Prediction of the Normalized COVID-19 Epidemic Prevention Costs of Construction Projects Based on an Optimized Neural Network

During the COVID-19 epidemic, the Chinese central government adopted a dynamic clearing prevention and control strategy. Meanwhile, most local governments issued policies to incorporate normal epidemic prevention costs into the costs of construction projects. However, there are few provisions on how to determine the calculation standards for these costs. To accurately predict the normalized epidemic prevention costs of construction projects from different aspects, the relevant factors that affect epidemic prevention costs are investigated and an optimized neural network prediction method that can effectively eliminate abnormal data with a too large deviation is proposed. The results show that compared with the traditional backpropagation (BP) neural network and BP neural networks optimized by genetic algorithm, the error of the optimized neural network achieves a smaller error in predicting the normalized epidemic prevention costs of construction projects (the average error of the traditional BP neural networks is 6%). Meanwhile, among the factors that affect epidemic prevention costs, total investment, project category, and construction scale have the greatest impact. Based on the research results, this paper proposes pricing suggestions and corresponding management solutions for the epidemic prevention costs of construction projects, which will be helpful to project managers. [ FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Association and Interaction Between Serum Interleukin-6 Levels and Metabolic Dysfunction-Associated Fatty Liver Disease in Patients With Severe Coronavirus Disease 2019.

Background and Aim: Circulating levels of interleukin (IL)-6, a well-known inflammatory cytokine, are often elevated in coronavirus disease-2019 (COVID-19). Elevated IL-6 levels are also observed in patients with metabolic dysfunction-associated fatty liver disease (MAFLD). Our study aimed to describe the association between circulating IL-6 levels and MAFLD at hospital admission with risk of severe COVID-19. Methods: A total of 167 patients with laboratory-confirmed COVID-19 from three Chinese hospitals were enrolled. Circulating levels of IL-2, IL-4, IL-6, IL-10, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ were measured at admission. All patients were screened for fatty liver by computed tomography. Forty-six patients were diagnosed as MAFLD. Results: Patients with MAFLD (n = 46) had higher serum IL-6 levels (median 7.1 [interquartile range, 4.3-20.0] vs. 4.8 [2.6-11.6] pg/mL, p = 0.030) compared to their counterparts without MAFLD (n = 121). After adjustment for age and sex, patients with MAFLD had a ~2.6-fold higher risk of having severe COVID-19 than those without MAFLD. After adjustment for age, sex and metabolic co-morbidities, increased serum IL-6 levels remained associated with higher risk of severe COVID-19, especially among infected patients with MAFLD (adjusted-odds ratio 1.14, 95% CI 1.05-1.23; p = 0.002). There was a significant interaction effect between serum IL-6 levels and MAFLD for risk of severe COVID-19 (p for interaction = 0.008). Conclusions: Patients with MAFLD and elevated serum IL-6 levels at admission are at higher risk for severe illness from COVID-19.

CANPT Score: A Tool to Predict Severe COVID-19 on Admission.

Background and Aims: Patients with critical coronavirus disease 2019 (COVID-19) have a mortality rate higher than 50%. The purpose of this study was to establish a model for the prediction of the risk of severe disease and/or death in patients with COVID-19 on admission. Materials and Methods: Patients diagnosed with COVID-19 in four hospitals in China from January 22, 2020 to April 15, 2020 were retrospectively enrolled. The demographic, laboratory, and clinical data of the patients with COVID-19 were collected. The independent risk factors related to the severity of and death due to COVID-19 were identified with a multivariate logistic regression; a nomogram and prediction model were established. The area under the receiver operating characteristic curve (AUROC) and predictive accuracy were used to evaluate the model's effectiveness. Results: In total, 582 patients with COVID-19, including 116 patients with severe disease, were enrolled. Their comorbidities, body temperature, neutrophil-to-lymphocyte ratio (NLR), platelet (PLT) count, and levels of total bilirubin (Tbil), creatinine (Cr), creatine kinase (CK), and albumin (Alb) were independent risk factors for severe disease. A nomogram was generated based on these eight variables with a predictive accuracy of 85.9% and an AUROC of 0.858 (95% CI, 0.823-0.893). Based on the nomogram, the CANPT score was established with cut-off values of 12 and 16. The percentages of patients with severe disease in the groups with CANPT scores <12, ≥12, and <16, and ≥16 were 4.15, 27.43, and 69.64%, respectively. Seventeen patients died. NLR, Cr, CK, and Alb were independent risk factors for mortality, and the CAN score was established to predict mortality. With a cut-off value of 15, the predictive accuracy was 97.4%, and the AUROC was 0.903 (95% CI 0.832, 0.974). Conclusions: The CANPT and CAN scores can predict the risk of severe disease and mortality in COVID-19 patients on admission.

Serum glucose, lactate dehydrogenase and hypertension are mediators of the effect of body mass index on severity of COVID-19.

Background: COVID-19 has a broad clinical spectrum. We investigated the role of serum markers measured on admission on severity as assessed at discharge and investigated those which relate to the effect of BMI on severity. Methods: Clinical and laboratory data from 610 COVID-19 cases hospitalized in the province of Zheijang, China were investigated as risk factors for severe COVID-19 (assessed by respiratory distress) compared to mild or common forms using logistic regression methods. Biochemical markers were correlated with severity using spearman correlations, and a ROC analysis was used to determine the individual contribution of each of the biochemical markers on severity. We carried out formal mediation analyses to investigate the extent of the effect of body mass index (BMI) on COVID-19 severity mediated by hypertension, glycemia, Lactose Dehydrogenase (LDH) at the time of hospitalization and C-Reactive Protein levels (CRP), in units of standard deviations. Results: The individual markers measured on admission contributing most strongly to prediction of COVID-19 severity as assessed at discharge were LDH, CRP and glucose. The proportion of the effect of BMI on severity of COVID-19 mediated by CRP, glycemia or hypertension, we find that glucose mediated 79% (p < .0001), LDH mediated 78% (p < .0001), hypertension mediated 66% (p < .0001); however, only 44% (p < .005) was mediated by systemic inflammation (CRP). Conclusion: Our data indicate that a larger proportion of the effect of BMI on severity of COVID-19 is mediated by glycemia and LDH levels whereas less than half of it is mediated by systemic inflammation.

Effects of Environmental Factors on Severity and Mortality of COVID-19.

Background: Most respiratory viruses show pronounced seasonality, but for SARS-CoV-2, this still needs to be documented. Methods: We examined the disease progression of COVID-19 in 6,914 patients admitted to hospitals in Europe and China. In addition, we evaluated progress of disease symptoms in 37,187 individuals reporting symptoms into the COVID Symptom Study application. Findings: Meta-analysis of the mortality risk in seven European hospitals estimated odds ratios per 1-day increase in the admission date to be 0.981 (0.973-0.988, p < 0.001) and per increase in ambient temperature of 1°C to be 0.854 (0.773-0.944, p = 0.007). Statistically significant decreases of comparable magnitude in median hospital stay, probability of transfer to the intensive care unit, and need for mechanical ventilation were also observed in most, but not all hospitals. The analysis of individually reported symptoms of 37,187 individuals in the UK also showed the decrease in symptom duration and disease severity with time. Interpretation: Severity of COVID-19 in Europe decreased significantly between March and May and the seasonality of COVID-19 is the most likely explanation.

Role of Drugs Used for Chronic Disease Management on Susceptibility and Severity of COVID-19: A Large Case-Control Study.

This study aimed to investigate whether specific medications used in the treatment chronic diseases affected either the development and/ or severity of coronavirus disease 2019 (COVID-19) in a cohort of 610 COVID-19 cases and 48,667 population-based controls from Zhejiang, China. Using a cohort of 578 COVID-19 cases and 48,667 population-based controls from Zhejiang, China, we tested the role of usage of cardiovascular, antidiabetic, and other medications on risk and severity of COVID-19. Analyses were adjusted for age, sex, and body mass index and for presence of relevant comorbidities. Individuals with hypertension taking calcium channel blockers had significantly increased risk (odds ratio (OR) = 1.73, 95% confidence interval (CI) 1.2-2.3) of manifesting symptoms of COVID-19, whereas those taking angiotensin receptor blockers and diuretics had significantly lower disease risk (OR = 0.22, 95% CI 0.15-0.30 and OR = 0.30, 95% CI 0.19-0.58, respectively). Among those with type 2 diabetes, dipeptidyl peptidase-4 inhibitors (OR = 6.02, 95% CI 2.3-15.5) and insulin (OR = 2.71, 95% CI 1.6-5.5) were more and glucosidase inhibitors were less prevalent (OR = 0.11, 95% CI 0.1-0.3) among with patients with COVID-19. Drugs used in the treatment of hypertension and diabetes influence the risk of development of COVID-19, but, not its severity.

Fatal outcome in a liver transplant recipient with COVID-19.

Liver injury is common in patients with COVID-19, but little is known about its clinical presentation and severity in the context of liver transplant. We describe a case of COVID-19 in a patient who underwent transplant 3 years ago for hepatocellular carcinoma. The patient came to clinic with symptoms of respiratory disease; pharyngeal swabs for severe acute respiratory syndrome coronavirus 2 were positive. His disease progressed rapidly from mild to critical illness and was complicated by several nosocomial infections and multiorgan failure. Despite multiple invasive procedures and rescue therapies, he died from the disease. The management of COVID-19 in the posttransplant setting presents complex challenges, emphasizing the importance of strict prevention strategies.