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1.
Annals of GIS ; : 1-14, 2022.
Article in English | Academic Search Complete | ID: covidwho-1730537

ABSTRACT

Without a widely distributed vaccine, controlling human mobility has been identified and promoted as the primary strategy to mitigate the transmission of COVID-19. Many studies have reported the relationship between human mobility and COVID-19 transmission by utilizing the spatial-temporal information of mobility data from various sources. To better understand the role of human mobility in the pandemic, we conducted a systematic review of articles that measure the relationship between human mobility and COVID-19 in terms of their data sources, mathematical models, and key findings. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we selected 47 articles from the Web of Science Core Collection up to September 2020. Restricting human mobility reduced the transmission of COVID-19, although the effectiveness and stringency of policy implementation vary temporally and spatially across different stages of the pandemic. We call for prompt and sustainable measures to control the pandemic. We also recommend researchers 1) to enhance multi-disciplinary collaboration;2) to adjust the implementation and stringency of mobility-control policies in corresponding to the rapid change of the pandemic;3) to improve mathematical models used in analysing, simulating, and predicting the transmission of the disease;and 4) to enrich the source of mobility data to ensure data accuracy and suability. [ FROM AUTHOR] Copyright of Annals of GIS is the property of Taylor & Francis Ltd 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.)

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-323729

ABSTRACT

Objective: The aim of this study was to identify early warning signs for severe coronavirus disease 2019 (COVID-19). Methods We retrospectively analysed the clinical data of 90 patients with COVID-19 from Guanggu District of Hubei Women and Children Medical and Healthcare Center, comprising 60 mild cases and 30 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory blood test results were compared between the two groups. The cutoff values were determined by receiver operating characteristic curve analysis. Logistic regression analysis was performed to identify the independent risk factors for severe COVID-19. Results The patients with mild and severe COVID-19 had significant differences in terms of cancer incidence, age, pretreatment neutrophil-to-lymphocyte ratio (NLR), and pretreatment C-reactive protein-to-albumin ratio (CAR) ( P =0.000;P =0.008;P=0.000;P =0.000). The severity of COVID-19 was positively correlated with comorbid cancer, age, NLR, and CAR ( P <0.005). Multivariate logistic regression analysis showed that age, the NLR and the CAR were independent risk factors for severe COVID-19 (OR=1.086, P =0.008;OR=1.512, P =0.007;OR=17.652, P =0.001). Conclusion An increased CAR can serve as an early warning sign of severe COVID-19 in conjunction with the NLR and age.

3.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-323713

ABSTRACT

Objective: The aim of this study was to identify early warning signs for severe novel coronavirus-infected pneumonia (COVID-19). Methods: We retrospectively analyzed the clinical data of 90 patients with COVID-19 at the Guanggu District of Hubei Women and Children Medical and Healthcare Center comprising 60 mild cases and 30 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory blood test results were compared between the two groups. Logistic regression analysis was performed to identify the independent risk factors that predicted severe COVID-19. The receiver-operating characteristic (ROC) curve of independent risk factors was calculated, and the area under the curve (AUC) was used to evaluate the efficiency of the prediction of severe COVID-19. Results: The patients with mild and severe COVID-19 showed significant differences in terms of cancer incidence, age, pretreatment neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP) and the serum albumin (ALB) level (P<0.05). The severity of COVID-19 was correlated positively with the comorbidity of cancer, age, NLR, and CRP but was negatively correlated with the ALB level (P<0.05). Multivariate logistic regression analysis showed that the NLR and ALB level were independent risk factors for severe COVID-19 (OR=1.319, 95% CI: 1.043-1.669, P=0.021;OR=0.739, 95% CI: 0.616-0.886, P=0.001), with AUCs of 0.851 and 0.128, respectively. An NLR of 4.939 corresponded to the maximum joint sensitivity and specificity according to the ROC curve (0.700 and 0.917, respectively). Conclusion: An increased NLR can serve as an early warning sign of severe COVID-19.

4.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-312651

ABSTRACT

The COVID-19 pandemic poses unprecedented challenges around the world. Many studies indicate that human mobility data provide significant support for public health actions during the pandemic. Researchers have applied mobility data to explore spatiotemporal trends over time, investigate associations with other variables, and predict or simulate the spread of COVID-19. Our objective was to provide a comprehensive overview of human mobility open data to guide researchers and policymakers in conducting data-driven evaluations and decision-making for the COVID-19 pandemic and other infectious disease outbreaks. We summarized the mobility data usage in COVID-19 studies by reviewing recent publications on COVID-19 and human mobility from a data-oriented perspective. We identified three major sources of mobility data: public transit systems, mobile operators, and mobile phone applications. Four approaches have been commonly used to estimate human mobility: public transit-based flow, social activity patterns, index-based mobility data, and social media-derived mobility data. We compared mobility datasets’ characteristics by assessing data privacy, quality, space-time coverage, high-performance data storage and processing, and accessibility. We also present challenges and future directions of using mobility data. This review makes a pivotal contribution to understanding the use of and access to human mobility data in the COVID-19 pandemic and future disease outbreaks.

5.
Cell Res ; 31(12): 1244-1262, 2021 12.
Article in English | MEDLINE | ID: covidwho-1493090

ABSTRACT

The infusion of coronavirus disease 2019 (COVID-19) patients with mesenchymal stem cells (MSCs) potentially improves clinical symptoms, but the underlying mechanism remains unclear. We conducted a randomized, single-blind, placebo-controlled (29 patients/group) phase II clinical trial to validate previous findings and explore the potential mechanisms. Patients treated with umbilical cord-derived MSCs exhibited a shorter hospital stay (P = 0.0198) and less time required for symptoms remission (P = 0.0194) than those who received placebo. Based on chest images, both severe and critical patients treated with MSCs showed improvement by day 7 (P = 0.0099) and day 21 (P = 0.0084). MSC-treated patients had fewer adverse events. MSC infusion reduced the levels of C-reactive protein, proinflammatory cytokines, and neutrophil extracellular traps (NETs) and promoted the maintenance of SARS-CoV-2-specific antibodies. To explore how MSCs modulate the immune system, we employed single-cell RNA sequencing analysis on peripheral blood. Our analysis identified a novel subpopulation of VNN2+ hematopoietic stem/progenitor-like (HSPC-like) cells expressing CSF3R and PTPRE that were mobilized following MSC infusion. Genes encoding chemotaxis factors - CX3CR1 and L-selectin - were upregulated in various immune cells. MSC treatment also regulated B cell subsets and increased the expression of costimulatory CD28 in T cells in vivo and in vitro. In addition, an in vivo mouse study confirmed that MSCs suppressed NET release and reduced venous thrombosis by upregulating kindlin-3 signaling. Together, our results underscore the role of MSCs in improving COVID-19 patient outcomes via maintenance of immune homeostasis.


Subject(s)
COVID-19/therapy , Immunomodulation , Mesenchymal Stem Cell Transplantation , Aged , Animals , Antibodies, Viral/blood , B-Lymphocyte Subsets/cytology , B-Lymphocyte Subsets/immunology , B-Lymphocyte Subsets/metabolism , C-Reactive Protein/analysis , COVID-19/immunology , COVID-19/virology , Cytokines/genetics , Cytokines/metabolism , Cytoskeletal Proteins/metabolism , Disease Models, Animal , Extracellular Traps/metabolism , Female , Humans , Leukocytes, Mononuclear/cytology , Leukocytes, Mononuclear/metabolism , Male , Mice , Mice, Inbred C57BL , Middle Aged , SARS-CoV-2/isolation & purification , T-Lymphocytes/cytology , T-Lymphocytes/immunology , T-Lymphocytes/metabolism , Venous Thrombosis/metabolism , Venous Thrombosis/pathology
6.
BMJ Open ; 11(10): e043790, 2021 10 01.
Article in English | MEDLINE | ID: covidwho-1448013

ABSTRACT

OBJECTIVES: As early prediction of severe illness and death for patients with coronavirus disease 2019 (COVID-19) is important, we aim to explore the clinical value of laboratory indicators in evaluating the progression and prognosis of patients with COVID-19. DESIGN: Retrospective cohort study. SETTING: Hospital-based study in China. PARTICIPANTS: Adult patients with COVID-19 from December 15, 2019 to March 15, 2020. END POINT: Disease severity and mortality. METHODS: Clinical data of 638 patients with COVID-19 were collected and compared between severe and non-severe groups. The predictive ability of laboratory indicators in disease progression and prognosis of COVID-19 was analysed using the receiver operating characteristic curve. The survival differences of COVID-19 patients with different levels of laboratory indicators were analysed utilising Kaplan-Meier analysis. RESULTS: 29.8% (190/638) of patients with COVID-19 progressed to severe. Compared with patients with no adverse events, C reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR) and D-dimer were significantly higher in severe patients with adverse events, such as acute myocardial injury, respiratory failure, acute kidney injury, mechanical ventilation, intensive care unit admission, multiple organ dysfunction syndromes and death (all p<0.05). The multivariate logistic analysis suggested that CRP, NLR and D-dimer were independent risk factors for the disease progression of COVID-19 (all p<0.05). The model combining all of them owned the highest area under the receiver operating characteristic curve (AUC) predicting disease progression and death of COVID-19, with AUC of 0.894 (95% CI 0.857 to 0.931) and 0.918 (95% CI 0.873 to 0.962), respectively. Survival analysis suggested that the patients with a high level of CRP, NLR or D-dimer performed shorter overall survival time (all p<0.05). CONCLUSIONS: The combination of CRP, NLR and D-dimer could be an effective predictor for the aggravation and death in patients with COVID-19. The abnormal expression of these indicators might suggest a strong inflammatory response and multiple adverse events in patients with severe COVID-19.


Subject(s)
COVID-19 , Laboratories , Adult , Disease Progression , Humans , Neutrophils , Prognosis , ROC Curve , Retrospective Studies , SARS-CoV-2
8.
Clin Respir J ; 15(3): 293-309, 2021 Mar.
Article in English | MEDLINE | ID: covidwho-916058

ABSTRACT

INTRODUCTION: COVID-19 has spread rapidly worldwide and has been declared a pandemic. OBJECTIVES: To delineate clinical features of COVID-19 patients with different severities and prognoses and clarify the risk factors for disease progression and death at an early stage. METHODS: Medical history, laboratory findings, treatment and outcome data from 214 hospitalised patients with COVID-19 pneumonia admitted to Eastern Campus of Renmin Hospital, Wuhan University in China were collected from 30 January 2020 to 20 February 2020, and risk factors associated with clinical deterioration and death were analysed. The final date of follow-up was 21 March 2020. RESULTS: Age, comorbidities, higher neutrophil cell counts, lower lymphocyte counts and subsets, impairment of liver, renal, heart, coagulation systems, systematic inflammation and clinical scores at admission were significantly associated with disease severity. Ten (16.1%) moderate and 45 (47.9%) severe patients experienced deterioration after admission, and median time from illness onset to clinical deterioration was 14.7 (IQR 11.3-18.5) and 14.5 days (IQR 11.8-20.0), respectively. Multivariate analysis showed increased Hazards Ratio of disease progression associated with older age, lymphocyte count <1.1 × 109/L, blood urea nitrogen (BUN)> 9.5 mmol/L, lactate dehydrogenase >250 U/L and procalcitonin >0.1 ng/mL at admission. These factors were also associated with the risk of death except for BUN. Prediction models in terms of nomogram for clinical deterioration and death were established to illustrate the probability. CONCLUSIONS: These findings provide insights for early detection and management of patients at risk of disease progression or even death, especially older patients and those with comorbidities.


Subject(s)
COVID-19/diagnosis , Hospitalization/trends , Pandemics , SARS-CoV-2 , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , China/epidemiology , Disease Progression , Female , Hospital Mortality/trends , Humans , Male , Middle Aged , Prognosis , Retrospective Studies , Risk Factors , Survival Rate/trends
9.
Am J Med ; 134(1): e6-e14, 2021 01.
Article in English | MEDLINE | ID: covidwho-640122

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease that first appeared in Wuhan, China, and quickly spread throughout the world. We aimed to understand the relationship between diabetes mellitus and the prognosis of COVID-19. METHODS: Demographic, clinical, laboratory, radiologic, treatments, complications, and clinical outcomes data were extracted from electronic medical records and compared between diabetes (n = 84) and nondiabetes (n = 500) groups. Kaplan-Meier method and multivariate Cox analysis were applied to determine the risk factors for the prognosis of COVID-19. RESULTS: Compared with nondiabetic patients, diabetic patients had higher levels of neutrophils (P = .014), C-reactive protein (P = .008), procalcitonin (P < .01), and D-dimer (P = .033), and lower levels of lymphocytes (P = .032) and albumin (P = .035). Furthermore, diabetic patients had a significantly higher incidence of bilateral pneumonia (86.9%, P = .020). In terms of complications and clinical outcomes, the incidence of respiratory failure (36.9% vs 24.2%, P = .022), acute cardiac injury (47.4% vs 21.2%, P < .01), and death (20.2% vs 8.0%, P = .001) in the diabetes group was significantly higher than that in the nondiabetes group. Kaplan-Meier survival curve showed that COVID-19 patients with diabetes had a shorter overall survival time. Multivariate Cox analysis indicated that diabetes (hazard ratio 2.180, P = .031) was an independent risk factor for COVID-19 prognosis. In subgroup analysis, we divided diabetic patients into insulin-required and non-insulin-required groups according to whether they needed insulin, and found that diabetic patients requiring insulin may have a higher risk of disease progression and worse prognosis after the infection of severe acute respiratory syndrome coronavirus 2. CONCLUSIONS: Diabetes is an independent risk factor for the prognosis of COVID-19. More attention should be paid to the prevention and treatment for diabetic patients, especially those who require insulin therapy.


Subject(s)
COVID-19/diagnosis , COVID-19/pathology , Diabetes Mellitus , SARS-CoV-2 , Adolescent , Adult , Aged , Aged, 80 and over , Cohort Studies , Female , Humans , Male , Middle Aged , Prognosis , Retrospective Studies , Young Adult
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