Dataset Information

Data based model for predicting COVID-19 morbidity and mortality in metropolis.

ABSTRACT: There is an ongoing need for scientific analysis to help governments and public health authorities make decisions regarding the COVID-19 pandemic. This article presents a methodology based on data mining that can offer support for coping with epidemic diseases. The methodological approach was applied in São Paulo, Rio de Janeiro and Manaus, the cities in Brazil with the most COVID-19 deaths until the first half of 2021. We aimed to predict the evolution of COVID-19 in metropolises and identify air quality and meteorological variables correlated with confirmed cases and deaths. The statistical analyses indicated the most important explanatory environmental variables, while the cluster analyses showed the potential best input variables for the forecasting models. The forecast models were built by two different algorithms and their results have been compared. The relationship between epidemiological and environmental variables was particular to each of the three cities studied. Low solar radiation periods predicted in Manaus can guide managers to likely increase deaths due to COVID-19. In São Paulo, an increase in the mortality rate can be indicated by drought periods. The developed models can predict new cases and deaths by COVID-19 in studied cities. Furthermore, the methodological approach can be applied in other cities and for other epidemic diseases.

SUBMITTER: Barcellos DDS

PROVIDER: S-EPMC8716530 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Data based model for predicting COVID-19 morbidity and mortality in metropolis.

Barcellos Demian da Silveira DDS Fernandes Giovane Matheus Kayser GMK de Souza Fábio Teodoro FT

Scientific reports 20211229 1

There is an ongoing need for scientific analysis to help governments and public health authorities make decisions regarding the COVID-19 pandemic. This article presents a methodology based on data mining that can offer support for coping with epidemic diseases. The methodological approach was applied in São Paulo, Rio de Janeiro and Manaus, the cities in Brazil with the most COVID-19 deaths until the first half of 2021. We aimed to predict the evolution of COVID-19 in metropolises and identify a ...[more]

PMID: 34966184

Similar Datasets

Project description:Background: Early risk stratification tools for COVID-19 patients have been indicated yet there are few data about their ability to effectively detect clinical deterioration among COVID-19 patients. Objectives: To evaluate the NEWS-2 to predict severe morbidity and mortality for COVID-19 patients admitted to hospitals. Methods: We conducted a prospective cohort study among adult COVID-19 patients with a confirmed diagnosis who were admitted to the inpatient units at COVID-19 Martyrs Medical Military Complex Hospital, from 1 March 2022, until 29 February 2023. NEWS-2 scores were measured at admission and 6, 12, 24, and 48 h after their admission to the hospital using receiver operating characteristic (ROC) curves. Results: Overall, 192 adult COVID-19 patients aged 25-94 years (mean = 62.1, SD = 13.9) were enrolled. Of those, 49.0% were males, 47.4% were vaccinated, and 53.6% had diabetes. The 192 enrolled patients were classified into NEWS-2 score categories, with almost 13% (12.5%) falling into the high-risk category already upon admission. The mean NEWS-2 scores were excellent predictors of mechanical ventilation, admission to the ICU, and mortality, as indicated by an AUROC of 0.94 (95% CI: 0.88-1.00, p < 0.001), 0.91 (95% CI: 0.87-0.96, p < 0.001), and 0.96 (95% CI: 0.92-1.00, p < 0.001), respectively. Significant differences in mean NEWS-2 scores were found between the participating patients, both with and without comorbidity in the course of the patient's stay in the ICU, and mortality (p = 0.004, p = 0.043, respectively). Positive correlations of the high NEWS-2 scores were revealed using a multiple linear regression model, indicating the necessity of administering non-invasive ventilatory assistance (p = 0.013), hospitalization for a minimum of six days (p = 0.013), and admission to the ICU (p = 0.006). Nonetheless, there was a negative association between mortality and the NEWS-2 score (p < 0.001). Conclusions: The NEWS-2 had moderate sensitivity and specificity in predicting the deterioration of patients with COVID-19 whereas there was high sensitivity and specificity in predicting the mortality for patients with COVID-19, both with and without comorbidity. Our findings support the utility of NEWS-2 monitoring as a sensitive approach for initially assessing COVID-19 patients. It could be helpful to enhance the accuracy of predictive performance by supplementing the score parameters by adding biological parameters in addition to clinical judgment.

Project description:BackgroundIn late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19.ObjectiveThe aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease.MethodsIn this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves.ResultsA total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters (P<.05), and LASSO regression analysis screened out 10 risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were age (OR 1.035, 95% CI 1.017-1.054; P<.001), CK level (OR 1.002, 95% CI 1.0003-1.0039; P=.02), CD4 count (OR 0.995, 95% CI 0.992-0.998; P=.002), CD8 % (OR 1.007, 95% CI 1.004-1.012, P<.001), CD8 count (OR 0.881, 95% CI 0.835-0.931; P<.001), and C3 count (OR 6.93, 95% CI 1.945-24.691; P=.003). The areas under the curve of the prediction model for 0.5-week, 1-week, 2-week and 3-week nonsevere probability were 0.721, 0.742, 0.87, and 0.832, respectively. The calibration curves showed that the model had good prediction ability within three weeks of disease onset.ConclusionsThis study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening.

Project description:BackgroundCOVID-19 rapidly spread around the world, putting health systems under unprecedented pressure and continuous adaptations. Well-established health information systems (HIS) are crucial in providing data to allow evidence-based policymaking and public health interventions in the pandemic response. This study aimed to compare morbidity information between two databases for COVID-19 management in Portugal and identify potential complementarities.MethodsThis is an observational study using records from both COVID-19 cases surveillance (National Epidemiological Surveillance System; SINAVE) and related deaths (National e-Death Certificates Information System; SICO) systems, which were matched on sex, age, municipality of residence and date of death. After the linkage, morbidity reported in SINAVE and identified in SICO, through the application of Charlson and Elixhauser comorbidity indexes algorithms, were compared to evaluate agreement level.ResultsOverall, 2285 matched cases were analyzed, including 53.9% males with a median age of 84 years. According to the method of data reporting assessment, the presence of any morbidity ranged between 26.3% and 62.5%. The reporting of ten morbidities could be compared between the information reported in SINAVE and SICO databases. The proportion of simultaneous reporting in both databases ranged between 5.7% for diabetes and 0.0% for human immunodeficiency virus infection or coagulopathy. Minimal or no agreement was found when assessing the similarity of the morbidity reporting in both databases, with neoplasms showing the highest level of agreement (0.352, 95% IC: 0.277-0.428; p < 0.001).ConclusionDifferent information about reported morbidity could be found in two HIS used to monitor COVID-19 cases and related deaths, as data are independently collected. These results show that the interoperability of SICO and SINAVE databases would potentially improve available HIS and improve available information to decision-making and address COVID-19 pandemic management.

Dataset Information

Data based model for predicting COVID-19 morbidity and mortality in metropolis.

Publications

Data based model for predicting COVID-19 morbidity and mortality in metropolis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets