Identification Symptoms and Underlying Diseases Related to COVID-19 And Prediction of Death Status Using Artificial Neural Network and Logistic Regression: A Data Mining Approach.

Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province. Method(s): This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19. Result(s): Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively. Conclusion(s): Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.Copyright © 2022 The Authors.

Q&A

In this interview, Sujay Jadhav, global vice president, study start-up, Oracle Health Sciences, touches on how COVID has affected study start-up and what new perspectives it has forced the industry to have on its own challenges. [...]assessing site ability to leverage telehealth will be a factor in site selection. Andy Studna is an Assistant Editor for Applied Clinical Trials Sujay Jadhav Global Vice President, Study Start-Up, Oracle Health Sciences Problems with startup, more than any other phase of a clinical trial, have the greatest potential to increase timelines and budgets.

Patient Recruitment Goes High-Tech

Clinical trial patient recruitment is arguably the most difficult aspect of pharmaceutical development, because it involves a variety of factors beyond study sponsors' control. The aggregation of data across 80 hospitals and 20 systems, for the purpose of understanding patients, doing feasibility studies, or engaging in decentralized recruitment, is the trend we're seeing." Nimita Limaye, PhD, is the vice president of research for the life sciences R&D strategy and technology division at the International Data Corporation (IDC), a market research and advisory firm specializing in the technology industry and headquartered in Boston, Mass. Limaye says the rise of social media-based patient recruitment has opened the door for sponsors and investigators to mine real-world data and to give patients a more central focus in research.

Autologous Conditioned Serum in Lumbar and Cervical Radiculopathy: A Systemic Review

Background: Intervertebral disc degeneration causing radiculopathy is driven by catabolic cytokines like IL-1β and TNFα. Autologous conditioned serum (ACS) was found to be rich in IL-1Ra (Interleukin-1 Receptor Antagonist), and thus, can impede disc degeneration. A systematic review of available literature was conducted to ascertain the potential therapeutic application of ACS in radiculopathy. Methods: Systematic literature reviews were conducted in PubMed, Scopus and Embase databases, up to September 2020. Randomised controlled trials (RCTs), prospective, retrospective studies and case series with lumbar or cervical radiculopathy and reporting use of ACS were included, with at least one of the outcome measures like VAS (Visual Analogue Scale) for pain, SF-12 (Short Form of Health Survey-12), Oswestry Disability Index, with a minimum follow up of 3 months. Animal studies, s, review articles and case reports were excluded. Results: A total of four studies, including 107 patients who received ACS were included based on the eligibility criteria. Two were RCTs and two were prospective non-comparative studies. Three studies evaluated the effect of IL-1Ra on lumbar radiculopathy and one on cervical radiculopathy. The mean age of patients in the studies ranged from 37.15 to 53.9. The dose of ACS used was 2-4 mL injection. In 1 RCT, methylprednisolone was used as control, in the other 5 mg and 10 mg triamcinolone was used. All studies reported a statistically significant reduction in pre-injection and post-injection VAS, there was also a significant difference as compared to 5 mg triamcinolone. Three studies reported significant improvement in ODI. Two studies reported statistically significant improvement in SF-12 scores post injection (p < 0.001). For cervical radiculopathy, Neck pain disability score showed a decrease of 73.76% from pre-injection to final follow up and Neck disability index showed a decrease of 74.47%. Conclusion: All of the four studies concluded that epidural perineural injection with ACS, reduced pain scores (VAS, NPDS) and improved functional scores (ODI, SF-12 and NPDS), as compared to placebo and other conventional therapeutic modalities like steroids, and analgesic-anaesthetic-steroid cocktail. Hence, ACS is a promising new therapeutic modality in both lumbar and cervical radiculopathy, and further studies can strengthen the present evidence regarding its efficacy and safety profile. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

Modeling of Physical Activity Behavioral Interventions Relying on MPC Strategy

Physical inactivity is becoming an important threat to public health in today's society. The COVID-19 pandemic has also reduced physical activity (PA) levels given all the restrictions imposed worldwide. In this work, physical activity interventions supported by mobile devices and relying on control engineering principles were proposed. The model was constructed relying on previous studies that consider a fluid analogy of Social Cognitive Theory (SCT), which is a psychological theory that describes how people acquire and maintain certain behaviors, including health-promoting behaviors, through the interplay of personal, environmental, and behavioral factors. The obtained model was validated using secondary data (collected earlier) from a real intervention with a group of male subjects in Great Britain. The present model was extended with new technology for a better understanding of behavior change interventions. This involved the use of applications, such as phone-based ecological momentary assessments, to collect behavioral data and the inclusion of simulations with logical reward conditions for reaching the behavioral threshold. A goal of 10,000 steps per day is recommended due to the significant link observed between higher daily step counts and lower mortality risk. The intervention was designed using a Model Predictive Control (MPC) algorithm configured to obtain a desired performance. The system was tested and validated using simulation scenarios that resemble different situations that may occur in a real setting.

Selection of forecasting models for infectious diseases in large warships.

As important combat platforms, large warships have the characteristics of compact internal space and dense personnel. Once infectious diseases occur, they are very easy to spread. Therefore, it is very important to select suitable forecasting models for infectious diseases in this environment. This paper introduces 4 classic dynamics models of infectious diseases, summarizes various kinds of compartmental models and their key characteristics, and discusses several common practical simulation requirements, helping relevant health personnel to cope with the challenges in health and epidemic prevention such as the prevention and control of coronavirus disease 2019.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Machine Learning and Deep Learning based Stock Market Prediction considering Covid-19 as a Feature

India's capital markets are witnessing intense uncertainty due to global market failures. Since the outbreak of COVID-19, risk asset prices have plummeted sharply. Risk assets declined half or more compared to the losses in 2008 and 2009. The high volatility is likely to continue in the short term;as a result, the Indian markets have declined sharply. In this paper, we have used different algorithms such as Gated Recurrent Unit, Long Short-Term Memory, Support Vector Regressor, Decision Tree, Random Forest, Lasso Regression, Ridge Regression, Bayesian Ridge Regression, Gradient Boost, and Stochastic Gradient Descent Algorithm to predict financial markets based on historical data available along with economic and financial features during this pandemic. According to our findings, deep learning models can accurately estimate financial indexes by utilizing non-linear transaction data. We found that the Gated Recurrent Unit performs better than the existing model. © 2023 IEEE.

Prediction of Corona Virus Disease Outcome and Future Forecast the Trend of COVID-19 in India: Observational Study

This work is motivated by the disease caused by the novel corona virus Covid-19, rapid spread in India. An encyclopaedic search from India and worldwide social networking sites was performed between 1 March 2020 and 20 Jun 2020. Nowadays social network platform plays a vital role to track spreading behaviour of many diseases earlier then government agencies. Here we introduced the approach to predict and future forecast the disease outcome spread through corona virus in society to give earlier warning to save from life threats. We compiled daily data of Covid-19 incidence from all state regions in India. Five states (Maharashtra, Delhi, Gujarat, Rajasthan and Madhya-Pradesh) with higher incidence and other states considered for time series analysis to construct a predictive model based on daily incidence training data. In this study we have applied the predictive model building approaches like k-nearest neighbour technique, Random-Forest technique and stochastic gradient boosting technique in COVID-19 dataset and the simulated outcome compared with the observed outcome to validate model and measure the performance of model by accuracy (ACC) and Kappa measures. Further forecast the future trends in number of cases of corona virus deceased patients using the Holt Winters Method. Time series analysis is effective tool for predict the outcome of corona virus disease. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Automating Biopharma Manufacturing

"With the advance of data science enabling factors such as easy access to scalable memory and computing resources;our growing competence in collecting, storing, and contextualizing data;advances in robotics;[and] the quickly evolving method landscape driven by the open-source community, the benefits of automation and simulation are becoming accessible in the notoriously complicated realm of biopharma manufacturing," says Marcel von der Haar, head of product strategy data analytics at Sartorius. "Plug-and-play" capabilities of automation systems, which enable flexible manufacturing and faster technology transfer, are more important than ever, he says. Walvax Biotech's new COVID-19 mRNA vaccine plant in China is another example of an intelligent and digital plant;it uses Honeywell's batch process control, building and energy management solution systems, and digital twins to monitor assets (5). "Automation brings in the data for machine learning to model the dynamic processes of cell growth and map it against the multiple dimensions provided by advanced sensors," explains Brandl.

The Predictive Value of Cystatin C for AKI in Patients with COVID-19.

Objective. To evaluate a potential of cystatin C blood concentration to predict acute kidney injury (AKI) in patients with severe and extremely severe pneumonia associated with a COVID-19. Materials and methods. An observational prospective study of 117 patients with severe and extremely severe pneumonia associated with a COVID-19 in an ICU setting was conducted in 2020-2022 (site: multifunctional Medical Center, 1586 Military Clinical Hospital of the Ministry of Defense of Russia, Moscow Region, Russia). Routine laboratory tests and instrumental examinations were performed according to generally accepted protocols. Cystatin C concentrations in blood (s-CysC) and urine (u-CysC) were measured by immunoturbidimetric method. Results. AKI was diagnosed in 21 (17.9%) patients, kidney dysfunction without AKI was found in 22 (18.8%) patients with severe and extremely severe pneumonia associated with COVID-19. s-CysC and u-CysC levels in the group of patients with AKI were statistically significantly higher compared to the levels in the group of patients without AKI. The levels of s-CysC obtained within Day 1 - T (-1), and Day 2 - T (-2) prior to AKI onset turned out to be the independent factors for AKI development in patients with severe and extremely severe pneumonia associated with COVID-19: OR 5.37, Wald chi-square 5.534 (CI: 1.324;21.788);P=0.019 and OR 3.225, Wald chi-square 4.121 (CI: 1.041;9.989);P=0.042, respectively. s-CysC T (-2) value is informative, and s- CysC T (-1) is a highly informative predictor of AKI development in severe and extremely severe pneumonia associated with COVID-19: ROC AUC 0.853 (95% CI, 0.74-0.966), P_0.001) with 90% sensitivity and 73% specificity at a cut-off of 1.67 mg/L, and ROC AUC 0.905 (95% CI, 0.837-0.973), P_0.001) with 90% sensitivity and 73% specificity at a cut-off of 1.69 mg/l, respectively. Serum CysC levels started increasing 3 days prior to AKI onset, outpacing the increase of SCr levels. The u-CysC levels were not predictive of AKI development. Impaired renal function probability was increasing with patients' age (P_0.0001). Conclusions. Serum CysC seems to be a statistically significant predictor of AKI. s-CysC levels started increasing 3 days prior to AKI onset, surpassing the increase of SCr levels in patients with severe and extremely severe pneumonia associated with COVID-19. Urine CysC did not achieve statistical significance as a predictor for AKI, although u-CysC concentrations were significantly higher on days 3, 2, 1 prior to AKI onset and on the day of AKI onset in the group of patients with AKI.Copyright © 2023, V.A. Negovsky Research Institute of General Reanimatology. All rights reserved.

Pancreatic Stone Protein (PSP) as a predictive biomarker of multiple organ failure and clinical outcome

Background: COVID-19 syndrome is associated with high morbidity and mortality in haemodialyzed patients. Pancreatic Stone Protein (PSP) is an early biomarker of sepsis and a prognostic biomarker of disease severity in critically-ill patients and can be rapidly measured at the patient's bedside with a point-of-care-test from a small drop of whole blood. The aim of our pilot was to investigate PSP in patients requiring haemodialysis with SARS-CoV-2 infection, at different severities of COVID-19 disease. Method(s): Between February and July 2021, 23 patients (6 severe COVID-19 with Acute Kidney Injury, 6 moderate COVID-19 haemodialyzed, 2 haemodialyzed without COVID-19 and 3 healthy controls) were recruited at the University Hospital of Foggia for PSP evaluation. Biomarker's measurements were performed within 48 hours after admission or upon arrival for haemodialysis (pre-treatment). PSP was measured at the patient's bedside with "abioSCOPE", a point-of-care test capable of evaluating PSP levels in five minutes from a small drop (50mul) of whole blood or serum. Result(s): The preliminary results of this pilot study showed a trend for PSP to increase along with the severity of disease. In fact, serum PSP levels were significantly higher in Intensive Care Unit subjects than in COVID-19 negative haemodialysis subjects and controls (ANOVA p=0.032). Furthermore, PSP levels were significantly higher in subjects who died (p<0.017). Whether this increase is due to the kidney injury or COVID-19 disease remains unknown, and more research is needed to understand the relationship. Conclusion(s): Several clinical studies published in literature have shown the predictive value of PSP in the early identification of sepsis and severity of the clinical outcome. In our experience we have seen a trend for PSP to increase with disease severity also in COVID-19 patients. These results are preliminary, but PSP was significantly higher in patients who died, in accordance with the literature. This experience also has demonstrated the feasibility of a point of care system to be easily implemented in the unit and adopted by personnel and its design enables fast results and immediate decisions to be taken, especially in urgent situations.

Comparison of Diagnostic Accuracy of CRP Versus Ferritin in COVID Patients as a Marker of Severity of Disease Taking who Severity Classification as Gold Standard

Objective: To determine the diagnostic accuracy of elevated C reactive protein (CRP) and ferritin in predicting severe Covid-19 infection using the World Health Organization's (WHO) Covid-19 severity classification as gold standard. Study Design: Descriptive study. Place and Duration of Study: This study was conducted at the Pak Emirates Military Hospital, Rawalpindi, from January 1st 2021 till April 30th 2021. Ethical review committee's (ERC) approval was taken and good clinical practice guidelines were followed. Material(s) and Method(s): Baseline blood samples were sent to the hospital laboratory for the measurement of C reactive protein and ferritin levels. PCR was taken as gold standard for the diagnosis of Corona virus disease. Patients were classified into severe and non-severe categories using WHO classification of severity. Sensitivity, specificity, diagnostic accuracy, negative predictive value and positive predictive value were calculated for elevated CRP and ferritin. Result(s): There were 65 (57.5%) patients who had severe Covid-19 disease and 48 (42.5%) patients who had non-severe Covid-19 disease. Among the patients with severe Covid-19, 57 (87.7%) had elevated CRP levels, and 50 (76.9%) patients had elevated ferritin levels. Testing ferritin levels, against the severity of Covid-19 patients, there was a sensitivity of 76.9%, specificity of 79.2%, positive predictive value (PPV) of 83.3%, negative predictive value (NPV) of 71.7% and diagnostic accuracy of 77.8%. Testing CRP levels, there was a sensitivity of 87.7%, specificity of 85.4%, PPV of 89.1%, NPV of 83.6% and diagnostic accuracy of 86.7%. Conclusion(s): The results from our study show that CRP has a slightly improved diagnostic accuracy as compared to ferritin. However, both these markers have value in the prediction of severity of Covid-19 infection.Copyright © 2023 Lahore Medical And Dental College. All rights reserved.

Socioemotional functioning as a moderator of the predictive ability of the DIAL-4 on academic achievement

Student performance on kindergarten screening measures and level of kindergarten-entry skills have been shown to be predictive of subsequent academic achievement, thus making kindergarten screening measures a useful tool that guides the monitoring of student progress over time. Though a commonly used tool to assist in kindergarten placement considerations by educators nationwide, the literature is lacking in studies that demonstrate the predictive ability of the Developmental Indicators for the Assessment of Learning - Fourth Edition (DIAL-4) on later academic achievement. Related, behavioral and emotional functioning has been demonstrated to significantly impact student achievement. While the literature supports the predictive ability of kindergarten screening measures on academic performance, research is limited on how behavioral functioning moderates this predictive relationship. The present study aimed to examine the predictive ability of the DIAL-4 on later academic achievement and identify whether behavioral and emotional functioning impacts upon, and to what degree, the relationship between academic achievement and the DIAL-4. Additionally, this study examined the impact of the pause of in-person learning, as caused by the COVID-19 pandemic on student achievement and behavioral and emotional functioning through within-samples comparisons of student functioning in 2019 and 2021 to identify change amongst individual students. The results support the predictive ability of the DIAL-4 on subsequent academic achievement with significant correlations between DIAL-4 scores obtained before kindergarten with subsequent measures of academic achievement. The was no evidence found for a moderation effect of behavioral and emotional functioning on the prediction of academic achievement. Lastly, when controlling for scores on the DIAL-4, the data suggest a decrease in rate of student academic achievement and an increase in emotional and behavioral dysregulation following the onset of the COVID-19 pandemic demonstrated by statistically significant differences in BERI scores as well as significant decreases in rates of growth in reading ability within some cohorts. These findings provide educators with empirical evidence for the utility of the DIAL-4 in predicting academic achievement as well as insight into how the COVID-19 pandemic impacted students' functioning. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

A Predictive Model for Assessing Satisfaction with Online Learning for Higher Education Students During and After COVID-19 Using Data Mining and Machine Learning Techniques: A Case of Jordanian Institutions

Higher education institutions confronted an escalating unexpected pressure to rapidly transform throughout and after the COVID-19 pandemic, by replacing most of the traditional teaching practices with online-based education. Such transformation required institutions to frequently strive for qualities that meet conceptual requirements of traditional education due to its agility and flexibility. The challenge of such electronic learning styles remains in their potential of bringing out many challenges, along with the advantages it has brought to the educational systems and students alike. This research came to shed the light on several factors presented as a predictive model and proposed to contribute to the success or failure in terms of students' satisfaction with online learning. The study took the kingdom of Jordan as a case example country experiencing online education while and after the covid -19 intensive implementation. The study used a dataset collected from a sample of over "300” students using online questionnaires. The questionnaire included "25” attributes mined into the Knime analytics platform. The data was rigorously learned and evaluated by both the "Decision Tree” and "Naive Bayes” algorithms. Subsequently, results revealed that the decision tree classifier outperformed the naïve bayes in the prediction of student satisfaction, additionally, the existence of the sense of community while learning electronically among other reasons had the most contribution to the satisfaction. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Analysis and Prediction of Student Performance Based on Moodle Log Data using Machine Learning Techniques

During the COVID-19 Pandemic, many universities in Thailand were mostly locked down and classrooms were also transformed into a fully online format. It was challenging for teachers to manage online learning and especially to track student behavior since the teacher could not observe and notify students. To alleviate this problem, one solution that has become increasingly important is the prediction of student performance based on their log data. This study, therefore, aims to analyze student behavior data by applying Predictive Analytics through Moodle Log for approximately 54,803 events. Six Machine Learning Classifiers (Neural Network, Random Forest, Decision Tree, Logistic Regression, Linear Regression, and Support Vector Machine) were applied to predict student performance. Further, we attained a comparison of the effectiveness of early prediction for four stages at 25%, 50%, 75%, and 100% of the course. The prediction models could guide future studies, motivate self-preparation and reduce dropout rates. In the experiment, the model with 5-fold cross-validation was evaluated. Results indicated that the Decision Tree performed best at 81.10% upon course completion. Meanwhile, the SVM had the best result at 86.90% at the first stage, at 25% of the course, and Linear Regression performed with the best efficiency at the middle stages at 70.80%, and 80.20% respectively. The results could be applied to other courses and on a larger e-learning systems log that has similar student activity conditions and this could contribute to more accurate student performance prediction © 2023, International Journal of Emerging Technologies in Learning.All Rights Reserved.

Current Advances and Future Avenues in Endocrinology

Endocrinology is a dynamic science with numerous advances in the field of diagnosis, prognosis and management. Newer diagnostic modalities in the field have not only revolutionised the manner glycaemic status in diabetes is assessed but have provided newer metrics of evaluation, including ‘time in range' and the importance of glycaemic variability as an independent association with vascular complications. The focus on lifestyle management for weight and glycaemic optimisation is at an all-time high, especially in terms of time-restricted feeding, intermittent fasting and chrononutrition. Precision and personalised medicine is also foraying into mainstream endocrinology, with potential applications in diabetes mellitus as well as other disorders such as acromegaly and adrenal diseases (phaeochromocytoma/paraganglioma). Genetic testing for clinical and predictive endocrinology is another rapidly advancing domain with use in disease gene identification and discerning the genetic and molecular basis of various endocrine disorders. Avenues for the future implicate improved genetics, epigenetics and environmental factors to understand the intricacies of disease as well as design more effective therapeutic options. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

Vaccine Rate Coverage Forecast in Brazil: Is It Possible to Keep Polio Eradicated?

Objectives: Brazil's annual vaccination coverage rate (AVCR) for Polio has risen to alarming levels in recent years. Given the imminent possibility of the return of the disease eradicated 32 years ago in Brazil, the objective was to assess the historical data of AVCR and foresee the Brazilian performance in the next five years. Method(s): We apply a classic linear forecasting model Holt Winter (HW), composed of a forecasting equation and three corresponding smoothing equations alpha, beta, and gamma. The Polio AVCR between 1994 and 2022 was collected from the National Immunization Program and was evaluated in two stages using the R software involving (i) analysis of data, (ii) application of the HW using least squares adjustment. Result(s): The AVCR showed a growing trend between 1994 (38%) and 1999 (86%). From 2000 to 2015, the average AVCR was 78.72%, with the best coverage in 2015 (95.07%). Between 2016 and 2022, the AVCR was 66.75%, with a tendency to reduce over time. Between 2020 and 2022, AVCR had its lower result (64.44%), which can be explained by the postponement of Polio vaccination due to the COVID-19 pandemic. The best adjustment of smoothing alpha, beta and gamma was achieved (0.67, 0, 0) by HW. The forecast showed positive results in the average AVCR, with a growth of 16.71% in the next five years and with an AVCR projection of just 75.89%, in the case of no public health action is endowed by the country. To reach the best AVCR achieved in 2015, it is necessary to expand it by 48.5%. Conclusion(s): Forecasts using HW are recommended for public health monitoring, helping managers make decisions with limited resources. The results indicate that it is necessary to develop a strategic plan to expand AVCR to keep Polio eradicated from Brazil, mainly due to both disease gravity and treatment unavailability.Copyright © 2023

Frail Scale as Predictor of 30-Day Postoperative Outcomes in Geriatric Surgical Patients at a Tertiary Hospital in the Philippines from June 2020 to June 2021

Introduction: The geriatric population is a growing subset of surgical patients. Specialized surgical risk management is important since physiologic changes are only loosely associated with age. Searching for better risk assessment tools, we come across the 5-point FRAIL scale, a validated measure of weakness and physiologic malfunction resulting to vulnerability to stressors like surgery. Method(s): Our objective was to assess the effectiveness of FRAIL scale in predicting 30-day complications in geriatric surgical patients. We conducted this research at a tertiary hospital in the Philippines from June 2020 to June 2021. Patients were classified preoperatively as frail or robust, and they were monitored 30 days post-surgery for adverse outcomes. Result(s): Out of 100 patients, fifty-seven were frail. Postoperatively, 20% had complications, while 18% expired, with 76% of all adverse outcomes belonging to frail group. FRAIL scale had a significantly better predictive value as compared with Charlson comorbidity index and ACS surgical risk calculator in cases of mortality, but there was no significant difference in predicting morbidity for the three assessment tools. The increase in adverse outcomes compared with previous years was attributed to (1) the proportion of colorectal procedures, and (2) patients were probably in a more advanced stage of illness due to the delays in treatment caused by the COVID-19 pandemic. Conclusion(s): In conclusion, FRAIL scale is an easy-to-use and effective risk assessment tool for geriatric surgical patients. Since most frail patients admit of weakness, resistance training and aerobic exercises may be an appropriate strategy to improve surgical outcomes.

Using Survey Information for Improving the Density Nowcasting of U.S. GDP

We provide a methodology that efficiently combines the statistical models of nowcasting with the survey information for improving the (density) nowcasting of U.S. real GDP. Specifically, we use the conventional dynamic factor model together with stochastic volatility components as the baseline statistical model. We augment the model with information from the survey expectations by aligning the first and second moments of the predictive distribution implied by this baseline model with those extracted from the survey information at various horizons. Results indicate that survey information bears valuable information over the baseline model for nowcasting GDP. While the mean survey predictions deliver valuable information during extreme events such as the Covid-19 pandemic, the variation in the survey participants' predictions, often used as a measure of "ambiguity,” conveys crucial information beyond the mean of those predictions for capturing the tail behavior of the GDP distribution.

Application of Machine Learning Techniques to Predict Visitors to the Tourist Attractions of the Moche Route in Peru

Due to the COVID-19 pandemic, the tourism sector has been one of the most affected sectors and requires management entities to develop urgent measures to reactivate and achieve digital transformation using emerging disruptive technologies. The objective of this research is to apply machine learning techniques to predict visitors to tourist attractions on the Moche Route in northern Peru, for which a methodology based on four main stages was applied: (1) data collection, (2) model analysis, (3) model development, and (4) model evaluation. Public data from official sources and internet data (TripAdvisor and Google Trends) during the period from January 2011 to May 2022 are used. Four algorithms are evaluated: linear regression, KNN regression, decision tree, and random forest. In conclusion, for both the prediction of national and foreign tourists, the best algorithm is linear regression, and the results allow for taking the necessary actions to achieve the digital transformation to promote the Moche Route and, thus, reactivate tourism and the economy in the north of Peru.