A class of delay SIQR-V models considering quarantine and vaccination: Validation based on the COVID-19 perspective.

To contain the novel SARS-CoV-2 (COVID-19) spreading worldwide, governments generally adopt two measures: quarantining the infected people and vaccinating the susceptible people. To investigate the disease latency's influence on the transmission characteristics of the system, we establish a new SIQR-V (susceptible-infective-quarantined-recovered-vaccinated) dynamic model that focus on the effectiveness of quarantine and vaccination measures in the scale-free network. We use theoretical analysis and numerical simulation to explore the evolution trend of different nodes and factors influencing the system stability. The study shows that both the complexity of the network and latency delay can affect the evolution trend of the infected nodes in the system. Still, only latency delay can destroy the stability of the system. In addition, through the parameter sensitivity analysis of the basic reproduction number, we find that the effect of the vaccination parameter α on the basic reproduction number R 0 is more significant than that of transmission rate ß and quarantine parameter σ . It shows that vaccination is one of the most effective public policies to prevent infectious diseases' spread. Finally, we calculate the basic reproduction numbers that are greater than one for Germany and Pakistan under COVID-19 and validate the model's effectiveness based on the disease data of COVID-19 in Germany. The results show that the changing trend of the infected population in Germany based on the SIQR-V model is roughly the same as that reflected by the actual epidemic data in Germany. Therefore, providing suggestions and guidance for treating infectious diseases based on this model can effectively reduce the harm caused by the outbreak of contagious diseases.

Follow-up study of pulmonary sequelae in discharged COVID-19 patients with diabetes or secondary hyperglycemia.

PURPOSE: To determine chest CT changes 6 months and 12 months after the onset of coronavirus disease 2019 (COVID-19) in patients with diabetes or hyperglycemia and the risk factors for these residual lung abnormalities. METHODS: In total, 141 COVID-19 patients were assigned to group 1 (diabetes), group 2 (secondary hyperglycemia) or group 3 (controls). Initial and six- and twelve-month follow-up computed tomography (CT) scans were performed 16 days, 175 days and 351 days after symptom onset, respectively. CT findings and clinical and peak laboratory parameters were collected and compared. Univariable and multivariable logistic regression analyses were performed to identify the independent predictors for the presence of residual lung abnormalities at the 6-month follow-up exam. Seven variables (age; the presence of acute respiratory distress syndrome; the duration of hospitalization; the peak levels of lactate dehydrogenase (LDH) and C-reactive protein; and the initial total CT score) were chosen in the final multivariable models. RESULTS: At the six-month follow-up, abnormalities were still observed on chest CT in 77/141 (54.6%) patients. Reticular patterns (40/141, 28.4%) and ground-glass opacities (GGOs) (29/141, 20.6%) were the most common CT abnormalities on the follow-up CT scans. Patients in Groups 1 and 2 had significantly higher incidences of residual lung abnormalities than those in Group 3 (65.4% and 58.3%, respectively vs. 36.6%; p < 0.05). Twelve months after disease onset, the chest CT changes persisted in 13/25 (52.0%) patients. A duration of hospitalization > 20 days (OR: 5.630, 95% CI: 1.394-22.744, p = 0.015), an LDH level ≥ 317 U/L (OR: 7.020, 95% CI: 1.032-47.743, p = 0.046) and a total CT score > 15 (OR: 9.919, 95% CI: 1.378-71.415, p = 0.023) were independent predictors of residual pulmonary abnormalities in patients with diabetes or secondary hyperglycemia. CONCLUSIONS: A considerable proportion of surviving COVID-19 patients with diabetes or secondary hyperglycemia had residual pulmonary abnormalities six months after disease onset, and we found evidence of persistent chest CT changes at the one-year follow-up. Residual lung abnormalities were associated with longer hospital stays, higher peak LDH levels and higher initial total CT scores.

Effect of a Previous History of Antiretroviral Treatment on Clinical Picture of Patients with Co-infection of SARS-CoV-2 and HIV: A Preliminary Study.

OBJECTIVES: We aimed to explore the effect of antiretroviral treatment (ART) history on clinical characteristics of patients with co-infection of SARS-CoV-2 and HIV. METHODS: We retrospectively reviewed 20 patients with laboratory-confirmed co-infection of SARS-CoV-2 and HIV in a designated hospital. Patients were divided into medicine group (n = 12) and non-medicine group (n = 8) according to previous ART history before SARS-CoV-2 infection. RESULTS: The median age was 46.5 years and 15 (75%) were female. Ten patients had initial negative RT-PCR on admission, 5 of which had normal CT appearance and 4 were asymptomatic. Lymphocytes were low in 9 patients (45%), CD4 cell count and CD4/CD8 were low in all patients. The predominant CT features in 19 patients were multiple (42%) ground-glass opacities (58%) and consolidations (32%). Erythrocyte sedimentation rate (ESR) in the medicine group was significantly lower than that in the non-medicine group [median (interquartile range, IQR):14.0 (10.0-34.0) vs. 51.0 (35.8-62.0), P = 0.005]. Nineteen patients (95%) were discharged with a median hospital stay of 30 days (IQR, 26-30). CONCLUSIONS: Most patients with SARS-CoV-2 and HIV co-infection exhibited mild to moderate symptoms. The milder extent of inflammatory response to SARS-CoV-2 infection might be associated with a previous history of ART in HIV-infected patients.

Baseline characteristics and risk factors for short-term outcomes in 132 COVID-19 patients with diabetes in Wuhan China: A retrospective study.

AIMS: To investigate the clinical characteristics, laboratory findings and high- resolution CT (HRCT) features and to explore the risk factors for in-hospital death and complications of coronavirus disease 2019 (COVID-19) patients with diabetes. METHODS: From Dec 31, 2019, to Apr 5, 2020, a total of 132 laboratory-confirmed COVID-19 patients with diabetes from two hospitals were retrospectively included in our study. Clinical, laboratory and chest CT data were analyzed and compared between the two groups with an admission glucose level of ≤11 mmol/L (group 1) and >11 mmol/L (group 2). Logistic regression analyses were used to identify the risk factors associated with in-hospital death and complications. RESULTS: Of 132 patients, 15 died in hospital and 113 were discharged. Patients in group 2 were more likely to require intensive care unit care (21.4% vs. 9.2%), to develop acute respiratory distress syndrome (ARDS) (23.2% vs. 9.2%) and acute cardiac injury (12.5% vs. 1.3%), and had a higher death rate (19.6% vs. 5.3%) than group 1. In the multivariable analysis, patients with admission glucose of >11 mmol/l had an increased risk of death (OR: 7.629, 95%CI: 1.391-37.984) and in-hospital complications (OR: 3.232, 95%CI: 1.393-7.498). Admission d-dimer of ≥1.5 µg/mL (OR: 6.645, 95%CI: 1.212-36.444) and HRCT score of ≥10 (OR: 7.792, 95%CI: 2.195-28.958) were associated with increased odds of in-hospital death and complications, respectively. CONCLUSIONS: In COVID-19 patients with diabetes, poorly-controlled blood glucose (>11 mmol/L) may be associated with poor outcomes. Admission hyperglycemia, elevated d-dimer and high HRCT score are potential risk factors for adverse outcomes and death.

Prognostic value of baseline clinical and HRCT findings in 101 patients with severe COVID-19 in Wuhan, China.

The aim of this study was to assess the prognostic value of baseline clinical and high resolution CT (HRCT) findings in patients with severe COVID-19. In this retrospective, two-center study, we included two groups of inpatients with severe COVID-19 who had been discharged or died in Jin Yin-tan hospital and Wuhan union hospital between January 5, 2020, and February 22, 2020. Cases were confirmed by real-time polymerase chain reaction. Demographic, clinical, and laboratory data, and HRCT imaging were collected and compared between discharged and deceased patients. Univariable and multivariable logistic regression models were used to assess predictors of mortality risk in these patients. 101 patients were included in this study, of whom 66 were discharged and 35 died in the hospital. The mean age was 56.6 ± 15.1 years and 67 (66.3%) were men. Of the 101 patients, hypertension (38, 37.6%), cardiovascular disease (21,20.8%), diabetes (18,17.8%), and chronic pulmonary disease (16,15.8%) were the most common coexisting conditions. The multivariable regression analysis showed older age (OR: 1.142, 95% CI 1.059-1.231, p < 0.001), acute respiratory distress syndrome (ARDS) (OR: 10.142, 95% CI 1.611-63.853, p = 0.014), reduced lymphocyte count (OR: 0.004, 95% CI 0.001-0.306, p = 0.013), and elevated HRCT score (OR: 1.276, 95% CI 1.002-1.625, p = 0.049) to be independent predictors of mortality risk on admission in severe COVID-19 patients. These findings may have important clinical implications for decision-making based on risk stratification of severe COVID-19 patients.