SIGNIFICANCE OF ELEVATED SARS-CoV-2 ANTIGEN LEVELS DURING EARLY HOSPITALIZATION

Background: Patients hospitalized with COVID-19 randomized to standard of care (SoC) plus placebo or SoC plus monoclonal antibody (mAb)[bamlanivimab, sotrovimab, amburvimab-romlusevimab, or tixagevimab-cilgagavimab] as separate arms of TICO/ACTIV-3 did not show differences in the time to sustained recovery through day 90. Combining these cohorts, we assessed if early changes in plasma nucleocapsid antigen(pNA) were associated with clinical outcomes. Method(s): TICO/ACTIV-3 enrolled 2,254 patients between 8/5/2020 to 9/30/2021. We used the Quanterix assay to measure pNA of stored samples. We selected those with pNA in the top quartile at baseline through day 5 and examined the association with baseline factors and clinical outcomes through day 90 using regression methods (proportional odds logistic, Cox proportional hazard, and Fine-Gray competing risk models as appropriate). Result(s): Of the 2,149 patients with a baseline value and at least one measurement of pNA on Days 1-5, we found a median age 57 (IQR 46-68), 58% male, 64.9% with one or more co-morbidities, 82.1% unvaccinated, 37.6% with delta variant, median symptom duration 8 days (IQR 6-10), and 9.2% on high flow nasal oxygen (HFNO) or non-invasive ventilation (NIV). Participants with pNA in the top quartile (>4693.5 ng/L at baseline and >29.9 ng/L at day 5) occurred more commonly among those with baseline renal impairment [OR 4.1 (95% CI 2.8 to 5.9)], and pulmonary severity of illness requiring oxygen of < 4 L/min [OR 2.2 (95 %CI: 1.5 to 3.4)], >4 L/min [OR 4.9(95% CI: 3.3, to 7.4)], and HFNO/NIV [OR 5.3 (95% CI: 3.1 to 9.0)] compared to those not using supplemental oxygen at study entry. Patients with positive anti-spike antibody at baseline had lower odds of persistently high pNA [OR 0.15 (95%CI: 0.10 to 0.20)]. Participants with pNA levels in the top quartile through day 5 were associated with increased risk of all-cause day 90 mortality [HR 4.4 (95% CI: 3.2, 5.9)], and reduced incidence of sustained recovery through day 90 [RRR 0.40 (95% CI: 0.35 to 0.45)]. Conclusion(s): PNA levels in the top quartile over the first 5 days were associated with elevated risk for death and reduced recovery. This group includes those with renal impairment, use of oxygen for COVID-19, and negative for anti-spike antibody. Top quartile pNA in early infection identified subjects on lower level of oxygen that were high risk for poor outcomes potentially identifying those that would benefit from additional treatment. (Figure Presented).

Leukocyte Cell Population Data as Potential Markers of Covid-19 Disease Characterization

Background: The usefulness of leukocyte cell population data (CPD) is currently being investigated. In COVID-19 pandemic several reports showed the clinical importance of hematological parameters. Our study aimed to assess CPDs in Sars CoV-2 patients as new disease markers. Method(s): From February to April 2020 (1st wave) 540 and from September to December 2020 (2nd wave) 2821 patients respectively were enrolled. SARS CoV-2 infection diagnosis was carried out by Multiplex rRT-PCR from nasopharyngeal swabs. CPDs were detected by XN 2000 hematology analyzer (Sysmex Corporation). A comparison between two disease waves was performed. Additionally, C-reactive protein (CRP) and lactate dehydrogenase (LDH) were assayed. Result(s): CPDs were classified into: cell complextity, DNA/RNA content and abnormal sized cells. We detected parameters increased from the reference population for all cell types for both 1st and 2nd wave (p<0.05). However, in the 2nd vs 1st wave 5 CPDs vs 9 CPDs were found. In addition we observed higher CPD values of the 1st compared to 2nd wave: (NE-SFL) (p<0.001), (LY-Y) (p<0.0001), (LY-Z) (p<0.0001), (MO-X) (p<0.0001), (MO-Y) (p<0.0001). These findings were confirmed by the higher concentrations of CRP and LDH in the 1st vs 2nd wave: 17.3 mg/L (8.5-59.3) vs 6.3 mg/L (2.3-17.6) (p<0.001) and 241.5 IU/L (201-345) vs 195 IU/L (174-228) (p< 0.001) (median, interquartile range) respectively. Conclusion(s): CPDs showed increased cell activation in 1st wave patients confirmed by clinical and biochemical data, associated with worse clinical conditions. Results highlighted the CPDs as disease characterization markers or useful for a risk model.Copyright © 2023 Sciendo. All rights reserved.

Evaluating the Effect of Activity and Environment on Fall Risk in a Paradigm-Depending Laboratory Setting: Protocol for an Experimental Pilot Study.

BACKGROUND: Knowledge about the causal factors leading to falls is still limited, and fall prevention interventions urgently need to be more effective to limit the otherwise increasing burden caused by falls in older people. To identify individual fall risk, it is important to understand the complex interplay of fall-related factors. Although fall events are common, they are seldom observed, and fall reports are often biased. Due to the rapid development of wearable inertial sensors, an objective approach to capture fall events and the corresponding circumstances is provided. OBJECTIVE: The aim of this work is to operationalize a prototypical dynamic fall risk model regarding 4 ecologically valid real-world scenarios (opening a door, slipping, tripping, and usage of public transportation). We hypothesize that individual fall risk is associated with an interplay of intrinsic risk factors, activity, and environmental factors that can be estimated by using data measured within a laboratory simulation setting. METHODS: We will recruit 30 community-dwelling people aged 60 years or older. To identify several fall-related intrinsic fall risk factors, appropriate clinical assessments will be selected. The experimental setup is adaptable so that the level of fall risk for each activity and each environmental factor is adjustable. By different levels of difficulty, the effect on the risk of falling will be investigated. An 8-camera motion tracking system will be used to record absolute body motions and limits of stability. All laboratory experiments will also be recorded by inertial sensors (L5, dominant leg) and video camera. Logistic regression analyses will be used to model the association between risk factors and falls. Continuous fall risk will be modeled by generalized linear regression models using margin of stability as outcome parameter. RESULTS: The results of this project will prove the concept and establish methods to further use the dynamic fall risk model. Recruitment and measurement initially began in October 2020 but were halted because of the COVID-19 pandemic. Recruitment and measurements recommenced in October 2022, and by February 2023, a total of 25 of the planned 30 subjects have been measured. CONCLUSIONS: In the field of fall prevention, a more precise fall risk model will have a significant impact on research leading to more effective prevention approaches. Given the described burden related to falls and the high prevalence, considerable improvements in fall prevention will have a significant impact on individual quality of life and also on society in general by reducing institutionalization and health care costs. The setup will enable the analysis of fall events and their circumstances ecologically valid in a laboratory setting and thereby will provide important information to estimate the individual instantaneous fall risk. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46930.

Developing a comorbidity score in cancer patients using healthcare utilization databases during the COVID-19 pandemic: An experience from Italy.

BACKGROUND: A strong relationship has been observed between comorbidities and the risk of severe/fatal COVID-19 manifestations, but no score is available to evaluate their association in cancer patients. To make up for this lacuna, we aimed to develop a comorbidity score for cancer patients, based on the Lombardy Region healthcare databases. METHODS: We used hospital discharge records to identify patients with a new diagnosis of solid cancer between February and December 2019; 61 comorbidities were retrieved within 2 years before cancer diagnosis. This cohort was split into training and validation sets. In the training set, we used a LASSO-logistic model to identify comorbidities associated with the risk of developing a severe/fatal form of COVID-19 during the first pandemic wave (March-May 2020). We used a logistic model to estimate comorbidity score weights and then we divided the score into five classes (<=-1, 0, 1, 2-4, >=5). In the validation set, we assessed score performance by areas under the receiver operating characteristic curve (AUC) and calibration plots. We repeated the process on second pandemic wave (October-December 2020) data. RESULTS: We identified 55,425 patients with an incident solid cancer. We selected 21 comorbidities as independent predictors. The first four score classes showed similar probability of experiencing the outcome (0.2% to 0.5%), while the last showed a probability equal to 5.8%. The score performed well in both the first and second pandemic waves: AUC 0.85 and 0.82, respectively. Our results were robust for major cancer sites too (i.e., colorectal, lung, female breast, and prostate). CONCLUSIONS: We developed a high performance comorbidity score for cancer patients and COVID-19. Being based on administrative databases, this score will be useful for adjusting for comorbidity confounding in epidemiological studies on COVID-19 and cancer impact.

Hospital-onset COVID-19 and antivirals

Aim: The PINETREE study showed benefit of remdesivir in non-hospitalised COVID patients. This became the evidence base for the NHSE policy on antivirals use in hospital-onset COVID patients. However, there are differences between PINETREE inclusion criteria and NHSE policy eligibility criteria, and PINETREE was conducted when Delta was dominant. We describe attributes, risk stratification and outcomes in hospital-onset COVID patients when Omicron is dominant. Method(s): A retrospective analysis of patients testing COVID +ve post-admission over 30 days at two district hospitals, collecting risk factors as defined by the QCovid model, and outcomes including days on/off oxygen, survival/discharge at 28 days, and whether antivirals were considered/given. Result(s): 68 eligible cases were identified. CV followed by respiratory diseases were the commonest risk factors. In the 28 days after a +ve test, 31% required supplemental oxygen and 16% died. Being male, and having CV disease, active solid malignancy and recent chemo/radiotherapy were over-represented in patients who died. Supplemental oxygen was associated with significantly higher 28-day mortality risk (43% v 4.3%). Average age of those who died was higher than the overall cohort (84 v 75y). 28-day mortality rates for those who received 1, 2 and 3 COVID vaccines were 60%, 21% and 5% respectively. 18 patients met criteria for highest risk group and were eligible for antivirals. Only 11% were considered for antivirals. Conclusion(s): Despite the milder omicron variant and high vaccination rate, hospital-onset COVID is associated with worse outcomes compared to community clinical trials. The lack of antivirals use according to NHSE criteria should push MDTs to consider a validated risk model for antivirals use.

How do temperature, humidity, and air saturation state affect the COVID-19 transmission risk?

Environmental parameters have a significant impact on the spread of respiratory viral diseases (temperature (T), relative humidity (RH), and air saturation state). T and RH are strongly correlated with viral inactivation in the air, whereas supersaturated air can promote droplet deposition in the respiratory tract. This study introduces a new concept, the dynamic virus deposition ratio (α), that reflects the dynamic changes in viral inactivation and droplet deposition under varying ambient environments. A non-steady-state-modified Wells-Riley model is established to predict the infection risk of shared air space and highlight the high-risk environmental conditions. Findings reveal that a rise in T would significantly reduce the transmission of COVID-19 in the cold season, while the effect is not significant in the hot season. The infection risk under low-T and high-RH conditions, such as the frozen seafood market, is substantially underestimated, which should be taken seriously. The study encourages selected containment measures against high-risk environmental conditions and cross-discipline management in the public health crisis based on meteorology, government, and medical research.

Human Coronavirus OC43 as a Low-Risk Model to Study COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has had irreversible and devastating impacts on every aspect of human life. To better prepare for the next similar pandemic, a clear understanding of coronavirus biology is a prerequisite. Nevertheless, the high-risk nature of the causative agent of COVID-19, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), requires the use of a cumbersome biosafety level-3 (BSL-3) confinement facility. To facilitate the development of preventive and therapeutic measures against SARS-CoV-2, one of the endemic strains of low-risk coronaviruses has gained attention as a useful research alternative: human coronavirus OC43 (HCoV-OC43). In this review, its history, classification, and clinical manifestations are first summarized. The characteristics of its viral genomes, genes, and evolution process are then further explained. In addition, the host factors necessary to support the life cycle of HCoV-OC43 and the innate, as well as adaptive, immunological responses to HCoV-OC43 infection are discussed. Finally, the development of in vitro and in vivo systems to study HCoV-OC43 and its application to the discovery of potential antivirals for COVID-19 by using HCoV-OC43 models are also presented. This review should serve as a concise guide for those who wish to use HCoV-OC43 to study coronaviruses in a low-risk research setting.

Neighborhood Condition Prevalence Rates Correlate With COVID-19 Mortality in Milwaukee County, Wisconsin.

Purpose: We sought to determine if census tract-level (ie, neighborhood) COVID-19 death rates in Milwaukee County correlated with the census tract-level condition prevalence rates (CPRs) for individual COVID-19 mortality risk. Methods: This study used Milwaukee County-reported COVID-19 death rates per 100,000 lives for the 296 census tracts within the county to perform a linear regression with individual COVID-19 mortality risk CPR, mean age, racial composition of census tract (by percentage of non-White residents), and poverty (by percentage within census tract), followed by multiple regression with all 7 CPRs as well as the 7 CPRs combined with the additional demographic variables. CPR estimates were accessed from the Centers for Disease Control and Prevention 500 Cities Project. Demographics were accessed from the U.S. Census. The Milwaukee County Medical Examiner's office identified 898 deaths from COVID-19 in Milwaukee County from March 2020 to June 2021. Results: Among the variables included, crude death rate demonstrated a statistically significant association with the 7 COVID-19 mortality risk CPRs (as analyzed collectively), census tract mean age, and several of the CPRs individually. The addition of census tract age, race, and poverty in multiple regression did not improve the association of the 7 CPRs with crude death rate. Conclusions: Results from this population-level study indicated that census tracts with high COVID-19 mortality correlated with high-risk condition prevalence estimates within those census tracts, illustrating how health data collection and analysis at a census tract level could be helpful when planning pandemic-mitigating public health efforts.

Network analysis between neuron dysfunction and neuroimmune response based on neural single-cell transcriptome of COVID-19 patients.

Despite global vaccination efforts, COVID-19 breakthrough infections caused by variant virus continue to occur frequently, long-term sequelae of COVID-19 infection like neuronal dysfunction emerge as a noteworthy issue. Neuroimmune disorder induced by Inflammatory factor storm was considered as a possible reason, however, little was known about the functional factors affecting neuroimmune response to this virus. Here, using medial prefrontal cortex single cell data of COVID-19 patients, expression pattern analysis indicated that some immune-related pathway genes expressed specifically, including genes associated with T cell receptor, TNF signaling in microglia and Cytokine-cytokine receptor interaction and HIF-1 signaling pathway genes in astrocytes. Besides the well-known immune-related cell type microglia, we also observed immune-related factors like IL17D, TNFRSF1A and TLR4 expressed in Astrocytes. Based on the ligand-receptor relationship of immune-related factors, crosstalk landscape among cell clusters were analyzed. The findings indicated that astrocytes collaborated with microglia and affect excitatory neurons, participating in the process of immune response and neuronal dysfunction. Moreover, subset of astrocytes specific immune factors (hinged neuroimmune genes) were proved to correlate with Covid-19 infection and ventilator-associated pneumonia using multi-tissue RNA-seq and scRNA-seq data. Function characterization clarified that hinged neuroimmune genes were involved in activation of inflammation and hypoxia signaling pathways, which could lead to hyper-responses related neurological sequelae. Finally, a risk model was constructed and testified in RNA-seq and scRNA data of peripheral blood.

Towards Multidimensional Infection Risk Monitoring

Many employees and employers are negatively affected by the ongoing Coronavirus disease pandemic. Although the infection spread has decreased during the summer 2022, the possibility of being infected still is high. There are plenty of industries that are forced to work onsite, and they must ensure a safe work environment by mitigating related risks and their negative effects on the health of employees and enterprise business continuity. In order to take control over the situation in offices, shops, factories and other working places, it is proposed to develop a Covid-19-safe workplace platform for infection risks monitoring and minimization. The platform is based on a risk model, which can help an employer to follow the rules and create safe work conditions for his employees. Scientific articles, safe work environment requirements and recommendations connected to the Covid-19 infection, its spread and control factors were studied and considered. As a result the risk model that has data about risks, their impact, hazard and mitigation measures was created. © 2022 IEEE.

Using a Deep Neural Network in a Relative Risk Model to Estimate Vaccination Protection for COVID-19

The proportional hazard Cox model is traditionally used in survival analysis to estimate the effect of several variables on the hazard rate of an event. Recently, neural networks were proposed to improve the flexibility of the Cox model. In this work, we focus on an extension of the Cox model, namely on a non-proportional relative risk model, where the neural network approximates a non-linear time-dependent risk function. We address the issue of the lack of time-varying variables in this model, and to this end, we design a deep neural network model capable of time-varying regression. The target application of our model is the waning of post-vaccination and post-infection immunity in COVID-19. This task setting is challenging due to the presence of multiple time-varying variables and different epidemic intensities at infection times. The advantage of our model is that it enables a fine-grained analysis of risks depending on the time since vaccination and/or infection, all approximated using a single non-linear function. A case study on a data set containing all COVID-19 cases in the Czech Republic until the end of 2021 has been performed. The vaccine effectiveness for different age groups, vaccine types, and the number of doses received was estimated using our model as a function of time. The results are in accordance with previous findings while allowing greater flexibility in the analysis due to a continuous representation of the waning function. © 2022, Springer Nature Switzerland AG.

The analysis of COVID-19 in-hospital mortality: A competing risk approach or a cure model?

Competing risk analyses have been widely used for the analysis of in-hospital mortality in which hospital discharge is considered as a competing event. The competing risk model assumes that more than one cause of failure is possible, but there is only one outcome of interest and all others serve as competing events. However, hospital discharge and in-hospital death are two outcomes resulting from the same disease process and patients whose disease conditions were stabilized so that inpatient care was no longer needed were discharged. We therefore propose to use cure models, in which hospital discharge is treated as an observed "cure" of the disease. We consider both the mixture cure model and the promotion time cure model and extend the models to allow cure status to be known for those who were discharged from the hospital. An EM algorithm is developed for the mixture cure model. We also show that the competing risk model, which treats hospital discharge as a competing event, is equivalent to a promotion time cure model. Both cure models were examined in simulation studies and were applied to a recent cohort of COVID-19 in-hospital patients with diabetes. The promotion time model shows that statin use improved the overall survival; the mixture cure model shows that while statin use reduced the in-hospital mortality rate among the susceptible, it improved the cure probability only for older but not younger patients. Both cure models show that treatment was more beneficial among older patients.

Optimizing operational strategies for mass transit systems in response to a global pandemic

This study analyzes the risk involved in riding various transit modes during and after a global pandemic. The goal is to identify which factors are related to this risk, how such a relationship can be represented in a manner amenable to analysis, and what a transit operator can do to mitigate the risk while running its service as efficiently as possible. The resulting infection risk model is sensitive to such factors as prevalence of infection, baseline transmission probability, social distance, and expected number of human contacts. Built on this model, we formulate, analyze and test three versions of a transit operator's design problem. In the first, the operator seeks to jointly optimize vehicle capacity and staff testing frequency while keeping the original service schedule and satisfying a predefined infection risk requirement. The second model assumes the operator is obligated to meet the returning demand after the peak of the pandemic. The third allows the operator to run more than one transit line and to allocate limited resources between the lines, subject to the penalty of unserved passengers. We find: (i) The optimal profit, as well as the testing frequency and the vehicle capacity, decreases when passengers expect to come in close contact with more fellow riders in a trip;(ii) Using a larger bus and/or reducing the testing cost enables the operator to both test drivers more frequently and allow more passengers in each bus;(iii) If passengers weigh the risk of riding bus relative to taxi, a higher prevalence of infection has a negative effect on transit operation, whereas a higher baseline transmission probability has a positive effect;(iv) The benefit of improving service capacity and/or testing more frequently is limited given the safety requirement. When the demand rises beyond the range of the capacity needed to maintain sufficient social distancing, the operator has no choice but to increase the service frequency;and (v) In the multi-line case, the lines that have a larger pre-pandemic demand, a higher penalty for each unserved passenger, or a greater exposure risk should be prioritized. © 2022 Elsevier Ltd

Description of Patients Readmitted within 30 Days from COVID-19 Hospitalization

Background. Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has led to increased hospitalizations and utilization of critical care services. There are few studies describing co-morbidities and demographics associated with patients re-admitted within 30-days of discharge. The purpose of this study is to describe this patient population. Methods. This was a single-center, retrospective study at The Ohio State University Wexner Medical Center to identify patients who were admitted secondary to SARS-CoV-2 and required readmission within 30 days due to complications that might be associated with COVID-19. Adults admitted between 3/15/2020 and 11/15/2020 were included in this study. Baseline demographics including age, gender and race in addition to select comorbidities were identified. Results. 250 patients were identified who were readmitted for various reasons. Readmitted patients had a median age of 55 years, 44% were male, and 41.2% were Black/African American. 62.4% of the population was obese (BMI ≥30 kg/m2) with 21.6% with a BMI ≥ 40 kg/m2. The top three co-morbidities seen included Diabetes Mellitus (DM) (32.2%), Hyperlipidemia (48.3%) and Hypertension (51.7%). Conclusion. Though this study lacked a comparator group, it is clear that patients readmitted with all cause etiologies were disproportionally Black/African-American and obese, with a high prevalence of DM, hyperlipidemia, and hypertension. We recommend close monitoring of patients in these groups to reduce COVID19 readmissions. This is the first step in identifying which patients may be more likely to develop complications and required readmission, the next step is to compare these patients to those that were not readmitted to develop a risk model for readmission.

Configurational Approach to Understand Preventive Behavior Adoption among a Non-Adhering Population During the COVID-19 Pandemic.

This study examined the risk preventive responses of individuals, especially those who do not adhere to preventive measures (e.g. anti-maskers) for COVID-19, by integrating three dominant theories in risk and health communication. The complex causal relationship between motivational elements and individuals' preventive behavior adoption was studied using Fuzzy-Set Qualitative Comparative Analysis. With a survey (N = 372) of non-abiding populations, this study found generalizable and unique configurations of motivational elements. Different effects of key motivational variables from three theories were found with different demographic factors. Theoretical and practical implications were also discussed.

POS-421 SITUATIONAL ANALYSIS OF KIDNEY DISEASE IN PATIENTS ATTENDED IN THE PUBLIC SECTOR OF GENERAL PUEYRREDÓN DISTRICT, BUENOS AIRES, ARGENTINA

Introduction: It is estimated that for every patient in a dialysis or transplant program there are, in the general population, 100 patients with less severe chronic kidney disease (CKD) who will probably develop advanced CKD. These patients have an increased morbidity and mortality due to cardiovascular events and, in the most severe cases, a more rapid progression to renal replacement therapy. Screening, support treatment, prevention of complications and renal replacement therapy are strongly influenced by socioeconomic factors and health system organization resulting in inequalities even within the same territory. This situation may have worsened during the COVID‑19 pandemic due to the health emergency and the isolation experienced by people with severe chronic diseases. The General Pueyrredón District has 656,456 inhabitants and it is the 3rd most populated city in Buenos Aires. Approximately 45% of the population is treated in the public health sector. Objectives To describe the clinical-epidemiological characteristics of patients with pathological abnormalities in renal function tests and to identify the main difficulties in the access to diagnosis, follow-up and treatment at first level of care in the public sector from January to June 2021. Methods: The study was conducted in two phases. First, a cross-sectional study of patients with at least one measurement of plasma creatinine greater than 1.3 mg/dL and 1.5 mg/dL in women and men respectively, urine albumin‑to-creatinine ratio (ACR) greater than 30 mg/g and 24-h proteinuria greater than 300 mg. Second, a situational analysis of the municipal health system to identify the main obstacles to provide care to patients with KD. Results: A total of 306 patients with KD were identified in four months, 54.1% were women. The median age was 52 (IR 44-58) and 55 years old (IR 46.4-62) for women and men respectively. Most abnormalities were seen in ACR values, even in 58.5% of cases it was the only pathological value. Glycosylated hemoglobin (HbA1c) values greater than 7% were found in 17.7% of patients with KD. Peritoneal dialysis was used as replacement therapy only in 3% of the cases. Analyzing the difficulties within the health system, deficient articulation between 1st and 2nd levels, lack of consensus in clinical practice, delay in scheduling appointments resulting in a high demand, obstacles regarding patient care, insufficient follow-up of serious patients and delay in the delivery of laboratory results were observed. Conclusions: In this first study, it was observed that 50% of patients were 45‑60 years old and ACR might have been the most sensitive parameter to detect KD. There was a low prevalence of patients with pathological HbA1c values which may indicate an underdiagnose of KD in diabetic patients. An underutilization of peritoneal dialysis was also observed. From an analysis based on a strategic situational planning, it is proposed to adopt different approaches through the design and implementation of a Renal Care Program based on the integrated risk model suggested by KDIGO aimed at organizing the dispersed clinical practice, improving the efficiency of the system, promoting prevention practices and integrating the public sector with the university and private health institutions. No conflict of interest

A Predictive Model for Severe COVID-19 in the Medicare Population: A Tool for Prioritizing Primary and Booster COVID-19 Vaccination.

Recommendations for prioritizing COVID-19 vaccination have focused on the elderly at higher risk for severe disease. Existing models for identifying higher-risk individuals lack the needed integration of socio-demographic and clinical risk factors. Using multivariate logistic regression and random forest modeling, we developed a predictive model of severe COVID-19 using clinical data from Medicare claims for 16 million Medicare beneficiaries and socio-economic data from the CDC Social Vulnerability Index. Predicted individual probabilities of COVID-19 hospitalization were then calculated for population risk stratification and vaccine prioritization and mapping. The leading COVID-19 hospitalization risk factors were non-white ethnicity, end-stage renal disease, advanced age, prior hospitalization, leukemia, morbid obesity, chronic kidney disease, lung cancer, chronic liver disease, pulmonary fibrosis or pulmonary hypertension, and chemotherapy. However, previously reported risk factors such as chronic obstructive pulmonary disease and diabetes conferred modest hospitalization risk. Among all social vulnerability factors, residence in a low-income zip code was the only risk factor independently predicting hospitalization. This multifactor risk model and its population risk dashboard can be used to optimize COVID-19 vaccine allocation in the higher-risk Medicare population.

A simplified tempo-spatial model to predict airborne pathogen release risk in enclosed spaces: An Eulerian-Lagrangian CFD approach.

COVID19 pathogens are primarily transmitted via airborne respiratory droplets expelled from infected bio-sources. However, there is a lack of simplified accurate source models that can represent the airborne release to be utilized in the safe-social distancing measures and ventilation design of buildings. Although computational fluid dynamics (CFD) can provide accurate models of airborne disease transmissions, they are computationally expensive. Thus, this study proposes an innovative framework that benefits from a series of relatively accurate CFD simulations to first generate a dataset of respiratory events and then to develop a simplified source model. The dataset has been generated based on key clinical parameters (i.e., the velocity of droplet release) and environmental factors (i.e., room temperature and relative humidity) in the droplet release modes. An Eulerian CFD model is first validated against experimental data and then interlinked with a Lagrangian CFD model to simulate trajectory and evaporation of numerous droplets in various sizes (0.1 µm-700 µm). A risk assessment model previously developed by the authors is then applied to the simulation cases to identify the horizontal and vertical spread lengths (risk cloud) of viruses in each case within an exposure time. Eventually, an artificial neural network-based model is fitted to the spread lengths to develop the simplified predictive source model. The results identify three main regimes of risk clouds, which can be fairly predicted by the ANN model.