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1.
Ieee Transactions on Computational Social Systems ; 10(3):1105-1114, 2023.
Article in English | Web of Science | ID: covidwho-20235399

ABSTRACT

In the context of the present global health crisis, we examine the design and valuation of a pandemic emergency financing facility (PEFF) akin to a catastrophe (CAT) bond. While a CAT bond typically enables fund generation to the insurers and re-insurers after a disaster happens, a PEFF or pandemic bond's payout is linked to random thresholds that keep evolving as the pandemic continues to unfold. The subtle difference in the timing and structure of the funding payout between the usual CAT bond and PEFF complicates the valuation of the latter. We address this complication, and our analysis identifies certain aspects in the PEFF's design that must be simplified and strengthened so that this financial instrument is able to serve the intent of its original creation. An extension of the compartmentalized deterministic epidemic model-which describes the random number of people in three classes: susceptible (S), infected (I), and removed (R) or SIR for short-to its stochastic analog is put forward. At time t, S(t), I(t), and R (t) satisfy a system of interacting stochastic differential equations in our extended framework. The payout is triggered when the number of infected people exceeds a predetermined threshold. A CAT-bond pricing setup is developed with the Vasicek-based financial risk factor correlated with the SIR dynamics for the PEFF valuation. The probability of a pandemic occurrence during the bond's term to maturity is calculated via a Poisson process. Our sensitivity analyses reveal that the SIR's disease transmission and recovery rates, as well as the interest rate's mean-reverting level, have a substantial effect on the bond price. Our proposed synthesized model was tested and validated using a Canadian COVID-19 dataset during the early development of the pandemic. We illustrate that the PEFF's payout could occur as early as seven weeks after the official declaration of the pandemic, and the deficiencies of the most recent PEFF sold by an international financial institution could be readily rectified.

2.
International Journal of Engineering Business Management ; 15, 2023.
Article in English | Web of Science | ID: covidwho-2323009

ABSTRACT

Flight demand forecasting is a particularly critical component for airline revenue management because of the direct influence on the booking limits that determine airline profits. The traditional flight demand forecasting models generally only take day of the week (DOW) and the current data collection point (DCP) adds up bookings as the model input and uses linear regression, exponential smoothing, pick-up as well as other models to predict the final bookings of flights. These models can be regarded as time series flight demand forecasting models based on the interval between the current date and departure date. They fail to consider the early bookings change features in the specific flight pre-sale period, and have weak generalization ability, at last, they will lead to poor adaptability to the random changes of flight bookings. The support vector regression (SVR) model, which is derived from machine learning, has strong adaptability to nonlinear random changes of data and can adaptively learn the random disturbances of flight bookings. In this paper, flight bookings are automatically divided into peak, medium, and off (PMO) according to the season attribute. The SVR model is trained by using the vector composed of historical flight bookings and adding up bookings of DCP in the early stage of the flight pre-sale period. Compared with the traditional models, the priori information of flight is increased. We collect 2 years of domestic route bookings data of an airline in China before COVID-19 as the training and testing datasets, and divide these data into three categories: tourism, business, and general, the numerical results show that the SVR model significantly improves the forecasting accuracy and reduces RMSE compared with the traditional models. Therefore, this study provides a better choice for flight demand forecasting.

3.
4th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022 ; : 50-53, 2022.
Article in English | Scopus | ID: covidwho-2327126

ABSTRACT

In recent years, the novel corona virus pandemic is raging around the world, and the safety of home environment and public environment has become the focus of people's attention [2]. Therefore, the research on disinfection robot has become one of the important directions in the field of machinery and artificial intelligence. This paper proposes a robot with the STM32 MCU as the core of disinfection, and is equipped with a variety of sensors and a camera vision, has the original cloud service management platform, the remote deployment of navigation, based on visual SLAM to realize high precision navigation and positioning, can realize to indoor environment autonomously route planning, automatic obstacle avoidance checking, disinfection, epidemic prevention function, at the same time can pass Bit computer software realizes remote control of robot, which has great development potential. © 2022 ACM.

4.
15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2213167

ABSTRACT

In the face of the serious aging of the global population and the sudden outbreak of COVID-19, monitoring human vital signs such as heart rate is very important to save lives. For more accurate heartbeat detection, we propose a heartbeat detection scheme based on variational mode decomposition (VMD) and multiple technologies of noise and interference suppression. First, a filter is designed to suppress the impulse noise and reduce the loss of useful signal information. Then, VMD is performed to decompose the pre-processed vital signs into a series of intrinsic mode function (IMF) components. Thirdly, much attention is paid on denoising of IMF components corresponding to the heartbeat signals, an improved wavelet threshold denoising method is proposed to process these IMF components and reconstruct the heartbeat signal. Finally, an adaptive notch filter is used to process the residual respiratory harmonics in the reconstructed heartbeat signal. To verify the heartbeat detection accuracy of our method, the results are compared with a reliable reference sensor. Our results show that the mean average absolute error (AAE) of heart rate estimated by the proposed method is 1.06 bpm, which is 7.51 bpm better than the original method. © 2022 IEEE.

5.
Zhonghua Yu Fang Yi Xue Za Zhi ; 56(12): 1795-1802, 2022 Dec 06.
Article in Chinese | MEDLINE | ID: covidwho-2201072

ABSTRACT

Objective: To trace and characterize the whole genome of SARS-CoV-2 of confirmed cases in the outbreak of COVID-19 on July 31, 2021 in Henan Province. Method: Genome-wide sequencing and comparative analysis were performed on positive nucleic acid samples of SARS-CoV-2 from 167 local cases related to the epidemic on July 31, 2021, to analyze the consistency and evolution of the whole genome sequence of virus. Results: Through high-throughput sequencing, a total of 106 cases of SARS-CoV-2 whole genome sequences were obtained. The results of genome analysis showed that the whole genome sequences of 106 cases belonged to the VOC/Delta variant strain (B.1.617.2 clade), and the whole genome sequences of 106 cases were shared with the genomes of 3 imported cases from Myanmar admitted to a hospital in Zhengzhou. On the basis of 45 nucleotide sites, 1-5 nucleotide variation sites were added, and the genome sequence was highly homologous. Conclusion: Combined with the comprehensive analysis of viral genomics, transmission path simulation experiments and epidemiology, it is determined that the local new epidemic in Henan Province is caused by imported cases in the nosocomial area, and the spillover has caused localized infection in the community. At the same time, it spills over to some provincial cities and results in localized clustered epidemics.


Subject(s)
COVID-19 , Epidemics , Humans , SARS-CoV-2/genetics , Genome, Viral , Phylogeny
6.
Frontiers in Physics ; 10, 2022.
Article in English | Scopus | ID: covidwho-2022845

ABSTRACT

Non-pharmaceutical interventions (NPIs) are essential for the effective prevention and control of the COVID-19 pandemic. However, the scenarios for disease transmission are complicated and varied, and it remains unclear how real-world networks respond to the changes in NPIs. Here, we propose a multi-layer network combining structurally fixed social contact networks with a time-varying mobility network, select the COVID-19 outbreak in two metropolitans in China as case studies, and assess the effectiveness of NPIs. Human mobility, both in relatively fixed places and in urban commuting, is considered. Enclosed places are simulated by three different types of social contact networks, while urban commuting is represented by a time-varying commute network. We provide a composite framework that captures the heterogeneity and time variation of the real world and enables us to simulate large populations with low computational costs. We give out a thorough evaluation of the effectiveness of NPIs (i.e., work from home, school closure, close-off management, public transit limitation, quarantine, and mask use) under certain vaccine coverage varying with implementation timing and intensity. Our results highlight the strong correlation between the NPI pattern and the epidemic mitigation effect and suggest important operational strategies for epidemic control. Copyright © 2022 Chen, Guo, Jiao, Liang, Li, Yan, Huang, Liu and Fan.

7.
Journal of General Internal Medicine ; 37:S294, 2022.
Article in English | EMBASE | ID: covidwho-1995705

ABSTRACT

BACKGROUND: The rapid conversion to telehealth services as an alternative to in-person ambulatory care in response to COVID-19 required abrupt adaptations by patients with diabetes and their providers, that may have resulted in poorer outcomes for subgroups of the population. METHODS: We conducted a longitudinal population study from a diabetes registry with clinical and administrative data maintained for all patients with diabetes seen at an academic medical center. From this registry, we identified all patients seen at least once in the year before and after 03/20/20 at any of the 16 ambulatory care clinics at this site (n=9760) who also had ≧ 1 HbA1c value in both periods (n=4710), and those with ≧ 2 visits and ≧ 2 HbA1c values in the same periods (n=1553). We compared patient characteristics (age, gender, race/ethnicity, Charlson comorbidity score), clinic site [Federally Qualified Health Centers (FQHCs) vs. other ambulatory care sites], total number of ambulatory visits and number of telehealth visits, mean HbA1c mean value and % in poor control (HbA1c ≧ 9%) for both groups of patients. We used odds ratios for bivariate comparisons and logistic regression for multivariable analyses. RESULTS: The mean age of patients with ≧ 1 visit in the pre-post periods was 62.5 [SD 14.0], 47% were female, 40% were Hispanic, 28% had a Charlson score greater than the median, 37% were seen at an FQHC, and 18.9% had poor glycemic control (HbA1c ≧9%). Characteristics for patients with ≧ 2 visits were comparable. Poor control was more likely among those seen at FQHC sites (OR=3.17, p<.0002), those ≧65 years (OR=3.53, p<.0001), those with substantial comorbidity (Charlson ≧ median value, OR=1.40, p=.0011), Hispanic patients (OR=3.08, p<.0001) and those seen by telehealth (OR=1.59, p<.0001). Results for patients with ≧ 2 visits and corresponding HbA1c values were comparable. Parameter estimates from the logistic regression model predicting HbA1c ≧ 9% were all statistically significant and in the expected direction for the variables considered. CONCLUSIONS: Telemedicine is currently being considered for continuation as an accepted, efficient and safe mode of healthcare delivery. However, it may not be effective for specific subgroups of patients with chronic diseases such as diabetes in which patient partnership and the provider patient relationship are key to optimizing outcomes. Further, advances in the delivery of telehealth care, including easily accessed high quality technologies are needed to ensure that remote healthcare delivery does not further increase disparities in health outcomes, particularly for the poor, underserved, minorities, elderly and those with complex diseases.

8.
IEEE Transactions on Engineering Management ; : 1-14, 2022.
Article in English | Scopus | ID: covidwho-1992678

ABSTRACT

Understanding the resilience capabilities of restaurant operations and the determinants affecting these capabilities is critical to helping restaurants overcome the hardships owing to the coronavirus disease (COVID-19) pandemic. This article adopts a textual analytics approach to scientifically measure consumption trends and identify the shock to restaurant sales using online customer review data from Dianping.com (an O2O platform in China). Moreover, the article proposes a theoretical model of business resilience for the restaurant industry in the context of the pandemic. Then, an empirical investigation on how the determinants in our theoretical framework affect the resilience of restaurant business operations using the panel logit model is conducted. Our findings indicate that the pandemic has severely disrupted the full-service restaurants as compared to the quick-service restaurants. We identify four determinants of resilience, namely social capital (i.e., restaurant rating), physical capital (i.e., contactless service), economic capital (i.e., chain operation), and natural capital (e.g., location), which are significantly associated with the resilience of restaurant business during the pandemic. These four determinants play different roles in the resilience of full-service and quick-service restaurants. The findings of this study have theoretical contribution and generate some important managerial implications for helping the restaurant industry recover from disruptions brought by the COVID-19 pandemic. IEEE

9.
Chinese Journal of Radiology (China) ; 56(4):377-384, 2022.
Article in Chinese | EMBASE | ID: covidwho-1896938

ABSTRACT

Objective To explore the application value of CT pulmonary function imaging in patients with Coronavirus Disease 2019 (COVID‑19) in the convalescent phase. Methods The COVID‑19 patients who were clinically cured and discharged from Union Hospital, Tongji Medical College, Huazhong University of Science and Technology were prospectively collected from January to April 2020. Clinical pulmonary function tests (PFTs) and CT pulmonary function imaging were performed 3 months after discharge. The Philips IntelliSpace Portal image post‑processing workstation was used to obtain the paired inspiratory‑expiratory CT quantitative indexes of the whole lung, left lung, right lung and five lobes. The patients were divided into two groups according to whether residual lesions remain in inspiratory CT images: non‑residual lesion group and residual lesion group. The chi‑square test was used to compare the differences in the PFT results between groups;the Mann‑Whitney U test was used to compare the differences in PFT indexes [forced expiratory volume in the first second as percentage of predicted value (FEV1%), FEV1/forced vital capacity (FEV1/FVC), total lung capacity as percentage of predicted value (TLC%), FVC% ] and the differences in quantitative CT indexes [lung volume (LV), mean lung density (MLD), volume change in inspiratory phase and expiratory phase (∆ LV)] between groups. Multiple linear regression was used to analyze the relationship between CT pulmonary function imaging and PFT indexes of convalescent COVID‑19 patients. Results Of the 90 patients with COVID‑19, 35 were males and 55 were females;45 were included in the non‑residual lesion group and 45 were included in the residual lesion group. Fifty‑three patients had clinical pulmonary dysfunction 3 months after discharge, including 22 patients in the non‑residual lesion group and 31 patients in the residual lesion group. In patients with residual disease, left lower lobe and right lower lobe LV, left lower lobe and right lower lobe ∆ LV in the inspiratory and expiratory phase were smaller than those without residual disease;whole lung, left lung, right lung, left upper lobe, left lower lobe and right lower lobe MLD in the inspiratory phase and left lower lobe and right lower lobe MLD in the expiratory phase were greater than those without residual disease (P<0.05). Since there was no significant difference in FEV1/FVC and FVC% between residual and non‑residual lesion groups (P>0.05), FEV1/FVC and FVC% of two groups were combined. Multiple linear regression analysis showed FEV1/FVC= 91.765-0.016×LVin‑right middle lobe+0.014×MLDex‑left lower lobe (R2 =0.200, P<0.001), FVC% =-184.122-0.358× MLDin‑right lung-0.024× ∆ LVleft upper lobe (R2 =0.261, P<0.001). There was significant difference in TLC% between residual and non‑residual lesion groups (P<0.05), so multiple linear regression analysis was performed both in the two groups. In the non‑residual lesion group, TLC% =80.645+0.031× (R2 =0.132, P<0.001);In the residual lesion group, TLC% =-110.237-0.163× LVex‑right lower lobe MLDin‑right upper lobe-0.098×MLDex‑left upper lobe -0.025×LVex‑right lower lobe (R2 =0.473, P<0.001). Conclusion CT pulmonary function imaging can quantitatively analyze the whole lung, unilateral lung and lobulated lung, thus reflecting the regional pulmonary function, providing more valuable diagnostic information for the assessment of ulmonar function in convalescent atients with COVID‑19

10.
Journal of the American College of Cardiology ; 79(9):1848-1848, 2022.
Article in English | Web of Science | ID: covidwho-1848324
11.
15th International Conference on Learning and Intelligent Optimization, LION 15 2021 ; 12931 LNCS:211-218, 2021.
Article in English | Scopus | ID: covidwho-1606012

ABSTRACT

In this paper, we discuss the medical staff scheduling problem in the Mobile Cabin Hospital (MCH) during the pandemic outbreaks. We investigate the working contents and patterns of the medical staff in the MCH of Wuhan during the outbreak of Covid-19. Two types of medical staff are considered in the paper, i.e., physicians and nurses. Besides, two different types of physicians are considered, i.e., the expert physician and general physician, and the duties vary among different types of physicians. The objective of the studied problem is to get the minimized number of medical staff required to accomplish all the duties in the MCH during the planning horizon. To solve the studied problem, a general Variable Neighborhood Search (general VNS) is proposed, involving the initialization, the correction strategy, the neighborhood structure, the shaking procedure, the local search procedure, and the move or not procedure. The mutation operation is adopted in the shaking procedure to make sure the diversity of the solution and three neighborhood structure operations are applied in the local search procedure to improve the quality of the solution. © 2021, Springer Nature Switzerland AG.

12.
IEEE Transactions on Computational Social Systems ; 2021.
Article in English | Scopus | ID: covidwho-1593200

ABSTRACT

In the context of the present global health crisis, we examine the design and valuation of a pandemic emergency financing facility (PEFF) akin to a catastrophe (CAT) bond. While a CAT bond typically enables fund generation to the insurers and re-insurers after a disaster happens, a PEFF or pandemic bond's payout is linked to random thresholds that keep evolving as the pandemic continues to unfold. The subtle difference in the timing and structure of the funding payout between the usual CAT bond and PEFF complicates the valuation of the latter. We address this complication, and our analysis identifies certain aspects in the PEFF's design that must be simplified and strengthened so that this financial instrument is able to serve the intent of its original creation. An extension of the compartmentalized deterministic epidemic model--which describes the random number of people in three classes: susceptible (S), infected (I), and removed (R) or SIR for short--to its stochastic analog is put forward. At time t, S(t), I(t), and R(t) satisfy a system of interacting stochastic differential equations in our extended framework. The payout is triggered when the number of infected people exceeds a predetermined threshold. A CAT-bond pricing setup is developed with the Vasiček-based financial risk factor correlated with the SIR dynamics for the PEFF valuation. The probability of a pandemic occurrence during the bond's term to maturity is calculated via a Poisson process. Our sensitivity analyses reveal that the SIR's disease transmission and recovery rates, as well as the interest rate's mean-reverting level, have a substantial effect on the bond price. Our proposed synthesized model was tested and validated using a Canadian COVID-19 dataset during the early development of the pandemic. We illustrate that the PEFF's payout could occur as early as seven weeks after the official declaration of the pandemic, and the deficiencies of the most recent PEFF sold by an international financial institution could be readily rectified. IEEE

13.
Kexue Tongbao/Chinese Science Bulletin ; 66(31):3925-3931, 2021.
Article in Chinese | Scopus | ID: covidwho-1523391

ABSTRACT

Left unmitigated, climate change poses a catastrophic risk to human health, demanding an urgent and concerted response from every country. The 2015 Lancet Commission on Health and Climate Change and The Lancet Countdown: Tracking Progress on Health and Climate Change have been initiated to map out the impacts of climate change and the necessary policy responses. To meet these challenges, Tsinghua University, partnering with the University College London and 17 Chinese and international institutions, has prepared the Chinese Lancet Countdown report, which has a national focus and builds on the work of the global Lancet Countdown: Tracking Progress on Health and Climate Change. Drawing on international methodologies and frameworks, this report aims to deepen the understanding of the links between public health and climate change at the national level and track them with 23 indicators. This work is part of the Lancet's Countdown broader efforts to develop regional expertise on this topic, and coincides with the launch of the Lancet Countdown Regional Centre in Asia, based at Tsinghua University. The data and results of this report are presented at the provincial level, where possible, to facilitate targeted response strategies for local decision-makers. Based on the data and findings of the 2020 Chinese Lancet Countdown report, five recommendations are proposed to key stakeholders in health and climate change in China: (1) Enhance inter-departmental cooperation. Climate change is a challenge that demands an integrated response from all sectors, urgently requiring substantial inter-departmental cooperation among health, environment, energy, economic, financial, and education authorities. (2) Strengthen health emergency preparedness. Knowledge and findings on current and future climate-related health threats still lack the required attention and should be fully integrated into the emergency preparedness and response system. (3) Support research and raise awareness. Additional financial support should be allocated to health and climate change research in China to enhance health system adaptation, mitigation measures, and their health benefits. At the same time, media and academia should be fully motivated to raise the public and politicians' awareness of this topic. (4) Increase climate change mitigation. Speeding up the phasing out of coal is necessary to be consistent with China's pledge to be carbon neutral by 2060 and to continue to reduce air pollution. Fossil fuel subsidies must also be phased out. (5) Ensure the recovery from COVID-19 to protect health now and in the future. China's efforts to recover from COVID-19 will shape public health for years to come. Climate change should be a priority in these interventions. © 2021, Science Press. All right reserved.

14.
Acs Es&T Water ; 1(10):2174-2185, 2021.
Article in English | Web of Science | ID: covidwho-1486380

ABSTRACT

A novel coronavirus (SARS-CoV-2) causing corona virus disease 2019 (COVID-19) has attracted global attention due to its highly infectious and pathogenic properties. Most of current studies focus on aerosols released from infected individuals, but the presence of SARS-CoV-2 in wastewater also should be examined. In this review, we used bibliometrics to statistically evaluate the importance of water-related issues in the context of COVID-19. The results show that the levels and transmission possibilities of SARS-CoV-2 in wastewater are the main concerns, followed by potential secondary pollution by the intensive use of disinfectants, sludge disposal, and the personal safety of workers. The presence of SARS-CoV-2 in wastewater requires more attention during the COVID-19 pandemic. Thus, the most effective techniques, i.e., wastewater-based epidemiology and quantitative microbial risk assessment, for virus surveillance in wastewater are systematically analyzed. We further explicitly review and analyze the successful operation of a sewage treatment plant in Huoshenshan Hospital in China as an example and reference for other sewage treatment systems to properly ensure discharge safety and tackle the COVID-19 pandemic. This review offers deeper insight into the prevention and control of SARS-CoV-2 and similar viruses in the post-COVID-19 era from a wastewater perspective.

15.
IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) ; 2020.
Article in English | Web of Science | ID: covidwho-1485912

ABSTRACT

The topological distance is to measure the structural difference between two graphs in a metric space. Graphs are ubiquitous, and topological measurements over graphs arise in diverse areas, including, e.g. COVID-19 structural analysis, DNA/RNA alignment, discovering the Isomers, checking the code plagiarism. Unfortunately, popular distance scores used in these applications, that scale over large graphs, are not metrics, and the computation usually becomes NP-hard. While, fuzzy measurement is an uncertain representation to apply for a polynomial-time solution for undirected multigraph isomorphism. But the graph isomorphism problem is to determine two finite graphs that are isomorphic, which is not known with a polynomial-time solution. This paper solves the undirected multigraph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial-time solution to check if two undirected multigraphs are isomorphic or not. Based on the solution, we define a new fuzzy measurement based on graph isomorphism for topological distance/structural similarity between two graphs. Thus, this paper proposed a fuzzy measure of the topological distance between two undirected multigraphs. If two graphs are isomorphic, the topological distance is 0;if not, we will calculate the Euclidean distance among eight extracted features and provide the fuzzy distance. The fuzzy measurement executes more efficiently and accurately than the current methods.

16.
Covid-19 and Governance: Crisis Reveals ; : 15-28, 2021.
Article in English | Scopus | ID: covidwho-1372302
17.
Medical Journal of Wuhan University ; 42(4):589-593, 2021.
Article in Chinese | Scopus | ID: covidwho-1299710

ABSTRACT

Objective: To investigate the initial chest CT imaging features and clinical types of patients with COVID-19. Methods: A total 165 patients with COVID-19 were retrospectively analyzed according to different age groups and different clinical classifications. Results: There were statistically significant differences in involvement sites, involvement range, lesion distribution and largest diameter of the lesions in COVID-19 patients among different age groups. Logistic regression analysis of age between normal type group and severe type group showed statistical significance (P<0.05). The maximum sensitivity and specificity were 64.20% and 69.00%. The corresponding age threshold was 50.5 years old. Conclusion: The main manifestations of initial chest CT in COVID-19 patients were ground-glass opacities, most often involving the posterior basal segment of the lower lung. Single site involvement was more common in 17-35 years patients. Most of the lesions were distributed around the subpleural and bronchovascular bundle in 36-90 years patients, involving both lungs and reaching more than two pulmonary lobes. For the COVID-19 patients with diabetes, hypertension, or older than 50.5 years, early diagnosis, isolation and treatment are necessary, and the changes of their conditions should be closely mornitored. © 2021, Editorial Board of Medical Journal of Wuhan University. All right reserved.

18.
Iranian Journal of Radiology ; 18(1):1-7, 2021.
Article in English | EMBASE | ID: covidwho-1215665

ABSTRACT

Background: As the coronavirus disease 2019 (COVID-19) epidemic continues to spread, it is important to predict the clinical classification of COVID-19 and evaluate the progression of lung injury. Objectives: To investigate the predictive factors of the outcome of moderate-stage coronavirus disease 2019 (COVID-19) and maximal extent of lung injury. Patients and Methods: This study was a retrospective analysis of 97 patients with moderate-stage COVID-19 diagnosed in our hos-pital. We divided the patients into two groups according to disease progression: one group for moderate stage and another for both severe stage and critically severe stage COVID-19. We then analyzed the independent factors influencing changes in the course of the disease in moderate-stage patients using binary logistic regression. Next, we assessed the computed tomography (CT) score of maximal lung injury using follow-up images of the patients. We used multiple linear regression (MLR) to analyze the independent variables, and to predict the CT score of maximal lung injury in COVID-19 patients. Results: The results were obtained using multivariate logistic regression analysis, and the independent factors affecting clinical classification were baseline CT score (P = 0.008), high-sensitivity C-reactive protein (hs-CRP) (P = 0.001), and diabetes (P = 0.04). MLR revealed that the factors predicting the extent of maximal lung injury in COVID-19 patients were age (P = 0.014), neutrophil percentage (P = 0.038), lymphocyte percentage (P = 0.031), hs-CRP (P = 0.010), and baseline CT score (P < 0.001). The optimal cut-off value of hs-CRP was 18.5, and the baseline CT score was 8.5. Conclusion: Age, baseline CT score, hs-CRP, neutrophil percentage, and lymphocyte percentage could predict the CT score of maximal lung injury, and hs-CRP > 18.5, baseline CT score ≥ 9, and diabetes were independent factors of severe/critically severe COVID-19.

19.
Iranian Journal of Radiology ; 18(1):1-7, 2021.
Article in English | Scopus | ID: covidwho-1154762
20.
Basic & Clinical Pharmacology & Toxicology ; 128:235-235, 2021.
Article in English | Web of Science | ID: covidwho-1113028
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