Risk Propagation Model and Simulation of an Assembled Building Supply Chain Network

In recent years, the prefabricated building supply chain has received strong support from the government and has developed rapidly, but there are various risks in the operation process. In this paper, on the basis of considering asymptomatic infections and relapse, this paper establishes a risk transmission model that considers a recurrent Susceptible–Exposed–Asymptomatic–Infectious–Recovered (abbr. SEAIR) model, systematically analyses the risks in the supply chain, and calculates the risk balance point to conclude that the risks can exist in the supply chain for a long time. By drawing a causal circuit diagram, the relationship between the influencing factors in the process of risk transmission is found, establishing a stock flow map to explore the law of risk propagation. The simulation results using Vensim PLE software show that the five influencing factors of infection rate, transmission rate, government financial support, government policy supervision, and immunity loss ratio have an important impact on the number of risk-unknown enterprises, risk-latent enterprises, risk transmission enterprises, and infection rehabilitation enterprises in risk transmission, and relevant countermeasures to deal with risk transmission in the supply chain are proposed. Theoretically, this paper broadens the ideas for improving infectious disease models. From the management point of view, it reveals how the prefabricated building supply chain enables enterprises to improve their ability to deal with risks through the risk propagation model, providing reference and helping to manage the risks faced by the prefabricated building supply chain.

Changes in Medicaid enrollment during the COVID-19 pandemic across 6 states.

The coronavirus disease 2019 public health emergency (PHE) caused extensive job loss and loss of employer-sponsored insurance. State Medicaid programs experienced a related increase in enrollment during the PHE. However, the composition of enrollment and enrollee changes during the pandemic is unknown. This study examined changes in Medicaid enrollment and population characteristics during the PHE. A retrospective study documenting changes in Medicaid new enrollment and disenrollment, and enrollee characteristics between March and October 2020 compared to the same time in 2019 using full-state Medicaid populations from 6 states of a wide geographical region. The primary outcomes were Medicaid enrollment and disenrollment during the PHE. New enrollment included persons enrolled in Medicaid between March and October 2020 who were not enrolled in January or February, 2020. Disenrollment included persons who were enrolled in March of 2020 but not enrolled in October 2020. The study included 8.50 million Medicaid enrollees in 2020 and 8.46 million in 2019. Overall, enrollment increased by 13.0% (1.19 million) in the selected states during the PHE compared to 2019. New enrollment accounted for 24.9% of the relative increase, while the remaining 75.1% was due to disenrollment. A larger proportion of new enrollment in 2020 was among adults aged 27 to 44 (28.3% vs 23.6%), Hispanics (34.3% vs 32.5%) and in the financial needy (44.0% vs 39.0%) category compared to 2019. Disenrollment included a larger proportion of older adults (26.1% vs 8.1%) and non-Hispanics (70.3% vs 66.4%) than in 2019. Medicaid enrollment grew considerably during the PHE, and most enrollment growth was attributed to decreases in disenrollment rather than increases in new enrollment. Our results highlight the impact of coronavirus disease 2019 on state health programs and can guide federal and state budgetary planning once the PHE ends.

Predicting social media rumours in the context of public health emergencies

The spread of rumours on social media in the context of public health emergencies often distorts perceptions of public events and obstructs crisis management. Microblog entries about 28 rumour cases are collected on Sina Weibo during the COVID-19 outbreak. The Modality–Agency–Interactivity–Navigability model is used to identify the key factors of rumour prediction. To investigate the relationship among information modality, information content, information source and rumour identification, the binary logistic regression model is established based on the features of users and microblog entries. In addition, we propose a multi-feature rumour prediction model based on the Bidirectional Encoder Representations from Transformers (BERT) and Extreme Gradient Boosting (XGBoost) models. The proposed rumour prediction model has the best performance compared with other models. The feature importance is then calculated by the SHapley Additive exPlanations (SHAP), which can also explain the XGBoost results. It is shown that the likelihood that microblog entries are rumours decreases as the values of variables such as user influence and the positive sentiment of comments rise. Microblog entries posted on Thursdays or at noon are more probably to be rumours than those posted at other time. The proposed model can assist emergency management departments in establishing a feasible rumour prediction mechanism to guide public opinion against rumours. [ FROM AUTHOR]

Durability of Protection Post-Primary COVID-19 Vaccination in the United States.

The durability of immune responses after COVID-19 vaccination will drive long-term vaccine effectiveness across settings and may differ by vaccine type. To determine durability of protection of COVID-19 vaccines (BNT162b2, mRNA-1273, and Ad26.COV2.S) following primary vaccination in the United States, a matched case-control study was conducted in three cohorts between 1 January and 7 September 2021 using de-identified data from a database covering 168 million lives. Odds ratios (ORs) for developing outcomes of interest (breakthrough SARS-CoV-2 infection, hospitalization, or intensive care unit admission) were determined for each vaccine (no direct comparisons). In total, 17,017,435 individuals were identified. Relative to the baseline, stable protection was observed for Ad26.COV2.S against infections (OR [95% confidence interval (CI)], 1.31 [1.18-1.47]) and hospitalizations (OR [95% CI], 1.25 [0.86-1.80]). Relative to the baseline, protection waned over time against infections for BNT162b2 (OR [95% CI], 2.20 [2.01-2.40]) and mRNA-1273 (OR [95% CI], 2.07 [1.87-2.29]) and against hospitalizations for BNT162b2 (OR [95% CI], 2.38 [1.79-3.17]). Baseline protection remained stable for intensive care unit admissions for all three vaccines. Calculated baseline VE was consistent with published literature. This study suggests that the three vaccines in three separate populations may have different durability profiles.

Durability of Protection Post–Primary COVID-19 Vaccination in the United States

The durability of immune responses after COVID-19 vaccination will drive long-term vaccine effectiveness across settings and may differ by vaccine type. To determine durability of protection of COVID-19 vaccines (BNT162b2, mRNA-1273, and Ad26.COV2.S) following primary vaccination in the United States, a matched case-control study was conducted in three cohorts between 1 January and 7 September 2021 using de-identified data from a database covering 168 million lives. Odds ratios (ORs) for developing outcomes of interest (breakthrough SARS-CoV-2 infection, hospitalization, or intensive care unit admission) were determined for each vaccine (no direct comparisons). In total, 17,017,435 individuals were identified. Relative to the baseline, stable protection was observed for Ad26.COV2.S against infections (OR [95% confidence interval (CI)], 1.31 [1.18–1.47]) and hospitalizations (OR [95% CI], 1.25 [0.86–1.80]). Relative to the baseline, protection waned over time against infections for BNT162b2 (OR [95% CI], 2.20 [2.01–2.40]) and mRNA-1273 (OR [95% CI], 2.07 [1.87–2.29]) and against hospitalizations for BNT162b2 (OR [95% CI], 2.38 [1.79–3.17]). Baseline protection remained stable for intensive care unit admissions for all three vaccines. Calculated baseline VE was consistent with published literature. This study suggests that the three vaccines in three separate populations may have different durability profiles.

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study.

BACKGROUND: For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. OBJECTIVE: This study aimed to develop and test the feasibility of a "patients-like-me" framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. METHODS: Our framework used COVID-19-like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19-like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19-like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. RESULTS: Compared to the COVID-19-like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P<.001). Patients with COVID-19 had a lower IMV rate (15.1 versus 23.2, P=.02) and shorter time to IMV (2.9 versus 4.1 days, P<.001) compared to the COVID-19-like patients. In the COVID-19-like training data, the top models achieved excellent performance (AUROC>0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19-like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values. CONCLUSIONS: We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.

The long-term consequences of Corona Virus Disease 2019 patients receiving Chinese herbal medicine treatments in acute phase: A protocol for systematic review and meta-analysis.

BACKGROUND: In December 2019, the first case of Corona Virus Disease 2019 (COVID-19) associated with severe acute respiratory syndrome coronavirus-2 viral infection was described in Wuhan. Similar to SARS in 2003, COVID-19 also had a lasting impact. Approximately 76% of patients discharged after hospitalization for COVID-19 had neurological manifestations which could persist for 6 months, and some long-term consequences such as the gradual loss of lung function due to pulmonary interstitial fibrosis could have comprehensive effects on daily quality of life for people who were initially believed to have recovered from COVID-19. METHODS AND ANALYSIS: Our comprehensive search strategy developed in consultation with a research librarian. We will search these following electronic databases: PubMed, Cochrane Library, Web of Science, ScienceDirect, Scopus, Google Scholar, Embase, ProQuest, China Science and Technology Journal Database (VIP), China National Knowledge Infrastructure, WANFANG DATA, WHO covid-19 website, and Centers for Disease Control and the Prevention COVID-19 websites of the United States and China. The bias of publication will be confirmed via the P value of Egger test. The quality of studies will be evaluated by the Newcastle-Ottawa Scale. ETHICS AND DISSEMINATION: There are no ethical considerations associated with this study protocol for this systematic review which mainly focuses on the examination of secondary data. On completion of this analysis, we will prepare a manuscript for publication in a peer-reviewed medical journal. PROSPERO REGISTRATION NUMBER: CRD42021258711.

Health management via telemedicine: Learning from the COVID-19 experience.

At the onset of the COVID-19 (coronavirus disease 2019) pandemic, telemedicine was rapidly implemented to protect patients and healthcare providers from infection. It is unlikely that care delivery will fully return to the pre-COVID form. Telemedicine offers many opportunities to improve care efficiency, accessibility, and patient outcomes, but many challenges exist related to technology interoperability, the digital divide, and usability. We propose that telemedicine evolve to support continuity of care throughout the patient journey, including multidisciplinary care teams and the seamless integration of data into the clinical workflow to support a learning healthcare system. Importantly, evidence is needed to support this paradigm shift in care delivery to ensure the quality and efficacy of care delivered via telemedicine. Here, we highlight gaps and opportunities that need to be addressed by the biomedical informatics community to move forward with safe and effective healthcare delivery via telemedicine.

Learning from past respiratory failure patients to triage COVID-19 patient ventilator needs: A multi-institutional study.

BACKGROUND: Unlike well-established diseases that base clinical care on randomized trials, past experiences, and training, prognosis in COVID19 relies on a weaker foundation. Knowledge from other respiratory failure diseases may inform clinical decisions in this novel disease. The objective was to predict 48-hour invasive mechanical ventilation (IMV) within 48 h in patients hospitalized with COVID-19 using COVID-like diseases (CLD). METHODS: This retrospective multicenter study trained machine learning (ML) models on patients hospitalized with CLD to predict IMV within 48 h in COVID-19 patients. CLD patients were identified using diagnosis codes for bacterial pneumonia, viral pneumonia, influenza, unspecified pneumonia and acute respiratory distress syndrome (ARDS), 2008-2019. A total of 16 cohorts were constructed, including any combinations of the four diseases plus an exploratory ARDS cohort, to determine the most appropriate cohort to use. Candidate predictors included demographic and clinical parameters that were previously associated with poor COVID-19 outcomes. Model development included the implementation of logistic regression and three ensemble tree-based algorithms: decision tree, AdaBoost, and XGBoost. Models were validated in hospitalized COVID-19 patients at two healthcare systems, March 2020-July 2020. ML models were trained on CLD patients at Stanford Hospital Alliance (SHA). Models were validated on hospitalized COVID-19 patients at both SHA and Intermountain Healthcare. RESULTS: CLD training data were obtained from SHA (n = 14,030), and validation data included 444 adult COVID-19 hospitalized patients from SHA (n = 185) and Intermountain (n = 259). XGBoost was the top-performing ML model, and among the 16 CLD training cohorts, the best model achieved an area under curve (AUC) of 0.883 in the validation set. In COVID-19 patients, the prediction models exhibited moderate discrimination performance, with the best models achieving an AUC of 0.77 at SHA and 0.65 at Intermountain. The model trained on all pneumonia and influenza cohorts had the best overall performance (SHA: positive predictive value (PPV) 0.29, negative predictive value (NPV) 0.97, positive likelihood ratio (PLR) 10.7; Intermountain: PPV, 0.23, NPV 0.97, PLR 10.3). We identified important factors associated with IMV that are not traditionally considered for respiratory diseases. CONCLUSIONS: The performance of prediction models derived from CLD for 48-hour IMV in patients hospitalized with COVID-19 demonstrate high specificity and can be used as a triage tool at point of care. Novel predictors of IMV identified in COVID-19 are often overlooked in clinical practice. Lessons learned from our approach may assist other research institutes seeking to build artificial intelligence technologies for novel or rare diseases with limited data for training and validation.

Identification and characterization of neutrophil heterogeneity in sepsis.

BACKGROUND: Although the immune function of neutrophils in sepsis has been well described, the heterogeneity of neutrophils remains unclear during the process of sepsis. METHODS: In this study, we used a mouse CLP model to simulate the clinical scenario of patients with sepsis, neutrophil infiltration, abnormal distribution and dysfunction was analyzed. LPS was used to stimulate neutrophils in vitro to simulate sepsis; single-cell gene sequencing technology was used to explore the immunological typing. To explore the immunological function of immunosuppressive neutrophils, PD-L1 knockout neutrophils were cocultured with lymphocytes from wild-type mice. RESULTS: We found that neutrophils presented variant dysfunction at the late stage of sepsis, including inhibition of apoptosis, seriously damaged chemotaxis and extensive infiltration into the tissues. Single-cell RNA sequencing revealed that multiple subclusters of neutrophils were differentiated after LPS stimulation. The two-dimensional spatial distribution analysis showed that Foxp3+ T cells were much closer to Ly-6G than the CD4+ and CD8+ cells, indicating that infiltrated neutrophils may play immunomodulatory effect on surrounding T-regs. Further observations showed that LPS mediates PD-L1 over expression through p38α-MSK1/-MK2 pathway in neutrophils. The subsets of highly expressed PD-L1 exert immunosuppressive effect under direct contact mode, including inhibition of T cell activation and induction of T cell apoptosis and trans-differentiation. CONCLUSIONS: Taken together, our data identify a previously unknown immunosuppressive subset of neutrophils as inhibitory neutrophil in order to more accurately describe the phenotype and characteristics of these cells in sepsis.

Performance of Two Risk-Stratification Models in Hospitalized Patients With Coronavirus Disease.

Background: Despite an increase in the familiarity of the medical community with the epidemiological and clinical characteristics of coronavirus disease 2019 (COVID-19), there is presently a lack of rapid and effective risk stratification indicators to predict the poor clinical outcomes of COVID-19 especially in severe patients. Methods: In this retrospective single-center study, we included 117 cases confirmed with COVID-19. The clinical, laboratory, and imaging features were collected and analyzed during admission. The Multi-lobular infiltration, hypo-Lymphocytosis, Bacterial coinfection, Smoking history, hyper-Tension and Age (MuLBSTA) Score and Confusion, Urea, Respiratory rate, Blood pressure, Age 65 (CURB65) score were used to assess the death and intensive care unit (ICU) risks in all patients. Results: Among of all 117 hospitalized patients, 21 (17.9%) patients were admitted to the ICU care, and 5 (4.3%) patients were died. The median hospital stay was 12 (10-15) days. There were 18 patients with MuLBSTA score ≥ 12 points and were all of severe type. In severe type, ICU care and death patients, the proportion with MuLBSTA ≥ 12 points were greater than that of CURB65 score ≥ 3 points (severe type patients, 50 vs. 27.8%; ICU care, 61.9 vs. 19.0%; death, 100 vs. 40%). For the MuLBSTA score, the ROC curve showed good efficiency of diagnosis death (area under the curve [AUC], 0.956; cutoff value, 12; specificity, 89.5%; sensitivity, 100%) and ICU care (AUC, 0.875; cutoff value, 11; specificity, 91.7%; sensitivity, 71.4%). The K-M survival analysis showed that patients with MuLBSTA score ≥ 12 had higher risk of ICU (log-rank, P = 0.001) and high risk of death (log-rank, P = 0.000). Conclusions: The MuLBSTA score is valuable for risk stratification and could effectively screen high-risk patients at admission. The higher score at admission have higher risk of ICU care and death in patients infected with COVID.