Clustering analysis of countries using the COVID-19 cases dataset.
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ABSTRACT:
SUBMITTER: Zarikas V
PROVIDER: S-EPMC7258836 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo rank and score 180 countries according to COVID-19 cases and fatality in 2020 and compare the results to existing pandemic vulnerability prediction models and results generated by standard epidemiological scoring techniques.SettingOne hundred and eighty countries' patients with COVID-19 and fatality data representing the healthcare system preparedness and performance in combating the pandemic in 2020.DesignUsing the retrospective daily COVID-19 data in 2020 broken into 24 half-month periods, we applied unsupervised machine learning techniques, in particular, hierarchical clustering analysis to cluster countries into five groups within each period according to their cumulative COVID-19 fatality per day over the year and cumulative COVID-19 cases per million population per day over the half-month period. We used the average of the period scores to assign countries' final scores for each measure.Primary outcomeThe primary outcomes are the COVID-19 cases and fatality grades in 2020.ResultsThe United Arab Emirates and the USA with F in COVID-19 cases, achieved A or B in the fatality scores. Belgium and Sweden ranked F in both scores. Although no African country ranked F for COVID-19 cases, several African countries such as Gambia and Liberia had F for fatality scores. More developing countries ranked D and F in fatality than in COVID-19 case rankings. The classic epidemiological measures such as averages and rates have a relatively good correlation with our methodology, but past predictions failed to forecast the COVID-19 countries' preparedness.ConclusionCOVID-19 fatality can be a good proxy for countries' resources and system's resilience in managing the pandemic. These findings suggest that countries' economic and sociopolitical factors may behave in a more complex way as were believed. To explore these complex epidemiological associations, models can benefit enormously by taking advantage of methods developed in computer science and machine learning.
| S-EPMC8578186 | biostudies-literature
Project description:Daily transcriptomic profiling was conducted on whole blood collected from COVID-19 cases. Whole blood was collected in Tempus Blood RNA tubes, and RNA was extracted from whole blood using the Tempus Spin RNA Isolation Kit. Healthy controls consisted of participants of a measles, mumps and rubella re-vaccination study. Pre-vaccination whole blood was collected and processed and analyzed as above.
2020-03-27 | E-MTAB-8871 | biostudies-arrayexpress
Project description:Background: The purpose of the study was to compare trends in the progression of COVID-19 among South Asian countries with more developed Western countries. Methods: COVID-19 data from South Asian countries were used for this observational study. Data were taken up to April 21, 2020 from the outbreak of the COVID-19. Four of the seven countries met the inclusion criteria and were included in the analysis. Results: An exponential increase in the average number of weekly cases was reported after the fifth week following the first case. The correlation between reported cases and tests was found to be strong and significant (r=0.90, p=0.037). However, on average, 315.25 tests per million population were performed, which was at least 12 times lower than the number of tests performed in countries with a large number of COVID-19 cases. Conclusions: At present, the number of confirmed cases from South Asia was found to be significantly lower than in Western countries. Hence, an increase in the strength of performing diagnostic tests is highly recommended. Strict measures are required to make the people of these countries follow the instructions of social distancing and comply with preventive measures.
| S-EPMC7905493 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2022-02-28 | GSE197259 | GEO
Project description:In this study, causalities of COVID-19 across a group of seventy countries are analyzed with effective transfer entropy. To reveal the causalities, a weighted directed network is constructed. In this network, the weights of the links reveal the strength of the causality which is obtained by calculating effective transfer entropies. Transfer entropy has some advantages over other causality evaluation methods. Firstly, transfer entropy can quantify the strength of the causality and secondly it can detect nonlinear causal relationships. After the construction of the causality network, it is analyzed with well-known network analysis methods such as eigenvector centrality, PageRank, and community detection. Eigenvector centrality and PageRank metrics reveal the importance and the centrality of each node country in the network. In community detection, node countries in the network are divided into groups such that countries in each group are much more densely connected.
| S-EPMC9407067 | biostudies-literature
Project description:BackgroundRecurring COVID-19 waves highlight the need for tools able to quantify transmission risk, and identify geographical areas at risk of outbreaks. Local outbreak risk depends on complex immunity patterns resulting from previous infections, vaccination, waning and immune escape, alongside other factors (population density, social contact patterns). Immunity patterns are spatially and demographically heterogeneous, and are challenging to capture in country-level forecast models.MethodsWe used a spatiotemporal regression model to forecast subnational case and death counts and applied it to three EU countries as test cases: France, Czechia, and Italy. Cases in local regions arise from importations or local transmission. Our model produces age-stratified forecasts given age-stratified data, and links reported case counts to routinely collected covariates (e.g. test number, vaccine coverage). We assessed the predictive performance of our model up to four weeks ahead using proper scoring rules and compared it to the European COVID-19 Forecast Hub ensemble model. Using simulations, we evaluated the impact of variations in transmission on the forecasts. We developed an open-source RShiny App to visualise the forecasts and scenarios.ResultsAt a national level, the median relative difference between our median weekly case forecasts and the data up to four weeks ahead was 25% (IQR: 12-50%) over the prediction period. The accuracy decreased as the forecast horizon increased (on average 24% increase in the median ranked probability score per added week), while the accuracy of death forecasts was more stable. Beyond two weeks, the model generated a narrow range of likely transmission dynamics. The median national case forecasts showed similar accuracy to forecasts from the European COVID-19 Forecast Hub ensemble model, but the prediction interval was narrower in our model. Generating forecasts under alternative transmission scenarios was therefore key to capturing the range of possible short-term transmission dynamics.DiscussionOur model captures changes in local COVID-19 outbreak dynamics, and enables quantification of short-term transmission risk at a subnational level. The outputs of the model improve our ability to identify areas where outbreaks are most likely, and are available to a wide range of public health professionals through the Shiny App we developed.
| S-EPMC11361242 | biostudies-literature
Project description:Daily transcriptomic profiling was conducted on whole blood collected from COVID-19 cases. Whole blood was collected in Tempus Blood RNA tubes, and RNA was extracted from whole blood using the Tempus Spin RNA Isolation Kit. Healthy controls consisted of participants of a measles, mumps and rubella re-vaccination study. Pre-vaccination whole blood was collected and processed and analyzed as above.
2021-02-24 | E-MTAB-9719 | biostudies-arrayexpress
Project description:The article represents coronavirus spread log history. The duration, coronavirus takes to spread from one country to another country, could be seen in this dataset and could predicted the same for future pandemics through this dataset. It is highly dependent on the cabalistic number of variables that is the main navel of these datasets. Information for this dataset is collected from trusted websites, local and international popular newspapers. This coronavirus dataset not only help to track the spreading route of coronavirus but also can be used for predicting the possible spreading route of similar future pandemics. This dataset consists of 186 countries' useful data related to COVID-19 pandemic from November 17, 2019, to May 16, 2020, with 8 unique variables that provide the information of the nature of the spread of COVID-19. The datasets mainly focus on two major fields, first one is First Case which consists of information of Date of First Case(s), Number of confirm Case(s) at First Day, Age of the patient(s) of First Case, Last Visited Country and the other one First Death information consist of Date of First Death and Age of the Patient who died first for every Country mentioning corresponding Continent.This dataset also can perform a bunch of predictions using Machine Learning applications, like -how fast the virus is spreading, affect rate, death rate, death rate and able to represent comparison between other pandemics. Using this dataset, any similar pandemic spreadness could be predicted earlier and necessary precaution measures could be taken.
| S-EPMC7430301 | biostudies-literature
Project description:Infections caused by SARS-CoV-2 may cause a severe disease, termed COVID-19, with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms, and their modulation is the only therapeutic strategy that has shown a mortality benefit. Herein, we used peripheral blood transcriptomes of critically-ill COVID-19 patients obtained at admission in an Intensive Care Unit, to identify two clusters that, in spite of no major clinical differences, have different gene expression profiles that reveal different underlying pathogenetic mechanisms and ultimately have different ICU outcome. A transcriptomic signature was used to identify these clusters in an external validation cohort, yielding a similar result. These results illustrate the potential of transcriptomic profiles to identify patient endotypes and point to relevant pathogenetic mechanisms in COVID-19.
2023-04-29 | GSE206264 | GEO
Project description:Comparing the impact of the COVID-19 pandemic between countries or across time is difficult because the reported numbers of cases and deaths can be strongly affected by testing capacity and reporting policy. Excess mortality, defined as the increase in all-cause mortality relative to the expected mortality, is widely considered as a more objective indicator of the COVID-19 death toll. However, there has been no global, frequently-updated repository of the all-cause mortality data across countries. To fill this gap, we have collected weekly, monthly, or quarterly all-cause mortality data from 94 countries and territories, openly available as the regularly-updated World Mortality Dataset. We used this dataset to compute the excess mortality in each country during the COVID-19 pandemic. We found that in several worst-affected countries (Peru, Ecuador, Bolivia, Mexico) the excess mortality was above 50% of the expected annual mortality. At the same time, in several other countries (Australia, New Zealand) mortality during the pandemic was below the usual level, presumably due to social distancing measures decreasing the non-COVID infectious mortality. Furthermore, we found that while many countries have been reporting the COVID-19 deaths very accurately, some countries have been substantially underreporting their COVID-19 deaths (e.g. Nicaragua, Russia, Uzbekistan), sometimes by two orders of magnitude (Tajikistan). Our results highlight the importance of open and rapid all-cause mortality reporting for pandemic monitoring.
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