Search

The new pandemic caused by the COVID-19 virus has generated an overload in the quality of medical care in clinical centers around the world. Causes that originate this fact include lack of medical personnel, infrastructure, medicines, among others. The rapid and exponential increase in the number of patients infected by COVID-19 has required an efficient and speedy prediction of possible infections and their consequences with the purpose of reducing the health care quality overload. Therefore, intelligent models are developed and employed to support medical personnel, allowing them to give a more effective diagnosis about the health status of patients infected by COVID-19. This paper aims to propose an alternative algorithmic analysis for predicting the health status of patients infected with COVID-19 in Mexico. Different prediction models such as KNN, logistic regression, random forests, ANN and majority vote were evaluated and compared. The models use risk factors as variables to predict the mortality of patients from COVID-19. The most successful scheme is the proposed ANN-based model, which obtained an accuracy of 90% and an F1 score of 89.64%. Data analysis reveals that pneumonia, advanced age and intubation requirement are the risk factors with the greatest influence on death caused by virus in Mexico. [ABSTRACT FROM AUTHOR]

The implementation and validation of anti-SARS-CoV-2 IgG serological assays are reported in this paper. S1 and RBD proteins were used to coat ELISA plates, and several secondary antibodies served as reporters. The assays were initially validated with 50 RT-PCR positive COVID-19 sera, which showed high IgG titers of mainly IgG1 isotype, followed by IgG3. Low or no IgG2 and IgG4 titers were detected. Then, the RBD/IgG assay was further validated with 887 serum samples from RT-PCR positive COVID-19 individuals collected at different times, including 7, 14, 21, and 40 days after the onset of symptoms. Most of the sera were IgG positive at day 40, with seroconversion happening after 14–21 days. A third party conducted an additional performance test of the RBD/IgG assay with 406 sera, including 149 RT-PCR positive COVID-19 samples, 229 RT-PCR negative COVID-19 individuals, and 28 sera from individuals with other viral infections not related to SARS-CoV-2. The sensitivity of the assay was 99.33%, with a specificity of 97.82%. All the sera collected from individuals with infectious diseases other than COVID-19 were negative. Given the robustness of this RBD/IgG assay, it received approval from the sanitary authority in Mexico (COFEPRIS) for production and commercialization under the name UDISTEST-V2G®. [ABSTRACT FROM AUTHOR]