Principal component analysis to study the relations between the spread rates of COVID-19 in high risks countries
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ABSTRACT:
SUBMITTER: Mahmoudi M
PROVIDER: S-EPMC7489989 | biostudies-literature | 2020 Sep
REPOSITORIES: biostudies-literature
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Project description:The numbers of confirmed cases of new coronavirus (Covid-19) are increased daily in different countries. To determine the policies and plans, the study of the relations between the distributions of the spread of this virus in other countries is critical. In this work, the distributions of the spread of Covid-19 in Unites States America, Spain, Italy, Germany, United Kingdom, France, and Iran were compared and clustered using fuzzy clustering technique. At first, the time series of Covid-19 datasets in selected countries were considered. Then, the relation between spread of Covid-19 and population's size was studied using Pearson correlation. The effect of the population's size was eliminated by rescaling the Covid-19 datasets based on the population's size of USA. Finally, the rescaled Covid-19 datasets of the countries were clustered using fuzzy clustering. The results of Pearson correlation indicated that there were positive and significant between total confirmed cases, total dead cases and population's size of the countries. The clustering results indicated that the distribution of spreading in Spain and Italy was approximately similar and differed from other countries.
| S-EPMC7442906 | biostudies-literature
Project description:RNA genomes from coronavirus have a length as long as 32 kilobases, and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the outbreak of coronavirus disease 2019 (COVID-19) pandemic has long sequences which made the analysis difficult. Over 20,000 sequences have been submitted to GISAID, and the number is growing fast each day which increased the difficulties in data analysis; however, genome sequence analysis is critical in understanding the COVID-19 and preventing the spread of the disease. In this study, a principal component analysis (PCA) was applied to the aligned large size genome sequences and the numerical numbers were converted from the letters using a published method designed for protein sequence cluster analysis. The study initialized with a shortlist sequence testing, and the PCA score plot showed high tolerance with low-quality data, and the major virus sequences from humans were separated from the pangolin and bat samples. Our study also successfully built a model for a large number of sequences with more than 20,000 sequences which indicate the potential mutation directions for the COVID-19 which can be served as a pretreatment method for detailed studies such as decision tree-based methods. In summary, our study provided a fast tool to analyze the high-volume genome sequences such as the COVID-19 and successfully applied to more than 20,000 sequences which may provide mutation direction information for COVID-19 studies.
| S-EPMC7804214 | biostudies-literature
Project description:The COVID-19 is one of the worst pandemics in modern history. We applied principal component analysis (PCA) to the daily time series of the COVID-19 death cases and confirmed cases for the top 25 countries from April of 2020 to February of 2021. We calculated the eigenvalues and eigenvectors of the cross-correlation matrix of the changes in daily accumulated data over monthly time windows. The largest eigenvalue describes the overall evolution dynamics of the COVID-19 and indicates that evolution was faster in April of 2020 than in any other period. By using the first two PC coefficients, we can identify the group dynamics of the COVID-19 evolution. We observed groups under critical states in the loading plot and found that American and European countries are represented by strong clusters in the loading plot. The first PC plays an important role and the correlations (C1) between the normalized logarithmic changes in deaths or confirmed cases and the first PCs may be used as indicators of different phases of the COVID-19. By varying C1 over time, we identified different phases of the COVID-19 in the analyzed countries over the target time period.
| S-EPMC8638895 | biostudies-literature
Project description: Not available
| S-EPMC7834364 | biostudies-literature
Project description:Background: COVID-19 has been quickly spreading, making it a serious public health threat. It is important to identify phenotypes to predict the severity of disease and design an individualized treatment. Methods: We collected data from 213 COVID-19 patients in Wuhan Pulmonary Hospital from January 1 to March 30, 2020. Principal component analysis (PCA) and cluster analysis were used to classify patients. Results: We identified three distinct subgroups of COVID-19. Cluster 1 was the largest group (52.6%) and characterized by oldest age, lowest cellular immune function, and albumin levels. 38.5% of subjects were grouped into Cluster 2. Most of the lab results in Cluster 2 fell between those of Clusters 1 and 3. Cluster 3 was the smallest cluster (8.9%), characterized by youngest age and highest cellular immune function. The incidence of respiratory failure, acute respiratory distress syndrome (ARDS), heart failure, and usage of non-invasive mechanical ventilation in Cluster 1 was significantly higher than others (P < 0.05). Cluster 1 had the highest death rate of 30.4% (P = 0.005). Although there were significant differences in age between Clusters 2 and 3 (P < 0.001), we found that there was no difference in demand for medical resources. Conclusions: We identified three distinct clusters of the COVID-19 patients. The results show that age alone could not be used to assess a patient's condition. Specifically, management of albumin, and immune function are important in reducing the severity of disease.
| S-EPMC7690648 | biostudies-literature
Project description:BackgroundThe emergence of a novel coronavirus (SARS-CoV-2) in Wuhan, China, at the end of 2019 has caused widespread transmission around the world. As new epicentres in Europe and America have arisen, of particular concern is the increased number of imported coronavirus disease 2019 (COVID-19) cases in Africa, where the impact of the pandemic could be more severe. We aim to estimate the number of COVID-19 cases imported from 12 major epicentres in Europe and America to each African country, as well as the probability of reaching 10,000 cases in total by the end of March, April, May, and June following viral introduction.MethodsWe used the reported number of cases imported from the 12 major epicentres in Europe and America to Singapore, as well as flight data, to estimate the number of imported cases in each African country. Under the assumption that Singapore has detected all the imported cases, the estimates for Africa were thus conservative. We then propagated the uncertainty in the imported case count estimates to simulate the onward spread of the virus, until 10,000 cases are reached or the end of June, whichever is earlier. Specifically, 1,000 simulations were run separately under four different combinations of parameter values to test the sensitivity of our results.ResultsWe estimated Morocco, Algeria, South Africa, Egypt, Tunisia, and Nigeria as having the largest number of COVID-19 cases imported from the 12 major epicentres. Based on our 1,000 simulation runs, Morocco and Algeria's estimated probability of reaching 10,000 cases by end of March was close to 100% under all scenarios. In particular, we identified countries with less than 1,000 cases in total reported by end of June whilst the estimated probability of reaching 10,000 cases by then was higher than 50% even under the most optimistic scenario.ConclusionsOur study highlights particular countries that are likely to reach (or have reached) 10,000 cases far earlier than the reported data suggest, calling for the prioritization of resources to mitigate the further spread of the epidemic.
| S-EPMC7424562 | biostudies-literature
Project description:We investigate the effect of social capital on health outcomes during the Covid-19 pandemic in independent analyses for Austria, Germany, Great Britain, Italy, the Netherlands, Sweden and Switzerland. Exploiting detailed geographical variation within countries, we show that a one-standard-deviation increase in social capital leads to between 14% and 34% fewer Covid-19 cases per capita accumulated from mid-March until end of June 2020, as well as between 6% and 35% fewer excess deaths per capita. Our results highlight the positive health returns of strengthening social capital.
| S-EPMC8520571 | biostudies-literature
Project description:Causal mediation analysis aims to quantify the intermediate effect of a mediator on the causal pathway from treatment to outcome. When dealing with multiple mediators, which are potentially causally dependent, the possible decomposition of pathway effects grows exponentially with the number of mediators. An existing approach incorporated the principal component analysis (PCA) to address this challenge based on the fact that the transformed mediators are conditionally independent given the orthogonality of the principal components (PCs). However, the transformed mediator PCs, which are linear combinations of original mediators, can be difficult to interpret. A sparse high-dimensional mediation analysis approach is proposed which adopts the sparse PCA method to the mediation setting. The proposed approach is applied to a task-based functional magnetic resonance imaging study, illustrating its ability to detect biologically meaningful results related to an identified mediator.
| S-EPMC7449232 | biostudies-literature
Project description:Objectives: Headache is a common symptom in systemic infections, and one of the symptoms of the novel coronavirus disease 2019 (COVID-19). The objective of this study was to characterize the phenotype of COVID-19 headache via machine learning. Methods: We performed a cross-sectional study nested in a retrospective cohort. Hospitalized patients with COVID-19 confirmed diagnosis who described headache were included in the study. Generalized Linear Models and Principal Component Analysis were employed to detect associations between intensity and self-reported disability caused by headache, quality and topography of headache, migraine features, COVID-19 symptoms, and results from laboratory tests. Results: One hundred and six patients were included in the study, with a mean age of 56.6 ± 11.2, including 68 (64.2%) females. Higher intensity and/or disability caused by headache were associated with female sex, fever, abnormal platelet count and leukocytosis, as well as migraine symptoms such as aggravation by physical activity, pulsating pain, and simultaneous photophobia and phonophobia. Pain in the frontal area (83.0% of the sample), pulsating quality, higher intensity of pain, and presence of nausea were related to lymphopenia. Pressing pain and lack of aggravation by routine physical activity were linked to low C-reactive protein and procalcitonin levels. Conclusion: Intensity and disability caused by headache attributed to COVID-19 are associated with the disease state and symptoms. Two distinct headache phenotypes were observed in relation with COVID-19 status. One phenotype seems to associate migraine symptoms with hematologic and inflammatory biomarkers of severe COVID-19; while another phenotype would link tension-type headache symptoms to milder COVID-19.
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Project description:The crude case fatality rate (CFR), because of the calculation method, is the most accurate when the pandemic is over since there is a possibility of the delay between disease onset and outcomes. Adjusted crude CFR measures can better explain the pandemic situation by improving the CFR estimation. However, no study has thoroughly investigated the COVID-19 adjusted CFR of the South Asian Association For Regional Cooperation (SAARC) countries. This study estimated both survival interval and underreporting adjusted CFR of COVID-19 for these countries. Moreover, we assessed the crude CFR between genders and across age groups and observed the CFR changes due to the imposition of fees on COVID-19 tests in Bangladesh. Using the daily records up to October 9, we implemented a statistical method to remove the delay between disease onset and outcome bias, and due to asymptomatic or mild symptomatic cases, reporting rates lower than 50% (95% CI: 10%-50%) bias in crude CFR. We found that Afghanistan had the highest CFR, followed by Pakistan, India, Bangladesh, Nepal, Maldives, and Sri Lanka. Our estimated crude CFR varied from 3.708% to 0.290%, survival interval adjusted CFR varied from 3.767% to 0.296% and further underreporting adjusted CFR varied from 1.096% to 0.083%. Furthermore, the crude CFRs for men were significantly higher than that of women in Afghanistan (4.034% vs. 2.992%) and Bangladesh (1.739% vs. 1.337%) whereas the opposite was observed in Maldives (0.284% vs. 0.390%), Nepal (0.006% vs. 0.007%), and Pakistan (2.057% vs. 2.080%). Besides, older age groups had higher risks of death. Moreover, crude CFR increased from 1.261% to 1.572% after imposing the COVID-19 test fees in Bangladesh. Therefore, the authorities of countries with higher CFR should be looking for strategic counsel from the countries with lower CFR to equip themselves with the necessary knowledge to combat the pandemic. Moreover, caution is needed to report the CFR.
| S-EPMC7967300 | biostudies-literature