Mapping a Country Image from Global News Reports about COVID-19 Pandemic.

The outbreak of the coronavirus disease of 2019 (COVID-19) pandemic has infected hundreds of millions of people worldwide and caused millions of deaths. This study used media analysis and correlation analysis to elucidate the significant differences in the ways in which news reports from 228 countries discussed a specific country when covering the COVID-19 pandemic. Media reports analysed in this study were collected from the Global Database of Events, Language, and Tone project (GDELT). These differences were found to be deeply embedded in the economic, socio-political, and cultural contexts of different countries. The findings reinforced the hypothetical assumption in framing theory and promoted a measurable and upscaled use of framing theory into macro geography studies. This study highlights the urgent need of a geo-political examination of COVID-19 in the global context-an area with insufficient interest from interdisciplinary perspective beyond epidemiology. Further research can be of great value for the promotion of an effective international cooperation mechanism to curb the spread of COVID-19. Supplementary Information: The online version contains supplementary material available at 10.1007/s12061-022-09498-4.

Growth of a fluid-infused patch from droplet drainage into a thin porous layer

Growth of a fluid-infused patch on a thin porous layer, e.g. on a piece of paper or cloth, is related to the transmission of virus particles through exhaled droplets and aerosols. We present a theoretical model to describe how a wet patch develops gradually through imbibition, once a sessile droplet attaches at a permeable surface and drains gradually into a thin porous layer. Two limiting cases are considered based on different assumptions on the motion of the contact line during the coupled process of drop drainage and patch growth: (i) the apparent contact angle remains unchanged, so the radius of a sessile droplet decreases with time;and (ii) the location of the contact line remains pinned, so the contact angle decreases as time progresses. The model leads to evolution pathways for both the droplet and the fluid film within the porous layer, without introducing arbitrary fitting parameters. Potential implications of the model and its solutions are also discussed briefly in the context of the outspread of COVID-19, employing physical parameters for exhaled droplets, paper and cloth.

Risk assessment for precise intervention of COVID-19 epidemic based on available big data and spatio-temporal simulation method: Empirical evidence from different public places in Guangzhou, China.

Risk assessment of the intra-city spatio-temporal spreading of COVID-19 is important for providing location-based precise intervention measures, especially when the epidemic occurred in the densely populated and high mobile public places. The individual-based simulation has been proven to be an effective method for the risk assessment. However, the acquisition of individual-level mobility data is limited. This study used publicly available datasets to approximate dynamic intra-city travel flows by a spatio-temporal gravity model. On this basis, an individual-based epidemic model integrating agent-based model with the susceptible-exposed-infectious-removed (SEIR) model was proposed and the intra-city spatio-temporal spreading process of COVID-19 in eleven public places in Guangzhou China were explored. The results indicated that the accuracy of dynamic intra-city travel flows estimated by available big data and gravity model is acceptable. The spatio-temporal simulation method well presented the process of COVID-19 epidemic. Four kinds of spatial-temporal transmission patterns were identified and the pattern was highly dependent on the urban spatial structure and location. It indicated that location-based precise intervention measures should be implemented according to different regions. The approach of this research can be used by policy-makers to make rapid and accurate risk assessments and to implement intervention measures ahead of epidemic outbreaks.

Optimizing Spatial Allocation of COVID-19 Vaccine by Agent-Based Spatiotemporal Simulations.

Optimizing allocation of vaccine, a highly scarce resource, is an urgent and critical issue during fighting against on-going COVID-19 epidemic. Prior studies suggested that vaccine should be prioritized by age and risk groups, but few of them have considered the spatial prioritization strategy. This study aims to examine the spatial heterogeneity of COVID-19 transmission in the city naturally, and optimize vaccine distribution strategies considering spatial prioritization. We proposed an integrated spatial model of agent-based model and SEIR (susceptible-exposed-infected-recovered). It simulated spatiotemporal process of COVID-19 transmission in a realistic urban context. Individual movements were represented by trajectories of 8,146 randomly sampled mobile phone users on December 28, 2016 in Guangzhou, China, 90% of whom aged 18-60. Simulations were conducted under seven scenarios. Scenarios 1 and 2 examined natural spreading process of COVID-19 and its final state of herd immunity. Scenarios 3-6 applied four vaccination strategies (random strategy, age strategy, space strategy, and space & age strategy), and identified the optimal vaccine strategy. Scenario 7 assessed the most appropriate vaccine coverage. The results demonstrates herd immunity is heterogeneously distributed in space, thus, vaccine intervention strategies should be spatialized. Among four strategies, space & age strategy is substantially most efficient, with 7.7% fewer in attack rate and 44 days longer than random strategy under 20% vaccine uptake. Space & age strategy requires 30%-40% vaccine coverage to control the epidemic, while the coverage for a random strategy is 60%-70% as a comparison. The application of our research would greatly improves the effectiveness of the vaccine usability.

Genomic Evolution and Variation of SARS-CoV-2 in the Early Phase of COVID-19 Pandemic in Guangdong Province, China.

Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) with unknown origin spread rapidly to 222 countries, areas or territories. To investigate the genomic evolution and variation in the early phase of COVID-19 pandemic in Guangdong, 60 specimens of SARS-CoV-2 were used to perform whole genome sequencing, and genomics, amino acid variation and Spike protein structure modeling analyses. Phylogenetic analysis suggested that the early variation in the SARS-CoV-2 genome was still intra-species, with no evolution to other coronaviruses. There were one to seven nucleotide variations (SNVs) in each genome and all SNVs were distributed in various fragments of the genome. The Spike protein bound with human receptor, an amino acid salt bridge and a potential furin cleavage site were found in the SARS-CoV-2 using molecular modeling. Our study clarified the characteristics of SARS-CoV-2 genomic evolution, variation and Spike protein structure in the early phase of local cases in Guangdong, which provided reference for generating prevention and control strategies and tracing the source of new outbreaks.

Construction and validation of a machine learning-based nomogram: A tool to predict the risk of getting severe coronavirus disease 2019 (COVID-19).

BACKGROUND: Identifying patients who may develop severe coronavirus disease 2019 (COVID-19) will facilitate personalized treatment and optimize the distribution of medical resources. METHODS: In this study, 590 COVID-19 patients during hospitalization were enrolled (Training set: n = 285; Internal validation set: n = 127; Prospective set: n = 178). After filtered by two machine learning methods in the training set, 5 out of 31 clinical features were selected into the model building to predict the risk of developing severe COVID-19 disease. Multivariate logistic regression was applied to build the prediction nomogram and validated in two different sets. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used to evaluate its performance. RESULTS: From 31 potential predictors in the training set, 5 independent predictive factors were identified and included in the risk score: C-reactive protein (CRP), lactate dehydrogenase (LDH), Age, Charlson/Deyo comorbidity score (CDCS), and erythrocyte sedimentation rate (ESR). Subsequently, we generated the nomogram based on the above features for predicting severe COVID-19. In the training cohort, the area under curves (AUCs) were 0.822 (95% CI, 0.765-0.875) and the internal validation cohort was 0.762 (95% CI, 0.768-0.844). Further, we validated it in a prospective cohort with the AUCs of 0.705 (95% CI, 0.627-0.778). The internally bootstrapped calibration curve showed favorable consistency between prediction by nomogram and the actual situation. And DCA analysis also conferred high clinical net benefit. CONCLUSION: In this study, our predicting model based on five clinical characteristics of COVID-19 patients will enable clinicians to predict the potential risk of developing critical illness and thus optimize medical management.

The prediction for development of COVID-19 in global major epidemic areas through empirical trends in China by utilizing state transition matrix model.

BACKGROUND: Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to its high transmissibility. We aimed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from China. METHODS: Data from reports released by the National Health Commission of the People's Republic of China (Dec 31, 2019 to Mar 5, 2020) and the World Health Organization (Jan 20, 2020 to Mar 5, 2020) were extracted as the training set and the data from Mar 6 to 9 as the validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death were collected and analyzed. We analyzed the data above through the State Transition Matrix model. RESULTS: The optimistic scenario (non-Hubei model, daily increment rate of - 3.87%), the cautiously optimistic scenario (Hubei model, daily increment rate of - 2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of - 1.50%) were inferred and modeling from data in China. The IFP of time in South Korea would be Mar 6 to 12, Italy Mar 10 to 24, and Iran Mar 10 to 24. The numbers of cumulative confirmed patients will reach approximately 20 k in South Korea, 209 k in Italy, and 226 k in Iran under fitting scenarios, respectively. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be earlier than predicted above. CONCLUSION: The end of the pandemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to curb the development of COVID-19.

The diagnosis of SARS-CoV2 pneumonia: A review of laboratory and radiological testing results.

The rapid emergence of coronavirus disease 2019 (COVID-19) has necessitated the implementation of diverse pandemic control strategies throughout the world. To effectively control the spread of this disease, it is essential that it be diagnosed at an early stage so that patients can be reliably quarantined such that disease spread will be slowed. At present, the diagnosis of this infectious form of coronavirus pneumonia is largely dependent upon a combination of laboratory testing and imaging analyses of variable diagnostic efficacy. In the present report, we reviewed prior literature pertaining to the diagnosis of different forms of pneumonia caused by coronaviruses (severe acute respiratory syndrome [SARS], Middle East respiratory syndrome, and SARS-CoV-2) and assessed two different potential diagnostic approaches. We ultimately found that computed tomography was associated with a higher rate of diagnostic accuracy than was a real-time quantitative polymerase chain reaction-based approach (P = .0041), and chest radiography (P = .0100). Even so, it is important that clinicians utilize a combination of laboratory and radiological testing where possible to ensure that this virus is reliably and quickly detected such that it may be treated and patients may be isolated in a timely fashion, thereby effectively curbing the further progression of this pandemic.

Patient follow-up after discharge after COVID-19 pneumonia: Considerations for infectious control.

Coronavirus disease 2019 (COVID-19) represents a significant global medical issue, with a growing number of cumulative confirmed cases. However, a large number of patients with COVID-19 have overcome the disease, meeting hospital discharge criteria, and are gradually returning to work and social life. Nonetheless, COVID-19 may cause further downstream issues in these patients, such as due to possible reactivation of the virus, long-term pulmonary defects, and posttraumatic stress disorder. In this study, we, therefore, queried relevant literature concerning severe acute respiratory syndrome, Middle East respiratory syndrome, and COVID-19 for reference to come to a consensus on follow-up strategies. We found that strategies, such as the implementation of polymerase chain reaction testing, imaging surveillance, and psychological assessments, starting at the time of discharge, were necessary for long-term follow-up. If close care is given to every aspect of coronavirus management, we expect that the pandemic outbreak will soon be overcome.

Immune environment modulation in pneumonia patients caused by coronavirus: SARS-CoV, MERS-CoV and SARS-CoV-2.

Currently, we are on a global pandemic of Coronavirus disease-2019 (COVID-19) which causes fever, dry cough, fatigue and acute respiratory distress syndrome (ARDS) that may ultimately lead to the death of the infected. Current researches on COVID-19 continue to highlight the necessity for further understanding the virus-host synergies. In this study, we have highlighted the key cytokines induced by coronavirus infections. We have demonstrated that genes coding interleukins (Il-1α, Il-1ß, Il-6, Il-10), chemokine (Ccl2, Ccl3, Ccl5, Ccl10), and interferon (Ifn-α2, Ifn-ß1, Ifn2) upsurge significantly which in line with the elevated infiltration of T cells, NK cells and monocytes in SARS-Cov treated group at 24 hours. Also, interleukins (IL-6, IL-23α, IL-10, IL-7, IL-1α, IL-1ß) and interferon (IFN-α2, IFN2, IFN-γ) have increased dramatically in MERS-Cov at 24 hours. A similar cytokine profile showed the cytokine storm served a critical role in the infection process. Subsequent investigation of 463 patients with COVID-19 disease revealed the decreased amount of total lymphocytes, CD3+, CD4+, and CD8+ T lymphocytes in the severe type patients which indicated COVID-19 can impose hard blows on human lymphocyte resulting in lethal pneumonia. Thus, taking control of changes in immune factors could be critical in the treatment of COVID-19.

The diagnosis of pandemic coronavirus pneumonia: A review of radiology examination and laboratory test.

Since the outbreak of novel coronavirus disease 2019 (COVID-19), epidemic prevention strategies have been implemented worldwide. For the sake of controlling the infectious coronavirus pneumonia, early diagnosis and quarantine play an imperative role. Currently, the mainstream diagnostic methods are imaging and laboratory diagnosis, which differ in their efficacy of diagnosis. To compare the detection rate, we reviewed numerous literature on pneumonia caused by coronaviruses (SARS, MERS, and SARS-CoV-2) and analyzed two different ways of diagnosis. The results showed that the detection rate of computed tomography (CT) diagnosis was significantly higher than that of real-time quantitative polymerase chain reaction (qPCR) (P = 0.00697). Still, clinicians should combine radiology and laboratory methods to achieve a higher detection rate, so that instant isolation and treatment could be effectively conducted to curb the rampant spread of the epidemic.

From Community Acquired Pneumonia to COVID-19: A Deep Learning Based Method for Quantitative Analysis of COVID-19 on thick-section CT Scans (preprint)

Background: Thick-section CT scanners are more affordable for the developing countries. Considering the widely spread COVID-19, it is of great benefit to develop an automated and accurate system for quantification of COVID-19 associated lung abnormalities using thick-section chest CT images. Purpose: To develop a fully automated AI system to quantitatively assess the disease severity and disease progression using thick-section chest CT images. Materials and Methods: In this retrospective study, a deep learning based system was developed to automatically segment and quantify the COVID-19 infected lung regions on thick-section chest CT images. 531 thick-section CT scans from 204 patients diagnosed with COVID-19 were collected from one appointed COVID-19 hospital from 23 January 2020 to 12 February 2020. The lung abnormalities were first segmented by a deep learning model. To assess the disease severity (non-severe or severe) and the progression, two imaging bio-markers were automatically computed, i.e., the portion of infection (POI) and the average infection HU (iHU). The performance of lung abnormality segmentation was examined using Dice coefficient, while the assessment of disease severity and the disease progression were evaluated using the area under the receiver operating characteristic curve (AUC) and the Cohen's kappa statistic, respectively. Results: Dice coefficient between the segmentation of the AI system and the manual delineations of two experienced radiologists for the COVID-19 infected lung abnormalities were 0.74 {+/-} 0.28 and 0.76 {+/-} 0.29, respectively, which were close to the inter-observer agreement, i.e., 0.79 {+/-} 0.25. The computed two imaging bio-markers can distinguish between the severe and non-severe stages with an AUC of 0.9680 (p-value < 0.001). Very good agreement ({kappa} = 0.8220) between the AI system and the radiologists were achieved on evaluating the changes of infection volumes. Conclusions: A deep learning based AI system built on the thick-section CT imaging can accurately quantify the COVID-19 associated lung abnormalities, assess the disease severity and its progressions.

Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images (preprint)

Background: Chest computed tomography (CT) is recognized as an important tool for COVID-19 severity assessment. As the number of affected patients increase rapidly, manual severity assessment becomes a labor-intensive task, and may lead to delayed treatment. Purpose: Using machine learning method to realize automatic severity assessment (non-severe or severe) of COVID-19 based on chest CT images, and to explore the severity-related features from the resulting assessment model. Materials and Method: Chest CT images of 176 patients (age 45.3$\pm$16.5 years, 96 male and 80 female) with confirmed COVID-19 are used, from which 63 quantitative features, e.g., the infection volume/ratio of the whole lung and the volume of ground-glass opacity (GGO) regions, are calculated. A random forest (RF) model is trained to assess the severity (non-severe or severe) based on quantitative features. Importance of each quantitative feature, which reflects the correlation to the severity of COVID-19, is calculated from the RF model. Results: Using three-fold cross validation, the RF model shows promising results, i.e., 0.933 of true positive rate, 0.745 of true negative rate, 0.875 of accuracy, and 0.91 of area under receiver operating characteristic curve (AUC). The resulting importance of quantitative features shows that the volume and its ratio (with respect to the whole lung volume) of ground glass opacity (GGO) regions are highly related to the severity of COVID-19, and the quantitative features calculated from the right lung are more related to the severity assessment than those of the left lung. Conclusion: The RF based model can achieve automatic severity assessment (non-severe or severe) of COVID-19 infection, and the performance is promising. Several quantitative features, which have the potential to reflect the severity of COVID-19, were revealed.