Financial inclusion role on energy efficiency financing gaps in COVID-19 period: empirical outcomes of emerging nations.

The structural imposed crises of the COVID-19 have halted the system of financial intermediation at large. By this, the energy sector needs huge financing for energy efficiency maximization in the COVID-19 crises. Thus, the current research aims to inquire the role of financial inclusion in filling the energy efficiency financing gaps for the period of COVID-19 outbreak. The governments of many countries are facing fiscal deficits and trying to survive under tight substantial fiscal limitations. So providing a cheap and efficient energy in modern times, under COVID-19 crises, is merely impossible for many economies because the main source of income for energy sector is the energy users, and having inefficient energy for consumption is raising energy poverty at large. Therefore, COVID-19 crises raised a wide energy financing gap in modern times that needs a fix. However, this research is suggesting the system to make financial inclusion structure as effective, to fill the energy financing gap, for post-COVID-19 time, and to develop a viable and sustainable financing option for energy sector in long-run perspective. This study also validated the empirical role of financial inclusion on energy poverty and energy efficiency, with historical data, to justify the significance of financial inclusion for energy financing gap fulfillment. More so, this paper is also recommending new policy implications for the stakeholders to utilize. We believe if the recommended policy recommendations are considered for practice, the energy financing gap in post-COVID-19 era would be mitigated, and there is a high probability to supply the efficient energy to the end users.

Longitudinal Assessment of Chest CT Findings and Pulmonary Function after COVID-19 Infection.

Background Information on pulmonary sequelae and pulmonary function 2 years after recovery from SARS-CoV-2 infection is lacking. Purpose To longitudinally assess changes in chest CT abnormalities and pulmonary function in individuals after SARS-CoV-2 infection. Materials and Methods In this prospective study, participants discharged from the hospital after SARS-CoV-2 infection from January 20 to March 10, 2020, were considered for enrollment. Participants without chest CT scans at admission or with complete resolution of lung abnormalities at discharge were excluded. Serial chest CT scans and pulmonary function test results were obtained 6 months (June 20 to August 31, 2020), 12 months (December 20, 2020, to February 3, 2021), and 2 years (November 16, 2021, to January 10, 2022) after symptom onset. The term interstitial lung abnormality (ILA) and two subcategories, fibrotic ILAs and nonfibrotic ILAs, were used to describe residual CT abnormalities on follow-up CT scans. Differences between groups were compared with the χ2 test, Fisher exact test, or independent samples t test. Results Overall, 144 participants (median age, 60 years [range, 27-80 years]; 79 men) were included. On 2-year follow-up CT scans, 39% of participants (56 of 144) had ILAs, including 23% (33 of 144) with fibrotic ILAs and 16% (23 of 144) with nonfibrotic ILAs. The remaining 88 of 144 participants (61%) showed complete radiologic resolution. Over 2 years, the incidence of ILAs gradually decreased (54%, 42%, and 39% of participants at 6 months, 12 months, and 2 years, respectively; P < .001). Respiratory symptoms (34% vs 15%, P = .007) and abnormal diffusing capacity of lung for carbon monoxide (43% vs 20%, P = .004) occurred more frequently in participants with ILAs than in those with complete radiologic resolution. Conclusion More than one-third of participants had persistent interstitial lung abnormalities 2 years after COVID-19 infection, which were associated with respiratory symptoms and decreased diffusion pulmonary function. Chinese Clinical Trial Registry no. ChiCTR2000038609 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by van Beek in this issue.

Quantitative inspiratory-expiratory chest CT findings in COVID-19 survivors at the 6-month follow-up.

We evaluated pulmonary sequelae in COVID-19 survivors by quantitative inspiratory-expiratory chest CT (QCT) and explored abnormal pulmonary diffusion risk factors at the 6-month follow-up. This retrospective study enrolled 205 COVID-19 survivors with baseline CT data and QCT scans at 6-month follow-up. Patients without follow-up pulmonary function tests were excluded. All subjects were divided into group 1 (carbon monoxide diffusion capacity [DLCO] < 80% predicted, n = 88) and group 2 (DLCO ≥ 80% predicted, n = 117). Clinical characteristics and lung radiological changes were recorded. Semiquantitative total CT score (0-25) was calculated by adding five lobes scores (0-5) according to the range of lesion involvement (0: no involvement; 1: < 5%; 2: 5-25%; 3: 26-50%; 4: 51-75%; 5: > 75%). Data was analyzed by two-sample t-test, Spearman test, etc. 29% survivors showed air trapping by follow-up QCT. Semiquantitative CT score and QCT parameter of air trapping in group 1 were significantly greater than group 2 (p < 0.001). Decreased DLCO was negatively correlated with the follow-up CT score for ground-glass opacity (r = - 0.246, p = 0.003), reticulation (r = - 0.206, p = 0.002), air trapping (r = - 0.220, p = 0.002) and relative lung volume changes (r = - 0.265, p = 0.001). COVID-19 survivors with lung diffusion deficits at 6-month follow-up tended to develop air trapping, possibly due to small-airway impairment.

Deep diagnostic agent forest (DDAF): A deep learning pathogen recognition system for pneumonia based on CT.

BACKGROUND: Even though antibiotics agents are widely used, pneumonia is still one of the most common causes of death around the world. Some severe, fast-spreading pneumonia can even cause huge influence on global economy and life security. In order to give optimal medication regimens and prevent infectious pneumonia's spreading, recognition of pathogens is important. METHOD: In this single-institution retrospective study, 2,353 patients with their CT volumes are included, each of whom was infected by one of 12 known kinds of pathogens. We propose Deep Diagnostic Agent Forest (DDAF) to recognize the pathogen of a patient based on ones' CT volume, which is a challenging multiclass classification problem, with large intraclass variations and small interclass variations and very imbalanced data. RESULTS: The model achieves 0.899 ± 0.004 multi-way area under curves of receiver (AUC) for level-I pathogen recognition, which are five rough groups of pathogens, and 0.851 ± 0.003 AUC for level-II recognition, which are 12 fine-level pathogens. The model also outperforms the average result of seven human readers in level-I recognition and outperforms all readers in level-II recognition, who can only reach an average result of 7.71 ± 4.10% accuracy. CONCLUSION: Deep learning model can help in recognition pathogens using CTs only, which might help accelerate the process of etiological diagnosis.

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.

Chest CT Imaging Features of Typical Covert COVID-19 Cases.

Purpose: To analyze the chest CT imaging findings of patients with initial negative RT-PCR and to compare with the CT findings of the same sets of patients when the RT-PCR turned positive for SARS-CoV-2 a few days later. Materials and methods: A total of 32 patients (8 males and 24 females; 52.9±7years old) with COVID-19 from 27 January and 26 February 2020 were enrolled in this retrospective study. Clinical and radiological characteristics were analyzed. Results: The median period (25%, 75%) between initial symptoms and the first chest CT, the initial negative RT-PCR, the second CT and the positive RT-PCR were 7(4.25,11.75), 7(5,10.75), 15(11,23) and 14(10,22) days, respectively. Ground glass opacities was the most frequent CT findings at both the first and second CTs. Consolidation was more frequently observed on lower lobes, and more frequently detected during the second CT (64.0%) with positive RT-PCR than the first CT with initial negative RT-PCR (53.1%). The median of total lung severity score and the number of lobes affected had significant difference between twice chest CT (P=0.007 and P=0.011, respectively). Conclusion: In the first week of disease course, CT was sensitive to the COVID-19 with initial negative RT-PCR. Throat swab test turned positive while chest CT mostly demonstrated progression.

Epidemiological, Clinical, Radiological Characteristics and Outcomes of Medical Staff with COVID-19 in Wuhan, China: Analysis of 101 Cases.

Objectives: As of 11 Feb 2020, a total of 1,716 medical staff infected with laboratory-confirmed the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) in China had been reported. The predominant cause of the infection among medical staff remains unclear. We sought to explore the epidemiological, clinical characteristics and prognosis of infected medical staff. Methods: Medical staff who infected with SARS-Cov-2 and admitted to Union Hospital, Wuhan between 16 Jan to 25 Feb, 2020 were included in this single-centered, retrospective study. Data were compared by occupation and analyzed with the Kaplan-Meier and Cox regression methods. Results: A total of 101 medical staff (32 males and 69 females; median age: 33) were included in this study and 74.3% were nurses. A small proportion of the cohort had contact with specimens (3%) as well as patients infected with SARS-Cov-2 in fever clinics (15%) and isolation wards (3%). 80% of medical staff showed abnormal IL-6 levels and 33% had lymphocytopenia. Chest CT mainly manifested as bilateral (62%), septal/subpleural (77%) and groundglass opacities (48%). The major differences between doctors and nurses manifested in laboratory indicators. As of the last observed date, no patient was transferred to intensive care unit or died. Fever (HR=0.57; 95% CI 0.36-0.90) and IL-6 levels greater than 2.9 pg/ml (HR=0.50; 95% CI 0.30-0.86) were unfavorable factors for discharge. Conclusions: Our findings suggested that the infection of medical staff mainly occurred at the early stages of SARS-CoV-2 epidemic in Wuhan, and only a small proportion of infection had an exact mode. Meanwhile, medical staff infected with COVID-19 have relatively milder symptoms and favorable clinical course than ordinary patients, which may be partly due to their medical expertise, younger age and less underlying diseases. The potential risk factors of fever and IL-6 levels greater than 2.9 pg/ml could help to identify medical staff with poor prognosis at an early stage.

Six-month Follow-up Chest CT Findings after Severe COVID-19 Pneumonia.

Background Little is known about the long-term lung radiographic changes in patients who have recovered from coronavirus disease 2019 (COVID-19), especially those with severe disease. Purpose To prospectively assess pulmonary sequelae and explore the risk factors for fibrotic-like changes in the lung at 6-month follow-up chest CT of survivors of severe COVID-19 pneumonia. Materials and Methods A total of 114 patients (80 [70%] men; mean age, 54 years ± 12) were studied prospectively. Initial and follow-up CT scans were obtained a mean of 17 days ± 11 and 175 days ± 20, respectively, after symptom onset. Lung changes (opacification, consolidation, reticulation, and fibrotic-like changes) and CT extent scores (score per lobe, 0-5; maximum score, 25) were recorded. Participants were divided into two groups on the basis of their 6-month follow-up CT scan: those with CT evidence of fibrotic-like changes (traction bronchiectasis, parenchymal bands, and/or honeycombing) (group 1) and those without CT evidence of fibrotic-like changes (group 2). Between-group differences were assessed with the Fisher exact test, two-sample t test, or Mann-Whitney U test. Multiple logistic regression analyses were performed to identify the independent predictive factors of fibrotic-like changes. Results At follow-up CT, evidence of fibrotic-like changes was observed in 40 of the 114 participants (35%) (group 1), whereas the remaining 74 participants (65%) showed either complete radiologic resolution (43 of 114, 38%) or residual ground-glass opacification or interstitial thickening (31 of 114, 27%) (group 2). Multivariable analysis identified age of greater than 50 years (odds ratio [OR]: 8.5; 95% CI: 1.9, 38; P = .01), heart rate greater than 100 beats per minute at admission (OR: 5.6; 95% CI: 1.1, 29; P = .04), duration of hospital stay greater than or equal to 17 days (OR: 5.5; 95% CI: 1.5, 21; P = .01), acute respiratory distress syndrome (OR: 13; 95% CI: 3.3, 55; P < .001), noninvasive mechanical ventilation (OR: 6.3; 95% CI: 1.3, 30; P = .02), and total CT score of 18 or more (OR: 4.2; 95% CI: 1.2, 14; P = .02) at initial CT as independent predictors for fibrotic-like changes in the lung at 6 months. Conclusion Six-month follow-up CT showed fibrotic-like changes in the lung in more than one-third of patients who survived severe coronavirus disease 2019 pneumonia. These changes were associated with an older age, acute respiratory distress syndrome, longer hospital stays, tachycardia, noninvasive mechanical ventilation, and higher initial chest CT score. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Wells et al in this issue.

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.

Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning.

Data from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling. Here, we describe an open resource containing data from 1,521 patients with pneumonia (including COVID-19 pneumonia) consisting of chest computed tomography (CT) images, 130 clinical features (from a range of biochemical and cellular analyses of blood and urine samples) and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical status. We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices. In an independent validation cohort of 351 patients, the algorithm discriminated between negative, mild and severe cases with areas under the receiver operating characteristic curve of 0.944, 0.860 and 0.884, respectively. The open database may have further uses in the diagnosis and management of patients with COVID-19.

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.

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis.

Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .

Novel Coronavirus Disease 2019 (COVID-19) Pneumonia Progression Course in 17 Discharged Patients: Comparison of Clinical and Thin-Section Computed Tomography Features During Recovery.

BACKGROUND: Our objective was to retrospectively analyze the evolution of clinical features and thin-section computed tomography (CT) imaging of novel coronavirus disease 2019 (COVID-19) pneumonia in 17 discharged patients. METHODS: Serial thin-section CT scans of 17 discharged patients with COVID-19 were obtained during recovery. Longitudinal changes of clinical parameters and a CT pattern were documented in all patients during the 4 weeks after admission. A CT score was used to evaluate the extent of the disease. RESULTS: There were marked improvements of fever, lymphocyte counts, C-reactive proteins, and erythrocyte sedimentation rates within the first 2 weeks after admission. However, the mean CT score rapidly increased from the first to the third week, with a top score of 8.2 obtained in the second week. During the first week, the main CT pattern was ground-glass opacities (GGO; 76.5%). The frequency of GGO (52.9%) decreased in the second week. Consolidation and mixed patterns (47.0%) were noted in the second week. Thereafter, consolidations generally dissipated into GGO, and the frequency of GGO increased in the third week (76.5%) and fourth week (71.4%). Opacities were mainly located in the peripheral (76.5%) and subpleural (47.1%) zones of the lungs; they presented as focal (35.3%) or multifocal (29.4%) in the first week and became more diffuse in the second (47.1%) and third weeks (58.8%), then showed a reduced extent in fourth week (50%). CONCLUSIONS: The progression course of the CT pattern was later than the progression of the clinical parameters within the first 2 weeks after admission; however, there were synchronized improvements in both the clinical and radiologic features in the fourth week.

Severe Acute Respiratory Syndrome Coronavirus 2 among Asymptomatic Workers Screened for Work Resumption, China.

After the outbreak in Wuhan, China, we assessed 29,299 workers screened for severe acute respiratory syndrome coronavirus 2 by reverse transcription PCR. We noted 18 (0.061%) cases of asymptomatic infection; 13 turned negative within 8.0 days, and 41 close contacts tested negative. Among 6 contacts who had serologic tests, none were positive.

Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study.

BACKGROUND: A cluster of patients with coronavirus disease 2019 (COVID-19) pneumonia caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were successively reported in Wuhan, China. We aimed to describe the CT findings across different timepoints throughout the disease course. METHODS: Patients with COVID-19 pneumonia (confirmed by next-generation sequencing or RT-PCR) who were admitted to one of two hospitals in Wuhan and who underwent serial chest CT scans were retrospectively enrolled. Patients were grouped on the basis of the interval between symptom onset and the first CT scan: group 1 (subclinical patients; scans done before symptom onset), group 2 (scans done ≤1 week after symptom onset), group 3 (>1 week to 2 weeks), and group 4 (>2 weeks to 3 weeks). Imaging features and their distribution were analysed and compared across the four groups. FINDINGS: 81 patients admitted to hospital between Dec 20, 2019, and Jan 23, 2020, were retrospectively enrolled. The cohort included 42 (52%) men and 39 (48%) women, and the mean age was 49·5 years (SD 11·0). The mean number of involved lung segments was 10·5 (SD 6·4) overall, 2·8 (3·3) in group 1, 11·1 (5·4) in group 2, 13·0 (5·7) in group 3, and 12·1 (5·9) in group 4. The predominant pattern of abnormality observed was bilateral (64 [79%] patients), peripheral (44 [54%]), ill-defined (66 [81%]), and ground-glass opacification (53 [65%]), mainly involving the right lower lobes (225 [27%] of 849 affected segments). In group 1 (n=15), the predominant pattern was unilateral (nine [60%]) and multifocal (eight [53%]) ground-glass opacities (14 [93%]). Lesions quickly evolved to bilateral (19 [90%]), diffuse (11 [52%]) ground-glass opacity predominance (17 [81%]) in group 2 (n=21). Thereafter, the prevalence of ground-glass opacities continued to decrease (17 [57%] of 30 patients in group 3, and five [33%] of 15 in group 4), and consolidation and mixed patterns became more frequent (12 [40%] in group 3, eight [53%] in group 4). INTERPRETATION: COVID-19 pneumonia manifests with chest CT imaging abnormalities, even in asymptomatic patients, with rapid evolution from focal unilateral to diffuse bilateral ground-glass opacities that progressed to or co-existed with consolidations within 1-3 weeks. Combining assessment of imaging features with clinical and laboratory findings could facilitate early diagnosis of COVID-19 pneumonia. FUNDING: None.