitoring the direction of the short-term trend of economic indicators

Socioeconomic indicators have long been used by official statistical agencies to analyze and assess the current stage at which the economy stands via the application of linear filters used in conjunction with seasonal adjustment procedures. In this study, we propose a new set of symmetric and asymmetric weights that offer substantial gains in real-time by providing timely and more accurate information for detecting short-term trends with respect to filters commonly applied by statistical agencies. We compare the new filters to the classical ones through application to indicators of the US economy, which remains the linchpin of the global economic system. To assess the superiority of the proposed filters, we develop and evaluate explicit tests of the null hypothesis of no difference in revision accuracy of two competing filters. Furthermore, asymptotic and exact finite-sample tests are proposed and illustrated to assess if two compared filters have equal probabilities of failing to detect turning points at different time horizons after their occurrence.

Assessing Rainfall Variability in Jamaica Using CHIRPS: Techniques and Measures for Persistence, Long and Short-Term Trends

Jamaica, as a Small Island Developing State (SIDS), is highly vulnerable to weather extremes. As precipitation persistence is a critical factor in determining the susceptibility of an area to risks, this work assesses the spatial and temporal variations of rainfall persistence in Jamaica from 1981 to 2020, using satellite-based information. The Hurst exponent (H) and the serial correlation coefficient (SCC) are used to evaluate the long-term persistence of precipitation and the Persistence Threshold (PT) concept is introduced to provide a description of rainfall characteristics over short periods, specifically, the number of consecutive days with precipitation above or below a set threshold value. The PT method is a novel concept that expands upon the Consecutive Dry Days (CDD) and Consecutive Wet Days (CWD) methods that only consider a threshold of 1 mm. Results show notable temporal and spatial variations in persistence over the decades, with an overall increasing trend in high precipitation persistence and a decreasing trend in low precipitation persistence. Geographically, the northern mountainous area of Jamaica received the most persistent rainfall over the study period with an observed increase in extreme rainfall events. The excess rainfall of the 2001–2010 decade is remarkable in this study, coinciding with the global unprecedented climate extremes during this time. We conclude that the data used in this study is viable for understanding and modeling rainfall trends in SIDS like Jamaica, and the derived PT method is a useful tool for short-term rainfall trends, but it is just one step toward determining flood or drought risk. Further research will focus on developing drought and flood indices.

Testing indicators for trend assessment of range and habitat of low-density cetacean species in the Mediterranean Sea

IntroductionConservation of cetaceans is challenging due to their large-range, highly-dynamic nature. The EU Habitats Directive (HD) reports 78% of species in ‘unknown’ conservation status, and information on low-density/elusive species such G.griseus, G.melas, Z.cavirostris is the most scattered.MethodsThe FLT-Net programme has regularly collected year-round data along trans-border fixed-transects in the Mediterranean Sea since 2007. Nearly 7,500 cetacean sightings were recorded over 500,000 km of effort with 296 of less-common species. Comparing data across two HD 6-years periods (2013-2019/2008-2012), this study aimed at testing four potential indicators to assess range and habitat short-term trends of G.griseus, G.melas, Z.cavirostris: 1) change in Observed Distributional Range-ODR based on known occurrence, calculated through the Kernel smoother within the effort area; 2) change in Ecological Potential Range-EPR extent, predicted through Spatial Distribution Models; 3) Range Pattern, assessed as overlap and shift of core areas between periods; 4) changes in ODR vs EPR.ResultsMost ODR and EPR confirmed the persistence of known important sites, especially in the Western-Mediterranean. All species, however, exhibit changes in the distribution extent (contraction or expansion) and an offshore shift, possibly indicating exploitation of new areas or avoidance of more impacted ones.DiscussionResults confirmed that the ODR could underestimate the real occupied range, as referring to the effort area only; it can be used to detect trends providing that the spatio-temporal effort scale is representative of species range. The EPR allows generalising species distribution outside the effort area, defining species’ Habitat and the Occupied/Potential Range proportion. To investigate range-trends, EPR needs to be adjusted based also on the Occupied/Potential Range proportion since it could be larger than the occupied range in presence of limiting factors, or smaller, if anthropogenic pressures force the species outside the ecological niche.ConclusionUsing complementary indicators proved valuable to evaluate the significance of changes. The concurrent analysis of more species with similar ecology was also critical to assess whether the detected changes are species-specific or representative of broader trends. The FLT-Net sampling strategy proved adequate for trend assessment in the Western-Mediterranean and Adriatic basins, while more transects are needed to characterize the Central-Mediterranean and Aegean-Levantine ecological variability.

A novel lithium-ion battery capacity prediction framework based on SVMD-AO-DELM

Accurate and efficient lithium-ion battery capacity prediction plays an important role in improving performance and ensuring safe operation. In this study, a novel lithium-ion battery capacity prediction model combining successive variational mode decomposition (SVMD) and aquila optimized deep extreme learning machine (AO-DELM) is proposed. Firstly, SVMD is used to divide capacity signal and it improves short-term trend prediction, especially for capacity growth that occurs during the degradation process. Secondly, the DELM network outperforms other networks in efficiently extracting time-dependent features, and it is more accurate than other standard ELM-based methods. The AO algorithm is used to find the parameters of the DELM training process for the problem of sensitivity to initial weights. Finally, experiments are conducted to validate the predictive performance of the proposed model based on NASA and CALCE lithium-ion batteries sequences. The MAE (0.0066Ah, 0.0044Ah), RMSE (0.0113Ah, 0.0078Ah), MAPE (0.44%, 0.82%) are effectively reduced, and the R2 (98.94%, 99.87%) is better than the prediction performance of other hybrid models.

High-frequency sensor data capture short-term variability in Fe and Mn concentrations due to hypolimnetic oxygenation and seasonal dynamics in a drinking water reservoir

The biogeochemical cycles of iron (Fe) and manganese (Mn) in lakes and reservoirs have predictable seasonal trends, largely governed by stratification dynamics and redox conditions in the hypolimnion. However, short-term (i.e., sub-weekly) trends in Fe and Mn cycling are less well-understood, as most monitoring efforts focus on longer-term (i.e., monthly to yearly) time scales. The potential for elevated Fe and Mn to degrade water quality and impact ecosystem functioning, coupled with increasing evidence for high spatiotemporal variability in other biogeochemical cycles, necessitates a closer evaluation of the short-term Fe and Mn dynamics in lakes and reservoirs. We adapted a UV-visible spectrophotometer coupled with a multiplexor pumping system and partial least squares regression (PLSR) modeling to generate high spatiotemporal resolution predictions of Fe and Mn concentrations in a drinking water reservoir (Falling Creek Reservoir, Vinton, VA, USA) equipped with a hypolimnetic oxygenation (HOx) system. We quantified hourly Fe and Mn concentrations during two transitional periods: reservoir turnover (Fall 2020) and HOx initiation (Summer 2021). Our sensor system successfully predicted mean Fe and Mn concentrations and trends, ground-truthed by grab sampling and laboratory analysis. During fall turnover, hypolimnetic Fe and Mn concentrations began to decrease more than two weeks before complete mixing of the reservoir, with rapid equalization of epilimnetic and hypolimnetic Fe and Mn concentrations in less than 48 h after full water column mixing. During the initiation of HOx in Summer 2021, Fe and Mn displayed distinctly different responses to oxygenation, as indicated by the rapid oxidation of soluble Fe but not soluble Mn. This study demonstrates that Fe and Mn concentrations are sensitive to changes in redox conditions induced by stratification and oxygenation, although their responses to these changes differ. We also show that high spatio-temporal resolution predictions of Fe and Mn can improve drinking water monitoring programs and reservoir management practices.

Data-driven characterization of performance trends in ageing wind turbines

The precise quantification of wind turbine long- and short-term performance is crucial to assess the health state of ageing turbines and to evaluate the benefit of maintenance activities. Indeed, during its lifetime, wind turbines can experience a decay in terms of performance (e.g. due to wear) or improvement (e.g. due to technology optimizations). For this reason, we developed an integrated data-driven methodology to characterize the long- and short-term performance trends and performance variability in turbines. The methodology is validated on a synthetic dataset with imposed decay and then tested on a real wind farm operated by Engie Italy and composed of seven turbines for which ten years of SCADA data are collected. We show how this methodology accurately captures the evolution of a turbine’s performance and how it is capable of quantifying the impact of the controller update.

Analysis and application of deep space exploration satellite telemetry data combined with empirical mode decomposition and ARIMA model

With China’s deep space exploration program constantly expanding, the number of satellites on-orbit is increasing year by year. The stable operation of satellites in orbit hinges on long-term monitoring and analysis of the telemetry data downloaded from the satellite on the ground, which enables quick detection of any irregularities. If we can grasp the changing trend and operation law of satellite telemetry data, we can monitor and control satellites and reduce losses better. Aiming at a large amount of telemetry data of deep space exploration satellites, the data presents non-stationary and nonlinear characteristics, The time series to be processed is constructed sequentially using the acquired telemetry data, and it is decomposed using the empirical mode decomposition approach. The non-stationary signal is divided into a stationary sequence and the sum of a series of stationary intrinsic mode functions. Then the decomposed sets of results are used as the input of the ARIMA model, and the ARIMA model is constructed to predict the predicted value of each decomposed sequence, and the weighted summation of the predicted values is performed to reconstruct the predicted results. Lastly, actual satellite telemetry data is used to confirm the method’s viability. The experimental findings demonstrate that the model can more successfully anticipate the short-term trend of satellite telemetry data.

Remote Sensing-Based Estimates of Changes in Stored Groundwater at Local Scales: Case Study for Two Groundwater Subbasins in California’s Central Valley

Sustainable groundwater management requires high-quality and low-latency estimates of changes in groundwater storage (∆Sgw). However, estimates of ∆Sgw produced using traditional methods, including groundwater models and well-based measurements, typically lag years behind the present because collecting the required on-the-ground data is a time consuming, expensive, and labor-intensive task. Satellite remote sensing measurements provide potential pathways to overcome these limitations by quantifying ∆Sgw through closing the water balance. However, the range of spatial scales over which ∆Sgw can be accurately estimated using remote sensing products remains unclear. To bridge this knowledge gap, this study quantified ∆Sgw for the period of 2002 through to 2021 using the water balance method and multiple remote sensing products in two subbasins (~2700 km2–3500 km2) within California’s Central Valley: (1) the Kaweah–Tule Subbasin, a region where the pumping of groundwater to support agriculture has resulted in decades of decline in head levels, resulting in land subsidence, damage to infrastructure, and contamination of drinking water and (2) the Butte Subbasin, which receives considerably more rainfall and surface water and has not experienced precipitous drops in groundwater. The remote sensing datasets which we utilized included multiple sources for key hydrologic components in the study area: precipitation, evapotranspiration, and soil moisture. To assess the fidelity of the remote sensing-based model, we compared estimates of ∆Sgw to alternative estimates of ∆Sgw derived from independent sources of data: groundwater wells as well as a widely used groundwater flow model. The results showed strong agreement in the Kaweah–Tule Subbasin in long-term ∆Sgw trends and shorter-term trends during droughts, and modest agreement in the Butte Subbasin with remote sensing datasets suggesting more seasonal variability than validation datasets. Importantly, our analysis shows that the timely availability of remote sensing data can potentially enable ∆Sgw estimates at sub-annual latencies, which is timelier than estimates derived through alternate methods, and thus can support adaptive management and decision making. The models developed herein can aid in assessing aquifer dynamics, and can guide the development of sustainable groundwater management practices at spatial scales relevant for management and decision making.

Dynamics and application of a generalized SIQR epidemic model with vaccination and treatment

In this paper, we propose and investigate the SIQR epidemic model with a generalized incidence rate function, a general treatment function and vaccination term. We firstly consider the existence and uniqueness of the global nonnegative solution to the deterministic model. Further, we show the locally asymptotic stability of the disease-free equilibrium and endemic equilibrium of the deterministic model, and obtain the basic reproduction number R0. Then we study the existence and uniqueness of the global positive solution to the stochastic model with any positive initial value. Meanwhile, we obtain sufficient conditions for the extinction of the disease in the stochastic epidemic model, and find that the large noise can make the disease die out exponentially. Finally, we make an empirical analysis by the COVID-19 data of Russia and Serbia. By the performance comparison of different models, it shows that the model with vaccination and treatment we proposed is better for the real situation, which is also verified by different estimation methods. Especially, that shows the recovery rate of the infected increases by 0.042 and the death rate of the recovered is 1.525 times that of normal human in Russia. Through statistical analysis, the short-term trend of epidemic transmission is predicted: under the condition of unchanged prevention and control policies, it may reach a stable endemic equilibrium state in Russia and the epidemic will eventually extinct in Serbia.

Compression of surface texture acceleration signal based on spectrum characteristics

BackgroundAdequate data collection can enhance the realism of online rendering or offline playback of haptic surface textures. A parallel challenge is to reduce communication delays and improve storage space utilization. MethodsBased on the similarity of the short-term amplitude spectrum trend, this study proposes a frequency-domain compression method. A compression framework is designed, which first maps the amplitude spectrum into grayscale images, compresses them with a still image compression method, and then adaptively encodes the maximum amplitude and part of the initial phase for each time window to achieve the final compression. ResultsThe comparison between the original signal and the recovered signal shows that when the time-frequency similarity is 90%, the average compression ratio of our method is 9.85% in the case of a single interaction point. The subjective score for similarity was found to be high, with an average of 87.85. ConclusionsOur method can be used for offline compression of vibrotactile data. For multi-interaction points in space, the trend similarity grayscale image can be reused, and the compression ratio is further reduced.

The fluctuation correlation between investor sentiment and stock index using VMD-LSTM: Evidence from China stock market

The impact of the investor sentiment on China’s capital market price volatility is concerned under the perspective of the behavioral finance. Firstly, in terms of the existing methods of establishing the investor sentiment index, the composite investor sentiment index which include six indicators (five objective indicators and a subjective indicator) are obtained. Secondly, VMD-LSTM (Variational Mode Decomposition and Long Short Term Memory) hybrid neural network model is used to decompose and restructure the investor sentiment index and the Shanghai Security Exchange Composite Index (SSEC) into the short-term, medium-term and long-term trend. Each trend is trained to obtain the forecasting results in three different time scales, and then to achieve the final predicting results by superimposing the output of each trend. Furthermore, compare with other prediction methods, the model can indeed improve the overall predicting accuracy. Finally, GARCH model and the co-integration error regression model are used to discuss the fluctuation correlation and VAR (Vector Auto-regression) models are established to analyze the causality between the stock market indices and the investor sentiment index.

The role of tropical upwelling in explaining discrepancies between recent modeled and observed lower-stratospheric ozone trends

Abstract. Several analyses of satellite-based ozone measurements have reported that lower-stratospheric ozone has declined since the late 1990s. In contrast to this, lower-stratospheric ozone was found to be increasing in specified-dynamics (SD) simulations from the Whole Atmosphere Community Climate Model (WACCM-SD) despite the fact that these simulations are expected to represent the real-world dynamics and chemistry relevant to stratospheric ozone changes. This paper seeks to explain this specific model and observational discrepancy and to more generally examine the relationship between tropical lower-stratospheric upwelling and lower-stratospheric ozone. This work shows that, in general, the standard configuration of WACCM-SD fails to reproduce the tropical upwelling changes present in its input reanalysis fields. Over the period 1998 to 2016, WACCM-SD has a spurious negative upwelling trend that induces a positive near-global lower-stratospheric column ozone trend and that accounts for much of the apparent discrepancy between modeled and observed ozone trends. Using a suite of SD simulations with alternative nudging configurations, it is shown that short-term (∼ 2-decade) lower-stratospheric ozone trends scale linearly with short-term trends in tropical lower-stratospheric upwelling near 85 hPa. However, none of the simulations fully capture the recent ozone decline, and the ozone and upwelling scaling in the WACCM simulations suggests that a large short-term upwelling trend (∼ 6 % decade−1) would be needed to explain the observed satellite trends. The strong relationship between ozone and upwelling, coupled with both the large range of reanalysis upwelling trend estimates and the inability of WACCM-SD simulations to reproduce upwelling from their input reanalyses, severely limits the use of SD simulations for accurately reproducing recent ozone variability. However, a free-running version of WACCM using only surface boundary conditions and a nudged quasi-biennial oscillation produces a positive decadal-scale lower-stratospheric upwelling trend and a negative near-global lower-stratospheric column ozone trend that is in closest agreement with the ozone observations.

A temporal graph framework for intelligence extraction in social media networks

Social media networks (SMNs) are increasingly used in professional management of knowledge workers and related assets. However, the factors affecting behavioral trends and activity levels in these networks are not well understood. Although social and cognitive theories can help to explain human behavior in traditional social networks, their application to SMNs has not been validated. Traditional social network modeling techniques may not accurately predict real-world SMN activities. This research developed a temporal graph framework for intelligence extraction in SMNs. Theory-based, data-driven models (Conformity Model (COM), Recency-Primacy Model (REM), Trend Interaction Model (TIM), Periodic Interaction Model (PIM)) were developed based on the framework to capture various aspects of user behavior: conformity effect, recency, primacy, periodicity, and dynamic trend. The models capture the activity history and dynamically combine pricing information to enhance predictive accuracy. Using data of 83,536 GitHub software repositories on cryptocurrency, this article reports the results of experiments that compare the models’ performance in predicting SMN activities over time. Experimental results show that the model (REM) that captures recency/primacy effects of human cognitive processing outperformed other models in 9 (out of 18) measures pertaining to engagement, contribution, influence, and popularity. Primacy plays a dominant role in predicting engagement, contribution, and popularity, whereas recency plays a key role in predicting influence. Short-term trend (modeled with TIM) was found to yield significantly better performance on predicting user contribution. The models also outperformed an integrated machine learning (IML) model by most measures. Overall, the effects modeled by REM and TIM were found to be more significant than the effects modeled by COM, PIM, and IML. The research contributes to enhancing understanding of SMN behavior, developing new models to simulate and predict SMN activities, and designing new artifacts for information systems practitioners to manage knowledge assets and to extract SMN intelligence.

Abstract P6-05-58: Comparing Patient-Reported Outcomes for Same-day Discharge and Inpatient Admission After Mastectomy

Abstract Background: Same-day discharge after mastectomy has been demonstrated to be safe in appropriately selected candidates. However, the association between same-day discharge after mastectomy and quality of life (QOL) is unclear. Patient-reported outcome (PRO) measures represent important indicators of QOL among patients undergoing treatment for breast cancer. In this study, we aimed to evaluate the effect of same-day discharge after mastectomy on PROs. Methods: We performed a retrospective review of a prospectively collected, longitudinal PRO registry of female breast cancer patients treated at an academic breast center between June 2019 and June 2022. Patients were invited to complete the BREAST-Q module, a validated PRO questionnaire measuring QOL domains such as psychosocial wellbeing, physical wellbeing, satisfaction with their surgeon, and satisfaction with their medical team. Preoperative and 2-week postoperative questionnaire responses were analyzed in this study. Patients who had a mastectomy with or without reconstruction were included in this analysis. Those who underwent immediate reconstruction with autologous tissue were excluded as these operations are not conducive to same-day discharge. Patients were divided into two groups: the first was discharged on the date of surgery while the second was admitted to the hospital for a minimum of one night. Clinical and demographic factors were collected from a review of the electronic medical record. The primary endpoints were mean satisfaction scores as well as differences between postoperative and preoperative scores for the psychosocial and physical wellbeing domains. T-tests were used to evaluate differences between groups. A multiple regression model was fit to adjust for the effects of relevant clinical and demographic factors. Results: A total of 104 patients within the registry underwent mastectomy during the study period and were offered questionnaires. Of these, 58 completed both the preoperative and 2-week postoperative questionnaire (56% response rate); 20 (34%) in the same-day discharge group and 38 (66%) in the inpatient admission group. The groups were similar in age, stage, American Society of Anesthesiologists’ classification group, body mass index, frequency of unplanned readmission or reoperation, and receipt of bilateral mastectomy, axillary lymph node dissection, post-mastectomy reconstruction, and neoadjuvant chemotherapy. Mean patient satisfaction scores and mean changes in psychosocial and physical wellbeing scores were similar between the groups 2 weeks after surgery (Table 1). After controlling for age, type of reconstructive operation, receipt of unplanned reoperation, and preoperative score, same-day discharge did not have a significant effect on satisfaction with the surgeon (Beta=-4.3, p=0.37), satisfaction with the medical team (Beta=0.2, p=0.97), physical wellbeing score (Beta=-0.1, p=0.99), or psychosocial wellbeing score (Beta=8.0, p=0.15). Conclusions: Patients who are discharged from the hospital on the same day of a mastectomy display similar levels of satisfaction with their care team and similar short-term trends in physical and psychosocial wellbeing compared to those who are admitted to the hospital. While further data are being accrued, these early results suggest patients tolerate same-day discharge after mastectomy well. Table 1 Patient-reported outcomes at 2 weeks after mastectomy for patients discharged on the date of surgery compared to those admitted inpatient Citation Format: Sudheer Vemuru, Kathryn Colborn, Laura Leonard, Victoria Huynh, Christodoulos Kaoutzanis, Justin Cohen, David Mathes, Nicole Christian, Gretchen M. Ahrendt, Christine M. Fisher, Simon Kim, Sarah Tevis. Comparing Patient-Reported Outcomes for Same-day Discharge and Inpatient Admission After Mastectomy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-05-58.

Effectiveness analysis of multiple epidemic prevention measures in the context of COVID-19 using the SVIRD model and ensemble Kalman filter

The ability to accurately forecast the spread of coronavirus disease 2019 (COVID-19) is of great importance to the resumption of societal normality. Existing methods of epidemic forecasting often ignore the comprehensive analysis of multiple epidemic prevention measures. This paper aims to analyze various epidemic prevention measures through a compound framework. Here, a susceptible-vaccinated-infected-recovered-deceased (SVIRD) model is constructed to consider the effects of population mobility among origin and destination, vaccination, and positive retest populations. And we further use real-time observations to correct the model trajectory with the help of data assimilation. Seven prevention measures are used to analyze the short-term trend of active cases. The results of the synthetic scene recommended that four measures—improving the vaccination protection rate (IVPR), reducing the number of contacts per person per day (RNCP), selecting the region with less infected people as origin A (SES-O) and limiting population flow entering from A to B per day (LAIP-OD)—are the most effective in the short-term, with maximum reductions of 75%, 53%, 35% and 31%, respectively, in active cases after 150 days. The results of the real-world experiment with Hong Kong as the origin and Shenzhen as the destination indicate that when the daily vaccination rate increased from 5% to 9.5%, the number of active cases decreased by only 7.35%. The results demonstrate that reducing the number of contacts per person per day after productive life resumes is more effective than increasing vaccination rates.

Real-time estimation and forecasting of COVID-19 cases and hospitalizations in Wisconsin HERC regions for public health decision making processes

BackgroundThe spread of the COVID-19 (SARS-CoV-2) and the surging number of cases across the United States have resulted in full hospitals and exhausted health care workers. Limited availability and questionable reliability of the data make outbreak prediction and resource planning difficult. Any estimates or forecasts are subject to high uncertainty and low accuracy to measure such components. The aim of this study is to apply, automate, and assess a Bayesian time series model for the real-time estimation and forecasting of COVID-19 cases and number of hospitalizations in Wisconsin healthcare emergency readiness coalition (HERC) regions.MethodsThis study makes use of the publicly available Wisconsin COVID-19 historical data by county. Cases and effective time-varying reproduction number R_t by the HERC region over time are estimated using Bayesian latent variable models. Hospitalizations are estimated by the HERC region over time using a Bayesian regression model. Cases, effective Rt, and hospitalizations are forecasted over a 1-day, 3-day, and 7-day time horizon using the last 28 days of data, and the 20%, 50%, and 90% Bayesian credible intervals of the forecasts are calculated. The frequentist coverage probability is compared to the Bayesian credible level to evaluate performance.ResultsFor cases and effective R_t, all three time horizons outperform the three credible levels of the forecast. For hospitalizations, all three time horizons outperform the 20% and 50% credible intervals of the forecast. On the contrary, the 1-day and 3-day periods underperform the 90% credible intervals. Questions about uncertainty quantification should be re-calculated using the frequentist coverage probability of the Bayesian credible interval based on observed data for all three metrics.ConclusionsWe present an approach to automate the real-time estimation and forecasting of cases and hospitalizations and corresponding uncertainty using publicly available data. The models were able to infer short-term trends consistent with reported values at the HERC region level. Additionally, the models were able to accurately forecast and estimate the uncertainty of the measurements. This study can help identify the most affected regions and major outbreaks in the near future. The workflow can be adapted to other geographic regions, states, and even countries where decision-making processes are supported in real-time by the proposed modeling system.