Impact of COVID-19 on Clinical Education for Occupational Therapy Students: A 3-Year Longitudinal Study on Fall Risk Prediction Ability.

This study aimed to examine if there were disadvantages to student learning and application when clinical education is canceled due to factors such as COVID-19 pandemic that occurred between 2020-2021. Forty occupational therapy students participated in the study, and they were classified into two groups: those with clinical education (clinical education group) and those without clinical education (inexperienced group). TP-KYT, which assesses a client's ability to predict risk related to falls, was administered in the first and final year. The inexperienced group showed less ability to predict risk related to client falls than the clinical education group.

Construction and Application of Bid Fraud Prediction Model Based on AdaBoost Algorithm

In recent years, with the development of Internet big data technology and e-commerce platform, many active offline transaction methods have gradually shifted to online. Online auctions have come a long way due to COVID-19, but bidding fraud has seriously disrupted the health of the industry. In this paper, the AdaBoost model is used to build a bidding fraud prediction model, and the prediction performance of the model is verified by data experiments, and it is found that it has a high accuracy for identifying bidding fraud. At present, there are few prediction models for bidding fraud, and it has broad development prospects. © 2022 IEEE.

Coronavirus Disease 2019-Associated Pulmonary Aspergillosis: A Noninvasive Screening Model for Additional Diagnostics.

Background: Coronavirus disease 2019 (COVID-19)-associated pulmonary aspergillosis (CAPA) is likely underdiagnosed, and current diagnostic tools are either invasive or insensitive. Methods: A retrospective study of mechanically ventilated patients with COVID-19 admitted to 5 Johns Hopkins hospitals between March 2020 and June 2021 was performed. Multivariable logistic regression was used for the CAPA prediction model building. Performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). Results: In the cohort of 832 patients, 98 (11.8%) met criteria for CAPA. Age, time since intubation, dexamethasone for COVID-19 treatment, underlying pulmonary circulatory diseases, human immunodeficiency virus, multiple myeloma, cancer, or hematologic malignancies were statistically significantly associated with CAPA and were included in the CAPA prediction model, which showed an AUC of 0.75 (95% confidence interval, .70-.80). At a screening cutoff of ≥0.085, it had a sensitivity of 82%, a specificity of 51%, a positive predictive value of 18.6%, and a negative predictive value of 95.3%. (The CAPA screening score calculator is available at www.transplantmodels.com). Conclusions: We developed a CAPA risk score as a noninvasive tool to aid in CAPA screening for patients with severe COVID-19. Our score will also identify a group of patients who are unlikely to have CAPA and who therefore need not undergo additional diagnostics and/or empiric antifungal therapy.

Characteristics Associated with 5-year Fracture Risk vs. 5-year Mortality Risk Among Late-life Men.

BACKGROUND: Identifying late-life men who might benefit from treatment to prevent fracture is challenging given high mortality. Our objective was to evaluate risks of clinical fracture, hip fracture, and mortality prior to fracture among men ≥80 years. METHODS: Study participants included 3,145 community-dwelling men (mean [SD] age 83 [2.8] years) from the Osteoporotic Fractures in Men (MrOS) Study. We used separate multivariable Fine-Gray competing risk models with pre-specified risk factors [age, hip bone mineral density (BMD), recent fracture (<5 years), fall history (previous year), and multimorbidity (# conditions)] to estimate sub-distribution hazard ratios and absolute 5-year risks of any clinical fracture and mortality prior to clinical fracture. Secondary analysis considered hip fracture. RESULTS: There were 414 incident clinical fractures and 595 deaths without prior fracture within 5 years. BMD, fall history, and recent fracture were strong predictors of clinical fracture. Age and multimorbidity were strong predictors of mortality before fracture. After accounting for competing risks, age, BMD, and fall history were each associated with both risk of hip fracture and mortality before hip fracture. Model discrimination varied from 0.65 (mortality before fracture) to 0.79 (hip fracture). Estimated mortality differed substantially among men with similar clinical fracture risk due to modest correlation between fracture risk and competing mortality risk=0.37. CONCLUSIONS: In late-life men, strong risk factors for clinical fracture and hip fracture include fall history, BMD, and recent fracture. Osteoporosis drug treatment decisions may be further enhanced by consideration of fracture risk versus overall life expectancy.

Death Predictors in Patients with COVID-19 on Venovenous ECMO

Background: Patients with coronavirus disease 2019 (COVID-19) and severe acute respiratory distress syndrome (ARDS) need in 10.5 to 15% veno-venous ECMO (V-V ECMO) therapy. The worldwide mortality in COVID-19 patients on ECMO has been described as extremely high with a mortality rate of 40 to 70%. Method(s): We collected data from 56 patients with severe ARDS who received V-V ECMO in 2020 to January 2022 at the University Hospital Magdeburg due to COVID-19 infection. We recorded demographic, pre-, intra-, and posttreatment data retrospectively. We divided the patients into two groups (survivors and nonsurvivors) to build the final prediction model based on our statistic and to detect relevant mortality risk factors. Result(s): Only 39.3% of patients survived the intensive care unit. Compared groups didn't differ in associated diseases. Most of the non-survivors were male (14 [63.6%] vs. 28 [82.4%], p = 0.114). Nonsurvivors showed a higher incidence of bleeding complications (10 [45.5%] vs. 23 [67.6%], p = 0,099), especially hemothorax (1 [4.5%] vs. 7 [20.6%], p = 0.094) and endobronchial bleeding (0 vs. 5 [14.7%], p = 0.059) as well as a higher incidence of bacterial superinfection (9 [40.1%] vs. 22 [64.7%], p = 0.080). Moreover, groups differed concerning the incidence of acute kidney injury without dialysis (1 [4.5% vs. 9 [26.5%], p = 0.036), and acute liver failure (1 [4.5%] vs. 7 [20.6%], p = 0.094). According to the results of bivariate regression analysis, male sex (odd ratio [OR]: 2.66;95% confidence interval [CI]: 0.773-9.194;p = 0.120), major bleeding events (OR: 2.50;95% CI: 0.831-7.574;p = 0.103), bacterial superinfection (OR: 2.65;95% CI: 0.879-7.981;p = 0.084), acute kidney injury without dialysis (OR: 7.56;95% CI: 0.884-64.636;p = 0.065), and acute liver failure (OR: 5.44;95% CI: 0.621-47.756, p = 0.126) were tendentious significant predictors of death. Subsequently, according to the results of multivariate analysis, the most significant factors of mortality were major bleeding events (OR: 3.27;95% CI: 0.888-12.047, p = 0.075) and the bacterial superinfection (OR: 2.81;95% CI: 0.800-9.888, p = 0.107). The mortality prediction model explained 31.8% (Nagelkerke R2) of the variance in-hospital mortality and correctly classified 71.4% of the cases. Conclusion(s): Major bleeding events and bacterial superinfection might be relevant mortality factors in COVID-19 patients on V-V ECMO therapy. Especially prevention of superinfection and strictly anticoagulation management might result in lower mortality rates.

Prediction, Risk Assessment and Comparison of Selected Emerging Markets' Stock Indices During COVID-19 Pandemic Using the Coherent Measure: Comparing Selected Emerging Markets' Stock Indices

In this study, we modeled the log-return of three emerging markets' stock indices, namely, Shanghai SSE, Russia MOEX, and Bombay Stock Exchange Sensex using the generalized hyperbolic family of distributions. We found the generalized hyperbolic family of distributions as the best fit for describing the probability density based on AIC and likelihood ratio test. The coherent risk measure, i.e., the expected shortfall, predicted using the best fit probability distribution, was used as a market risk quantification metric. During the COVID-19 period, the Indian stock market showed maximum market risk, followed by the Russian. The Chinese market showed the least market risk. Our experiment demonstrated a significant (p = 0.000) difference in the three markets concerning the coherent risk at different probability levels from 0.001 to 0.05 in the COVID-19 period using the Jonckheere-Terpstra test. The coherent market risk increased substantially in the Indian and Russian markets during the COVID-19 pandemic compared to the pre-COVID-19 period. However, in the Chinese market, we found that the coherent risk decreased during the COVID-19 period compared to the pre-COVID-19 period. We carried out the empirical study using the adjusted daily closing values of SSE, MOEX, and Sensex from July 2018 to July 2021 and dividing the data sets into pre-COVID-19 and COVID-19 periods based on the first emergence of the COVID-19 case.

COVID-19 severity prediction based on patient risk factors and number of vaccines received

Background: Vaccines are highly effective in preventing severe disease and death from COVID-19, and new medications that can reduce disease severity have been approved. However, many countries are facing limited supply of vaccine doses and medications. Research question: A model estimating the probabilities for hospitalisation and mortality according to individual risk factors and vaccine doses received could help prioritise vaccination and yet scarce medications to maximise lives saved and reduce the burden on hospitalisation facilities. Method(s): Electronic health records from 101,034 individuals infected with SARS-CoV-2, since the beginning of the pandemic and until 30 November 2021, were extracted from a national healthcare organization in Israel. Logistic regression models were built to estimate the risk for subsequent hospitalization and death based on the number of BNT162b2 mRNA vaccine doses received and few major risk factors (age, sex, body mass index, hemoglobin A1C, kidney function, and presence of hypertension, pulmonary disease or malignancy). Result(s): The models built predicts the outcome of newly infected individuals with remarkable accuracy: area under the curve was 0.889 for predicting hospitalisation, and 0.967 for predicting mortality. Even when a breakthrough infection occurs, receiving three vaccination doses significantly reduces the risk of hospitalization by 66% (OR = 0.336) and death by 78% (OR = 0.220). Conclusion(s): The models enable rapid identification of individuals at high risk for hospitalisation and death when infected. These patients can be prioritised to receive booster vaccination and the yet scarce medications. A calculator based on these models is made public: http://covidest.web.app.

Mortality Predictors in Covid-19 Pneumonia: Results of Large Roc-Analisis

High mortality rate is one of most important problems in COVID-19. But still now there are no sufficient data how to predict high probability of death in COVID-19 patients (pts). Aim(s): to determine the most probable markers of death in COVID-19 pts. Material(s) and Method(s): 181 pts, who were hospitalized with COVID-19 pneumonia (male - 97 (43,6%), age - 56,7+/-1,04 yrs, SpO2 at admission - 91,7+/-0,6%). All pts were divided into two groups: group 1 included 167 pts (male - 70 (41,9+/-3,8%), age - 56,5+/-1,1 yrs, SpO2 at admission - 92,1+/-0,6%) who successfully treated and discharged from hospital;group 2 included 14 pts (male - 8 (57,1+/-3,2%) (p=0,270), age - 59,7+/-3,1 yrs (p=0,332), SpO2 at admission - 87,6+/-2,8% (p=0,009)) who died. Measurements: clinical examination, SpO2, chest CT, laboratory: CRP, D-dimer, ferritin, ST-2, fibrinogen. Result(s): ROC-analysis: increasing of D-dimer on level <436 ng/ml, ST-2 on level <180 ng/ml, ferritin on level <799 ng/ml can really be predictors of mortality in pts with COVID-19 pneumonia and predict prognosis of disease. Levels of CRP, fibrinogen are not significant predictors of mortality in COVID-19 pneumonia (Fig.1). Conclusion(s): 1) best parameters for mortality prediction in hospitalized patients with COVID-19 are D-dimer, ST-2, ferritin;2) increasing of D-dimer on level <436 ng/ml, ST-2 on level <180 ng/ml, ferritin on level <799 ng/ml at admission can reflect very high risk of mortality in COVID-19 pneumonia. (Figure Presented).

Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers.

Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.

Predicting COVID-19 progression in hospitalized patients in Belgium from a multi-state model.

Objectives: To adopt a multi-state risk prediction model for critical disease/mortality outcomes among hospitalised COVID-19 patients using nationwide COVID-19 hospital surveillance data in Belgium. Materials and methods: Information on 44,659 COVID-19 patients hospitalised between March 2020 and June 2021 with complete data on disease outcomes and candidate predictors was used to adopt a multi-state, multivariate Cox model to predict patients' probability of recovery, critical [transfer to intensive care units (ICU)] or fatal outcomes during hospital stay. Results: Median length of hospital stay was 9 days (interquartile range: 5-14). After admission, approximately 82% of the COVID-19 patients were discharged alive, 15% of patients were admitted to ICU, and 15% died in the hospital. The main predictors of an increased probability for recovery were younger age, and to a lesser extent, a lower number of prevalent comorbidities. A patient's transition to ICU or in-hospital death had in common the following predictors: high levels of c-reactive protein (CRP) and lactate dehydrogenase (LDH), reporting lower respiratory complaints and male sex. Additionally predictors for a transfer to ICU included middle-age, obesity and reporting loss of appetite and staying at a university hospital, while advanced age and a higher number of prevalent comorbidities for in-hospital death. After ICU, younger age and low levels of CRP and LDH were the main predictors for recovery, while in-hospital death was predicted by advanced age and concurrent comorbidities. Conclusion: As one of the very few, a multi-state model was adopted to identify key factors predicting COVID-19 progression to critical disease, and recovery or death.

Developing and Implementing Predictive Models in a Learning Healthcare System: Traditional and Artificial Intelligence Approaches in the Veterans Health Administration.

Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extra services. The field of predictive models has been revolutionized over the past two decades by electronic health record data; the ability to link such data with other demographic, socioeconomic, and geographic information; the availability of high-capacity computing; and new machine learning and artificial intelligence methods for extracting insights from complex datasets. These advances have produced a new generation of computerized predictive models, but debate continues about their development, reporting, validation, evaluation, and implementation. In this review we reflect on more than 10 years of experience at the Veterans Health Administration, the largest integrated healthcare system in the United States, in developing, testing, and implementing such models at scale. We report lessons from the implementation of national risk prediction models and suggest an agenda for research.

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases. © 2022 Elsevier B.V.

Clinical characteristics and predictors for hospitalisation during the initial phases of the Delta variant COVID-19 outbreak in Sydney, Australia.

OBJECTIVES: The COVID-19 Delta variant of concern continues to pose significant challenges to health systems globally, with increased transmissibility and different patient populations affected. In Sydney, a virtual model of care was implemented in response to the COVID-19 pandemic and Special Health Accommodation (SHA) was made available for community patients with COVID-19 who could not isolate at home or needed health support. METHODS: This retrospective observational cohort study of all patients with COVID-19 Delta variant in SHA during the initial phases of the Delta variant outbreak in Sydney describes the demographic and clinical characteristics of patients with Delta variant COVID-19 and determines predictors of need for in-patient hospital admission. RESULTS: Data from 794 patients were analysed. One hundred and fifty-seven patients (19.8%) were transferred to ED. Of those, 125 were admitted to an in-patient unit (admission rate from ED 79.6%), and of these 30 (24%) went to ICU and seven were intubated. Two patients died within the follow-up period. Age >40 years, obesity, and presence of fever (temperature >37.5°C), hypoxia (oxygen saturation <95%), tachycardia or gastrointestinal symptoms on initial assessment in SHA were independent predictors of in-patient admission with an AUROC of 0.78 (95% confidence interval 0.73, 0.82). CONCLUSIONS: Initial symptoms and vital signs were just as predictive for short-term deterioration as age and pre-existing comorbidities and should be included in future risk prediction models for COVID-19. Based on this, we derive a proposed risk prediction score that incorporates these predictors with further validation required.

Laboratory markers of severity across three COVID-19 outbreaks in Australia: has Omicron and vaccinations changed disease presentation?

COVID-19 has rapidly evolved since it was first discovered in December 2019. We aimed to retrospectively review our experience with COVID-19 infection across 2020-2022, focusing on differences in laboratory markers at presentation. Consecutive adult patients admitted to hospital with confirmed COVID-19 infection were retrospectively reviewed across three periods (29/3/2020-29/9/2020, 16/8/2021-13/10/2021 and 1/1/2022-31/1/2022), correlating with the lineages B.1.338, Delta (B.1.617.2) and Omicron (B.1.1.159), respectively. Laboratory findings of the first requested blood test within 24 h of presentation were recorded and correlated with patient outcome. The primary outcome was requirement for oxygen therapy at any point. Inflammatory markers, namely serum ferritin, lactate dehydrogenase (LDH), C-reactive protein (CRP) were significantly lower on presentation during 2022 compared to 2021, corresponding to a milder disease course. More than 80% of 2022 patients had received 2 or more vaccine doses and fully vaccinated patients displayed significantly lower inflammatory markers at presentation. Using 2022 data, a multivariate prediction model was constructed to predict for oxygen requirement, with c-statistic 0.86. Patients in 2022, corresponding with the Omicron variant, displayed a milder disease course, even in hospitalised patients, with the majority not requiring oxygen and lower inflammatory markers. We constructed a simple-to-use risk prediction model with c-statistic 0.86 which may identify individuals who can be safely managed as outpatients in the era of highly transmissible variants.

Local Naïve Bayes for Predicting Evolution of COVID-19 Patients on Self Organizing Maps

The most recent Clinical Decision Support Systems use the potential of Machine Learning techniques to target clinical problems, avoiding the use of explicit rules. In this paper, a model to monitor and predict the risk of unfavourable evolution (UE) during hospitalization of COVID-19 patients is proposed. It combines Self Organizing Maps and local Naïve Bayes (NB) classifiers because of interpretation purposes. We used the results of six blood tests (leukocytes, D-dimer, among others) provided by a Spanish hospital group. The probabilistic approach allows us to get the daily risk of UE for each patient in an interpretable way. Several variants of the NB classifiers family have been explored, mainly weighting and likelihood estimation (parametric and nonparametric). Despite the over-simplified assumptions of the NB classifiers, they provided good predictive results in terms of sensitivity and specificity. The model with nonparametric likelihood estimation provided the best risk prediction over time even when designed with a limited number of samples. Specifically, the median value and interquartil range for the risk prediction were quite reliable even 10 days before the event day for patients hospitalized longer than 7 days. The risk median values also agree with the gold-standard for patients with a hospital stay shorter than 7 days, though the interquartil range can be too wide (probably because of the variability in the inpatient days - sometimes, just 2 days). Though a deepest analysis considering more patients and features would be convenient, our results show the potential of the proposed approach, both from a technical and clinical viewpoint. © 2022 IEEE.

A Clinical Prediction Rule for Thrombosis in Critically Ill COVID-19 Patients: Step 1 Results of the Thromcco Study.

The incidence of thrombosis in COVID-19 patients is exceptionally high among intensive care unit (ICU)-admitted individuals. We aimed to develop a clinical prediction rule for thrombosis in hospitalized COVID-19 patients. Data were taken from the Thromcco study (TS) database, which contains information on consecutive adults (aged ≥ 18) admitted to eight Spanish ICUs between March 2020 and October 2021. Diverse logistic regression model analysis, including demographic data, pre-existing conditions, and blood tests collected during the first 24 h of hospitalization, was performed to build a model that predicted thrombosis. Once obtained, the numeric and categorical variables considered were converted to factor variables giving them a score. Out of 2055 patients included in the TS database, 299 subjects with a median age of 62.4 years (IQR 51.5-70) (79% men) were considered in the final model (SE = 83%, SP = 62%, accuracy = 77%). Seven variables with assigned scores were delineated as age 25-40 and ≥70 = 12, age 41-70 = 13, male = 1, D-dimer ≥ 500 ng/mL = 13, leukocytes ≥ 10 × 103/µL = 1, interleukin-6 ≥ 10 pg/mL = 1, and C-reactive protein (CRP) ≥ 50 mg/L = 1. Score values ≥28 had a sensitivity of 88% and specificity of 29% for thrombosis. This score could be helpful in recognizing patients at higher risk for thrombosis, but further research is needed.

Clinical Characteristics and Risk Prediction Score in Patients With Mild-to-Moderate Coronavirus Disease 2019 in Japan.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has rapidly spread worldwide, causing widespread mortality. Many patients with COVID-19 have been treated in homes, hotels, and medium-sized hospitals where doctors were responsible for assessing the need for critical care hospitalization. This study aimed to establish a severity prediction score for critical care triage. METHOD: We analyzed the data of 368 patients with mild-to-moderate COVID-19 who had been admitted to Fussa Hospital, Japan, from April 2020 to February 2022. We defined a high-oxygen group as requiring ≥4 l/min of oxygen. Multivariable logistic regression was used to construct a risk prediction score, and the best model was selected using a stepwise selection method. RESULTS: Multivariable analysis showed that older age (≥70 years), elevated creatine kinase (≥127 U/L), C-reactive protein (≥2.19 mg/dL), and ferritin (≥632.7 ng/mL) levels were independent risk factors associated with the high-oxygen group. Each risk factor was assigned a score ranging from 0 to 4, and we referred to the final overall score as the Fussa score. Patients were classified into two groups, namely, high-risk (total risk factors, ≥2) and low-risk (total risk score, <2) groups. The high-risk group had a significantly worse prognosis (low-risk group, undefined vs. high-risk group, undefined; P< 0.0001). CONCLUSIONS: The Fussa score might help to identify patients with COVID-19 who require critical care hospitalization.

A Spatiotemporal Solution to Control COVID-19 Transmission at the Community Scale for Returning to Normalcy: COVID-19 Symptom Onset Risk Spatiotemporal Analysis.

BACKGROUND: Following the recent COVID-19 pandemic, returning to normalcy has become the primary goal of global cities. The key for returning to normalcy is to avoid affecting social and economic activities while supporting precise epidemic control. Estimation models for the spatiotemporal spread of the epidemic at the refined scale of cities that support precise epidemic control are limited. For most of 2021, Hong Kong has remained at the top of the "global normalcy index" because of its effective responses. The urban-community-scale spatiotemporal onset risk prediction model of COVID-19 symptom has been used to assist in the precise epidemic control of Hong Kong. OBJECTIVE: Based on the spatiotemporal prediction models of COVID-19 symptom onset risk, the aim of this study was to develop a spatiotemporal solution to assist in precise prevention and control for returning to normalcy. METHODS: Over the years 2020 and 2021, a spatiotemporal solution was proposed and applied to support the epidemic control in Hong Kong. An enhanced urban-community-scale geographic model was proposed to predict the risk of COVID-19 symptom onset by quantifying the impact of the transmission of SARS-CoV-2 variants, vaccination, and the imported case risk. The generated prediction results could be then applied to establish the onset risk predictions over the following days, the identification of high-onset-risk communities, the effectiveness analysis of response measures implemented, and the effectiveness simulation of upcoming response measures. The applications could be integrated into a web-based platform to assist the antiepidemic work. RESULTS: Daily predicted onset risk in 291 tertiary planning units (TPUs) of Hong Kong from January 18, 2020, to April 22, 2021, was obtained from the enhanced prediction model. The prediction accuracy in the following 7 days was over 80%. The prediction results were used to effectively assist the epidemic control of Hong Kong in the following application examples: identified communities within high-onset-risk always only accounted for 2%-25% in multiple epidemiological scenarios; effective COVID-19 response measures, such as prohibiting public gatherings of more than 4 people were found to reduce the onset risk by 16%-46%; through the effect simulation of the new compulsory testing measure, the onset risk was found to be reduced by more than 80% in 42 (14.43%) TPUs and by more than 60% in 96 (32.99%) TPUs. CONCLUSIONS: In summary, this solution can support sustainable and targeted pandemic responses for returning to normalcy. Faced with the situation that may coexist with SARS-CoV-2, this study can not only assist global cities in responding to the future epidemics effectively but also help to restore social and economic activities and people's normal lives.

A Risk Prediction Model to Identify Newborns at Risk for Missing Early Childhood Vaccinations.

BACKGROUND: Approximately 30% of US children aged 24 months have not received all recommended vaccines. This study aimed to develop a prediction model to identify newborns at high risk for missing early childhood vaccines. METHODS: A retrospective cohort included 9080 infants born weighing ≥2000 g at an academic medical center between 2008 and 2013. Electronic medical record data were linked to vaccine data from the Washington State Immunization Information System. Risk models were constructed using derivation and validation samples. K-fold cross-validation identified risk factors for model inclusion based on alpha = 0.01. For each patient in the derivation set, the total number of weighted adverse risk factors was calculated and used to establish groups at low, medium, or high risk for undervaccination. Logistic regression evaluated the likelihood of not completing the 7-vaccine series by age 19 months. The final model was tested using the validation sample. RESULTS: Overall, 53.6% failed to complete the 7-vaccine series by 19 months. Six risk factors were identified: race/ethnicity, maternal language, insurance status, birth hospitalization length of stay, medical service, and hepatitis B vaccine receipt. Likelihood of non-completion was greater in the high (77.1%; adjusted odds ratio [AOR] 5.6; 99% confidence interval [CI]: 4.2, 7.4) and medium (52.7%; AOR 1.9; 99% CI: 1.6, 2.2) vs low (38.7%) risk groups in the derivation sample. Similar results were observed in the validation sample. CONCLUSIONS: Our prediction model using information readily available in birth hospitalization records consistently identified newborns at high risk for undervaccination. Early identification of high-risk families could be useful for initiating timely, tailored vaccine interventions.

A risk measurement study evaluating the impact of COVID-19 on China's financial market using the QR-SGED-EGARCH model.

Due to the significant impact of COVID-19, financial markets in various countries have undergone drastic fluctuations. Accurately measuring risk in the financial market and mastering the changing rules of the stock market are of great importance to macro-control and financial market management of the government. This paper focuses on the return rate of the Shanghai Composite Index. Using the SGED-EGARCH(1,1) model as a foundation, a quantile regression is introduced to establish the QR-SGED-EGARCH(1,1) model. Further, the corresponding value at risk (VaR) is calculated for a crisis and stable period within each model. To better compare the models, the Cornish-Fisher expansion model is included for comparison. According to the Kupiec test, VaR values calculated by the QR-SGED-EGARCH(1,1) model are superior to other models at different confidence levels most of the time. In addition, to account for the VaR method's inability to effectively measure tail extreme risk, the expected shortfall (ES) method is introduced. The constructed model is used to calculate the corresponding ES values during different periods. According to the evaluation index, the ES values calculated by the QR-SGED-EGARCH(1,1) model have a better effect during a crisis period with the model showing higher accuracy and robustness. It is of great significance for China to better measure financial risk under the impact of a sudden crisis.