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Acknowledging the extreme risk COVID-19 poses to humans, this paper attempted to analyze and compare case fatality rates, identify the existence of learning curves for COVID-19 medical treatments, and examine the impact of vaccination on fatality rate reduction. Confirmed cases and deaths were extracted from the "Daily Situation Report" provided by the World Health Organization. The results showed that low registration and low viral test rates resulted in low fatality rates, and the learning curve was significant for all countries except China. Treatment for COVID-19 can be improved through repeated experience. Vaccinations in the U.K. and U.S.A. are highly effective in reducing fatality rates, but not in other countries. The positive impact of vaccines may be attributed to higher vaccination rates. In addition to China, this study identified the existence of learning curves for the medical treatment of COVID-19 that can explain the effect of vaccination rates on fatalities. [ABSTRACT FROM AUTHOR]

The global impact of coronavirus disease 2019 (COVID-19) is unprecedented, and many control and prevention measures have been implemented to test for and trace COVID-19. However, invisible-spreaders, who are associated with nucleic acid detection and asymptomatic infections, have received insufficient attention in the current COVID-19 control efforts. In this paper, we analyze the time series infection data for Italy, Germany, Brazil, India and Sweden since the first wave outbreak to address the following issues through a series of experiments. We conclude that: 1) As of June 1, 2020, the proportion of invisible-spreaders is close to 0.4% in Sweden, 0.8% in early Italy and Germany, and 0.4% in the middle and late stages. However, in Brazil and India, the proportion still shows a gradual upward trend; 2) During the spread of this pandemic, even a slight increase in the proportion of invisible-spreaders could have large implications for the health of the community; and 3) On resuming work, the pandemic intervention measures will be relaxed, and invisible-spreaders will cause a new round of outbreaks., Competing Interests: The authors have declared that no competing interests exist.

This paper studies the interplay between social distancing and the spread of the COVID-19 disease-a global pandemic that has affected most of the world's population. Our goals are to (1) to observe the correlation between the strictness of social distancing policies and the spread of disease and (2) to determine the optimal adoption level of social distancing policies. The earliest instances of the virus were found in China, and the virus has reached the United States with devastating consequences. Other countries severely affected by the pandemic are Brazil, Russia, the United Kingdom, Spain, India, Italy, and France. Although it is impossible to stop it, it is possible to slow down its spread to reduce its impact on the society and economy. Governments around the world have deployed various policies to reduce the virus spread in response to the pandemic. To assess the effectiveness of these policies, the system's dynamics of the society needs to be analyzed, which is generally not possible with mathematical linear equations or Monte Carlo methods because human society is a complex adaptive system with continuous feedback loops. Because of the challenges with the other methods, we chose agent-based methods to conduct our study. Moreover, recent agent-based modeling studies for the COVID-19 pandemic show significant promise in assisting decision-makers in managing the crisis by applying policies such as social distancing, disease testing, contact tracing, home isolation, emergency hospitalization, and travel prevention to reduce infection rates. Based on modeling studies conducted in Imperial College, increasing levels of interventions could slow the spread of disease and infection. We ran the model with six different percentages of social distancing while keeping the other parameters constant. The results show that social distancing affects the spread of COVID-19 significantly, in turn decreasing the spread of disease and infection rates when implemented at higher levels. We also validated these results by using the behavior space tool with ten experiments with varying social distancing levels. We conclude that applying and increasing social distancing policy levels leads to a significant reduction in infection spread and the number of deaths. Both experiments show that infection rates are reduced drastically when social distancing intervention is implemented between 80% to 100%.

Background: During the coronavirus disease 2019 (COVID-19) pandemic, in patients treated for SARS-CoV-2 infection, infections with the Klebsiella pneumoniae bacteria producing New Delhi metallo-B-lactamase (NDM) carbapenemase in the USA, Brazil, Mexico, and Italy were observed, especially in intensive care units (ICUs). This study aimed to assess the impact of Klebsiella pneumoniae NDM infection and other bacterial infections on mortality in patients treated in ICUs due to COVID-19. Methods: The 160 patients who qualified for the study were hospitalized in ICUs due to COVID-19. Three groups were distinguished: patients with COVID-19 infection only (N = 72), patients with COVID-19 infection and infection caused by Klebsiella pneumoniae NDM (N = 30), and patients with COVID-19 infection and infection of bacterial etiology other than Klebsiella pneumoniae NDM (N = 58). Mortality in the groups and chosen demographic data; biochemical parameters analyzed on days 1, 3, 5, and 7; comorbidities; and ICU scores were analyzed. Results: Bacterial infection, including with Klebsiella pneumoniae NDM type, did not elevate mortality rates. In the group of patients who survived the acute phase of COVID-19 the prolonged survival time was demonstrated: the median overall survival time was 13 days in the NDM bacterial infection group, 14 days in the other bacterial infection group, and 7 days in the COVID-19 only group. Comparing the COVID-19 with NDM infection and COVID-19 only groups, the adjusted model estimated a statistically significant hazard ratio of 0.28 (p = 0.002). Multivariate analysis revealed that age, APACHE II score, and CRP were predictors of mortality in all the patient groups. Conclusion: In patients treated for SARS-CoV-2 infection acquiring a bacterial infection due to prolonged hospitalization associated with the treatment of COVID-19 did not elevate mortality rates. The data suggests that in severe COVID-19 patients who survived beyond the first week of hospitalization, bacterial infections, particularly Klebsiella pneumoniae NDM, do not significantly impact mortality. Multivariate analysis revealed that age, APACHE II score, and CRP were predictors of mortality in all the patient groups. [ABSTRACT FROM AUTHOR]

This study introduces a new method for identifying COVID-19 infections using blood test data as part of an anomaly detection problem by combining the kernel principal component analysis (KPCA) and one-class support vector machine (OCSVM). This approach aims to differentiate healthy individuals from those infected with COVID-19 using blood test samples. The KPCA model is used to identify nonlinear patterns in the data, and the OCSVM is used to detect abnormal features. This approach is semi-supervised as it uses unlabeled data during training and only requires data from healthy cases. The method's performance was tested using two sets of blood test samples from hospitals in Brazil and Italy. Compared to other semi-supervised models, such as KPCA-based isolation forest (iForest), local outlier factor (LOF), elliptical envelope (EE) schemes, independent component analysis (ICA), and PCA-based OCSVM, the proposed KPCA-OSVM approach achieved enhanced discrimination performance for detecting potential COVID-19 infections. For the two COVID-19 blood test datasets that were considered, the proposed approach attained an AUC (area under the receiver operating characteristic curve) of 0.99, indicating a high accuracy level in distinguishing between positive and negative samples based on the test results. The study suggests that this approach is a promising solution for detecting COVID-19 infections without labeled data. [ABSTRACT FROM AUTHOR]