STCovidNet: Automatic Detection Model of Novel Coronavirus Pneumonia Based on Swin Transformer (preprint)

The novel coronavirus disease 2019 (COVID-19) has emerged as an enormous challenge facing China today. Preventive Medicine physicians and Artificial Intelligence (AI) researchers try to improve the ability to early automatic warning of coronavirus infections, promote epidemic prevention, and reduce medical costs using deep learning methods. In this work, we build an extensive database of chest computed tomography (CT) scans with image data from domestic and international open-source medical datasets. Swin Transformer is chosen as the backbone network to establish a model (STCovidNet) for the prediction of COVID-19. We then compare the performance of our technique against that of Vision Transformer (ViT) and Convolutional Neural Network (CNN). Next, to visualize our model's high-dimensional outputs in 2-dimensional space, we apply t-distributed stochastic neighbor embedding (t-SNE) as the dimension-reduction strategy. Finally, we employ gradient-weighted class activation mapping (Grad-CAM) to present a class activation map. The results indicate that STCovidNet’s performance surpasses ViT and CNN with a 0.9811 AUC and 0.9858 accuracy score. Our network outperforms previous techniques to reduce intra-class variability and generate well-separated feature embedding. The CAM figure illustrates that the decision region corresponds to radiologists' detecting spots. The suggested method can be an effective way of catching COVID-19 instances.

The Emergence of COVID-19 Associated Mucormycosis: Analysis of Cases From 18 Countries (preprint)

Coronavirus disease 2019 (COVID-19)–associated mucormycosis (CAM) has recently been increasingly reported, particularly among patients with uncontrolled diabetes. Patients with diabetes and hyperglycemia often display an inflammatory state that may be potentiated by the activation of antiviral immunity to SARS-CoV-2, and thus may favor secondary infections. We analyze 80 published and unpublished cases of CAM, with a predominance (42/80) of cases from India. Uncontrolled diabetes mellitus as well as systemic corticosteroid treatment represented major comorbid predisposing factors and rhino-orbital cerebral mucormycosis was the most frequent presentation of disease. Mortality was high at 49%, driven particularly by those with pulmonary or disseminated mucormycosis and those with cerebral involvement. Furthermore, a significant proportion of surviving patients suffered life-changing morbidities (loss of vision in 46% of survivors). Our review indicates that CAM may be a relevant complication of severe COVID-19, particularly in those with uncontrolled diabetes. Funding: Martin Hoenigl received funding from Astellas for two investigator initiated studies (ISR005824 and ISR005838), and was supported by the National Institutes of Health, Grant UL1TR001442. Agostinho Carvalho was supported by the Fundação para a Ciência e a Tecnologia (FCT) (UIDB/50026/2020 and UIDP/50026/2020), the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) (NORTE-01-0145-FEDER-000039), the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 847507, and the “la Caixa” Foundation (ID 100010434) and FCT under the agreement LCF/PR/HR17/52190003.Declaration of Interest: MH received research funding from Gilead, Pfizer, Astellas, Scynexis and NIH. JPG received speaker and expert advice fees from Pfizer and Gilead. NK has received research grants or honoraria as a speaker or advisor from Astellas, Gilead, MSD, and Pfizer, outside the submitted work. KL received consultancy fees from SMB Laboratoires Brussels, MSD and Gilead, travel support from Pfizer, speaker fees from FUJIFILM WAKO, Pfizer and Gilead, a service fee from Thermo fisher Scientific. OAC is supported by the German Federal Ministry of Research and Education, is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – CECAD, EXC 2030 – 390661388 and has received research grants from, is an advisor to, or received lecture honoraria from Actelion, Allecra Therapeutics, Al-Jazeera Pharmaceuticals, Amplyx, Astellas, Basilea, Biosys, Cidara, Da Volterra, Entasis, F2G, Gilead, Grupo Biotoscana, Immunic, IQVIA, Janssen, Matinas, Medicines Company, MedPace, Melinta Therapeutics, Menarini, Merck/MSD, Mylan, Nabriva, Noxxon, Octapharma, Paratek, Pfizer, PSI, Roche Diagnostics, Scynexis, and Shionogi. PLW performed diagnostic evaluations and received meeting sponsorship from Bruker, Dynamiker, and Launch Diagnostics; Speakers fees, expert advice fees and meeting sponsorship from Gilead; and speaker and expert advice fees 489 from F2G and speaker fees MSD and Pfizer. Is a founding member of the European Aspergillus PCR Initiative. ACh received funding support from educational grant of Pfizer, MSD Pharmaceutical Ltd, and Gilead. All other authors no conflicts.Ethical Approval: MISSING

Predictions, role of interventions and effects of a historic national lockdown in India's response to the COVID-19 pandemic: data science call to arms (preprint)

Importance: India has taken strong and early public health measures for arresting the spread of the COVID-19 epidemic. With only 536 COVID-19 cases and 11 fatalities, India - a democracy of 1.34 billion people - took the historic decision of a 21-day national lockdown on March 25. The lockdown was further extended to May 3, soon after the analysis of this paper was completed. Objective: To study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 cases in India compared to other less severe non-pharmaceutical interventions using epidemiological forecasting models and Bayesian estimation algorithms; to compare effects of hypothetical durations of lockdown from an epidemiological perspective; to study alternative explanations for slower growth rate of the virus outbreak in India, including exploring the association of the number of cases and average monthly temperature; and finally, to outline the pivotal role of reliable and transparent data, reproducible data science methods, tools and products as we reopen the country and prepare for a post lock-down phase of the pandemic. Design, Setting, and Participants: We use the daily data on the number of COVID-19 cases, of recovered and of deaths from March 1 until April 7, 2020 from the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Additionally, we use COVID-19 incidence counts data from Kaggle and the monthly average temperature of major cities across the world from Wikipedia. Main Outcome and Measures: The current time-series data on daily proportions of cases and removed (recovered and death combined) from India are analyzed using an extended version of the standard SIR (susceptible, infected, and removed) model. The eSIR model incorporates time-varying transmission rates that help us predict the effect of lockdown compared to other hypothetical interventions on the number of cases at future time points. A Markov Chain Monte Carlo implementation of this model provided predicted proportions of the cases at future time points along with credible intervals (CI). Results: Our predicted cumulative number of COVID-19 cases in India on April 30 assuming a 1-week delay in people's adherence to a 21-day lockdown (March 25 - April 14) and a gradual, moderate resumption of daily activities after April 14 is 9,181 with upper 95% CI of 72,245. In comparison, the predicted cumulative number of cases under "no intervention" and "social distancing and travel bans without lockdown" are 358 thousand and 46 thousand (upper 95% CI of nearly 2.3 million and 0.3 million) respectively. An effective lockdown can prevent roughly 343 thousand (upper 95% CI 1.8 million) and 2.4 million (upper 95% CI 38.4 million) COVID-19 cases nationwide compared to social distancing alone by May 15 and June 15, respectively. When comparing a 21-day lockdown with a hypothetical lockdown of longer duration, we find that 28-, 42-, and 56-day lockdowns can approximately prevent 238 thousand (upper 95% CI 2.3 million), 622 thousand (upper 95% CI 4.3 million), 781 thousand (upper 95% CI 4.6 million) cases by June 15, respectively. We find some suggestive evidence that the COVID-19 incidence rates worldwide are negatively associated with temperature in a crude unadjusted analysis with Pearson correlation estimates [95% confidence interval] between average monthly temperature and total monthly incidence around the world being -0.185 [-0.548, 0.236] for January, -0.110 [-0.362, 0.157] for February, and -0.173 [-0.314, -0.026] for March. Conclusions and Relevance: The lockdown, if implemented correctly in the end, has a high chance of reducing the total number of COVID-19 cases in the short term, and buy India invaluable time to prepare its healthcare and disease monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for the best outcome. We cannot heavily rely on the hypothetical prevention governed by meteorological factors such as temperature based on current evidence. From an epidemiological perspective, a longer lockdown between 42-56 days is preferable. However, the lockdown comes at a tremendous price to social and economic health through a contagion process not dissimilar to that of the coronavirus itself. Data can play a defining role as we design post-lockdown testing, reopening and resource allocation strategies. Software: Our contribution to data science includes an interactive and dynamic app (covind19.org) with short- and long-term projections updated daily that can help inform policy and practice related to COVID-19 in India. Anyone can visualize the observed data for India and create predictions under hypothetical scenarios with quantification of uncertainties. We make our prediction codes freely available (https://github.com/umich-cphds/cov-ind-19) for reproducible science and for other COVID-19 affected countries to use them for their prediction and data visualization work.