Predicting COVID-19 in very large countries: The case of Brazil.
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ABSTRACT:
SUBMITTER: Parro VC
PROVIDER: S-EPMC8248665 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:COVID-19 pandemic has changed several aspects in human behaviour, whereas population mobility patterns have been strongly affected by social distancing. In parallel, changes in solid waste generation patterns have been reported worldwide. This work assessed the influence of the COVID-19 pandemic on waste generation and collection in São Paulo city, Brazil, the largest city of Latin America. Data on nine types of wastes collected between 2013 and 2021 were obtained, and the quantities of wastes collected before and during the pandemic were compared. These data were also discussed in light of data on COVID-19 cases and rates of social distancing and mobility. An increase in the amount of recyclables collected during the first wave of COVID-19 (March to September 2020) was observed. Decreases in the quantities of construction, demolition and bulky wastes (first wave of COVID-19) and farmers market wastes (second wave - October 2020 to February 2021) were also evidenced. The quantities of medical wastes collected during the pandemic significantly increased. The amount of residential wastes was lower than the mean for the pre-pandemic period during the first months of COVID-19. Thus, changes in lifestyle and consumption patterns of the population of São Paulo city seem to have affected solid waste generation during the pandemic, which reinforces the need of implementing solid waste management policies based on a diagnostic that characterise and consider these changes.
| S-EPMC10247687 | biostudies-literature
Project description:Coronavirus (COVID-19) has continued to be a global threat to public health. As the matter of fact, it needs unreserved effort to monitor the prevalence of the virus. However, applying an effective prediction of the prevalence is thought to be the fundamental requirement to effectively control the spreading rate. Time series models have extensively been considered as the convenient methods to predict the prevalence or spreading rate of the disease. This study, therefore, aimed to apply the Autoregressive Integrated Moving Average (ARIMA) modeling approach for projecting coronavirus (COVID-19) prevalence patterns in East Africa Countries, mainly Ethiopia, Djibouti, Sudan and Somalia. The data for the study were obtained from the reports of confirmed COVID-19 cases by the official website of Johns Hopkins University from 13th March, 2020 to 30th June, 2020.The results of the study, then, showed that in the coming four month, the number of COVID-19 positive people in Ethiopia may reach up to 56,610 from 5,846 on June 30, 2020 in average-rate scenario. However, in worst case scenario forecast, the model showed that the cases will be around 84,497. The analysis further depicted that with average interventions and control scenario, cumulative number of infected persons in Djibouti, Somalia and Sudan will increase from 4,656, 2,904 and 9,258 respectively at the end of June to 8,336, 3,961 and 21,388, which is by the end of October, 2020, after four-months. But, with insufficient intervention, the number of infected persons may grow quickly and reach up to 14,072, 10,037 and 38,174 in Djibouti, Somalia and Sudan respectively. Generally, the extent of the coronavirus spreading was increased from time to time in the past four month, until 30 th June, 2020, and it is expected to continue quicker than before for the coming 4-month, until the end of October, 2020, in Ethiopia, Djibouti, Somalia, and Sudan and more rapidly than before in Sudan and Ethiopia, while the peak will remain unknown yet. Therefore, an effective implementation of the preventive measures and a rigorous compliance by avoiding negligence with the rules such as prohibiting public gatherings, travel restrictions, personal protection measures, and social distancing may alleviate the spreading rates of the virus, particularly, Sudan and Ethiopia. Moreover, more efforts should be exerted on Ethiopian side to control the population movement across all the border areas and to strengthen border quarantining. Further, through updating more new data with continuous reconsideration of predictive model, provide useful and more precise prediction. Applying, ARIMAX-Transfer Function model in region-wise by take in to consideration of climatic data like temperature and humidity in each countries looking spatial pattern for reliable measure of COVID-19 prevalence.
| S-EPMC7434383 | biostudies-literature
Project description:Brazil is among the countries which have faced two devastating infection waves of COVID-19 in the past year. Despite the fact the country has one of the world's leading immunization programs, Brazil only slowly established a national COVID-19 vaccination strategy and campaign. This case study is based on an integrative review of primary and secondary literature sources. Different search strategies on Medline and Google Scholar were performed for the case presentation, for the management and outcome of the COVID-19 outbreak and for the state of the COVID-19 vaccination program. Official documents from the Brazilian Ministry of Health, the website of the World Health Organization and pharmaceutical companies were also reviewed. Searches were limited to English, French, German, Portuguese and Spanish. This article describes the Brazilian COVID-19 vaccination campaign and the drivers and barriers to its implementation; and evaluates further investigations needed to have a conclusive overview over the constantly evolving situation. Healthcare inequalities, which were widened during the pandemic, a lack of coordination at the federal level, the absence of federal government support for scientific research and the lack of endorsement and commitment to the mitigation of the COVID-19 pandemic set the country's COVID-19 vaccination campaign off to a challenging start. However, Brazil had a well-developed primary care system and national vaccination program prior to the pandemic, which are both important facilitators. At the time of writing, six vaccines are currently available in the country, and the program is advancing. The scientific community needs to continue to investigate the country's vaccination strategy and its implementation to make sure that maximum effort is undertaken for the health of the Brazilian population.
| S-EPMC9620893 | biostudies-literature
Project description:IntroductionThe COVID-19 pandemic continues, with a late hyperinflammatory phase. The immunosuppressive therapy used in heart transplant patients, in theory, could reduce inflammation, thus benefitting patients with COVID-19. So far, however, there is still very little literature on this subject.MethodsThis is a single-center retrospective study. We described laboratory parameters and clinical outcomes from 11 heart transplant patients with COVID-19 assisted at Dante Pazzanese Institute of Cardiology between March and July 2020.ResultsPatients with ages of between 35 and 79 years were enrolled, and heart transplantation ranged from 3 to 264 months. The main comorbidities were diabetes mellitus (9/11; 81.8%), hypertension (10/11; 90.9%), and chronic renal disease (6/11; 54.5%). Cyclosporine A was used in 10 (90.9%) patients, mycophenolate mofetil in 9 (81.8%) patients, and mTOR inhibitor in 5 (45.5%) patients. Fever and cough were observed in 8 (72.7%) patients, and dyspnea and gastrointestinal symptoms in 5 (45.5%) patients. Lymphopenia was observed in 10 (90.9%) patients and thrombocytopenia in 5 (45.5%) patients. The higher level of troponin associated with chest tomography above 50% of bilateral pulmonary infiltrates with ground-glass opacity (GGO) was observed in those with the worst outcomes. Nine patients needed intensive care, and hospital stay ranged from 4 to 21 days, with 2 (18.2%) patients requiring vasopressor drugs and mechanical ventilation, and three (27.3%) patients dying due to COVID-19 complications.ConclusionHeart transplant patients had similar symptoms and outcomes as the general population; immunosuppressive therapy seems not to have protected them. Patients who presented higher levels of troponin and D-dimer, associated with greater GGO pulmonary infiltrates, had worse outcomes. More studies with larger cohorts may clarify immunosuppressive effects on COVID-19 outcomes.
| S-EPMC8209934 | 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:An outbreak of coronavirus disease began in a large penitentiary complex in Brazil on April 1, 2020. By June 12, there were 1,057 confirmed cases among inmates and staff. Nine patients were hospitalized, and 3 died. Mean serial interval was ≈2.5 days; reproduction number range was 1.0-2.3.
| S-EPMC7920649 | biostudies-literature
Project description:IntroductionCoronavirus disease (COVID-19) rapidly spread from Wuhan, China to other parts of China and other regions/countries around the world, resulting in a pandemic due to large populations moving through the massive transport hubs connecting all regions of China via railways and a major international airport. COVID-19 will remain a threat until safe and effective vaccines and antiviral drugs have been developed, distributed, and administered on a global scale. Thus, there is urgent need to establish effective implementation of preemptive non-pharmaceutical interventions for appropriate prevention and control strategies, and predicting future COVID-19 cases is required to monitor and control the issue.MethodsThis study attempts to utilize a three-layer graph convolutional network (GCN) model to predict future COVID-19 cases in 190 regions and countries using COVID-19 case data, commercial flight route data, and digital maps of public transportation in terms of transnational human mobility. We compared the performance of the proposed GCN model to a multilayer perceptron (MLP) model on a dataset of COVID-19 cases (excluding the graph representation). The prediction performance of the models was evaluated using the mean squared error.ResultsOur results demonstrate that the proposed GCN model can achieve better graph utilization and performance compared to the baseline in terms of both prediction accuracy and stability.DiscussionThe proposed GCN model is a useful means to predict COVID-19 cases at regional and national levels. Such predictions can be used to facilitate public health solutions in public health responses to the COVID-19 pandemic using deep learning and data pooling. In addition, the proposed GCN model may help public health policymakers in decision making in terms of epidemic prevention and control strategies.
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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
Project description:Using RNA-seq and high-resolution mass spectrometry we performed a comprehensive systems analysis on 128 plasma and leukocyte samples from hospitalized patients with or without COVID-19 (n=102 and 26 respectively) and with differing degrees of disease severity. We generated abundance measurements for over 17,000 transcripts, proteins, metabolites, and lipids and compiled them with clinical data into a curated relational database. This resource offers the unique opportunity to perform systems analysis and cross-ome correlations to both molecules and patient outcomes. In total 219 molecular features were mapped with high significance to COVID-19 status and severity, including those involved in processes such as complement system activation, dysregulated lipid transport, and B cell activation. In one example, we detected a trio of covarying molecules – citrate, plasmenyl-phosphatidylcholines, and gelsolin (GSN) – that offer both pathophysiological insight and potential novel therapeutic targets. Further, our data revealed in some cases, and supported in others, that several biological processes were dysregulated in COVID-19 patients including vessel damage, platelet activation and degranulation, blood coagulation, and acute phase response. For example, we observed that the coagulation-related protein, cellular fibronectin (cFN), was highly increased within COVID-19 patients and provide new evidence that the upregulated proteoform stems from endothelial cells, consistent with endothelial injury as a major activator of the coagulation cascade. The abundance of prothrombin, which is cleaved to form thrombin during clotting, was significantly reduced and correlated with severity and might help to explain the hyper coagulative environment of SARS-CoV-2 infection. From transcriptomic analysis of leukocytes, we concluded that COVID-19 patients with acute respiratory distress syndrome (ARDS) demonstrated a phenotype that overlapped with, but was distinct from, that found in patients with non-COVID-19-ARDS. To aid in the global efforts toward elucidation of disease pathophysiology and therapeutic development, we created a web-based tool with interactive visualizations allowing for easy navigation of this systems-level compendium of biomolecule abundance in relation to COVID-19 status and severity. Finally, we leveraged these multi-omic data to predict COVID-19 patient outcomes with machine learning, which highlighted the predictive power of these expansive molecular measurements beyond the standardized clinical estimate of 10-year survival Charlson score.
2020-08-29 | GSE157103 | GEO
Project description:The SARS-CoV-2 outbreak started on December 2019 in China and rapidly spread worldwide. Clinical manifestations of Coronavirus-disease 2019 (COVID-19) vary broadly, ranging from asymptomatic infection to acute respiratory failure and death, yet the underlying mechanisms and predictive biomarkers for this high variability are still unknown. Emerging evidence has shown that circulating extracellular vesicles (EVs) and extracellular RNAs (exRNAs) are functionally involved in a number of physiologic and pathologic processes. To test the hypothesis that these extracellular components are a key determinant of severity in COVID-19, we collected 31 serum samples from mild COVID-19 patients at admission in single center. After standard therapy without corticosteroids, 9 of 31 patients became severe COVID-19. We analyzed exRNA profiles from the 31 serums and 10 healthy controls for predicting COVID-19 severity value.
2022-03-31 | GSE158877 | GEO