Analysis of signs and symptoms of SARS-CoV-2 virus infection considering different waves using Machine Learning (preprint)

In March 2020, the World Health Organization declared a world pandemic of COVID-19, which can manifest in humans as a consequence of virus infection of SARS-CoV-2. On this context, this work uses Data Mining and Machine Learning techniques for the infection diagnosis. A methodology was created to facilitate this task and can be applied in any outbreak or pandemic wave. Besides generating diagnosis models based only on signals and symptoms, the method can evaluate if there are differences in signals and symptoms between waves (or outbreaks) through explainable techniques of the machine learning models. Another aspect is identifying possible quality differences between exams, for example, Rapid Test (RT) and Reverse Transcription-Polymerase Chain Reaction (RT-PCR). The case study in this work is based on data from patients who sought care at Piquet Carneiro Polyclinic of the State University of Rio de Janeiro. In this work, the results obtained with the tests were used to diagnose symptomatic infection of the SARS-CoV-2 virus, based on related signals and symptoms, and the date of the initial of these signals and symptoms. Using the Random Forrest model, it was possible to achieve the result of up to 76% sensitivity, 86% specificity, and 79% accuracy in the results of tests in one contagion wave of the SARS-CoV-2 virus. Moreover, differences were found in signals and symptoms between contagion waves, in addition to the observation that exams RT-PCR and RT Antigen tests are more reliable than RT antibody test.

Genomic surveillance of SARS-CoV-2 tracks early interstate transmission of P.1 lineage and diversification within P.2 clade in Brazil (preprint)

The sharp increase of COVID-19 cases in late 2020 has made Brazil the new epicenter of the ongoing SARS-CoV-2 pandemic. Novel SARS-CoV-2 lineages P.1 and P.2, first identified respectively in Manaus and Rio de Janeiro, have been associated with potentially higher transmission rates and antibody neutralization escape. In this study, we performed a whole-genome sequencing of 185 samples isolated from three out of the five Brazilian regions, including Amazonas (North region), Rio Grande do Norte, Paraiba and Bahia (Northeast region), and Rio de Janeiro (Southeast region) aiming to identify SARS-CoV-2 mutations that could be involved in the surge of COVID19 cases in Brazil. Here, we showed a widespread dispersion of P.1 and P.2 across Brazilian regions. Except for Manaus, P.2 was the predominant lineage identified country-wise. P.2 lineage was estimated to have originated in February, 2020 and has diverged into new clades. Interstate transmission of P.2 was detected since March, but reached its peak in December, 2020 and January, 2021. Transmission of P.1 was also high in December. P.1 origin was inferred to have happened in August 2020. We also confirmed the presence of the variant under investigation (VUI) NP13L recently described in the southernmost region of Brazil to have spread across the Northeastern states. P.1, P.2 and NP13L are descended from the ancient B.1.1.28 strain, although during the first phase of the pandemic in Brazil presence of B.1.1.33 strain was also reported. Here, for the first time, we investigate the possible occurrence of a new variant of concern descending from B.1.1.33 that also carries the E484K mutation. Indeed, the recurrent report of many novel SARS-CoV-2 genetic variants in Brazil could be due to the absence of effective control measures resulting in high SARS-CoV2 transmission rates. Altogether, our findings provided a landscape of the critical state of SARS-CoV-2 across Brazil and confirm the need to sustain continuous sequencing of the SARS-CoV-2 isolates worldwide in order to early identify novel variants of concern and to monitor for vaccine effectiveness.

Combining science and social engagement againstCovid-19 in a Brazilian Slum (preprint)

Vaccine is the only way out towards an effective action against Covid-19. Nevertheless, for many underdeveloped countries, ordinary people's access will occur late within or even after the second disease wave. In this context, strategies implemented by social communities allied to the scientific knowledge may attenuate the rapid spread of cases and allow access of treatments at health care services to the population. Here, we present results of coordinate actions combining the aerosol SARS-Cov-2 virus monitoring and sanitization at Santa Marta slum in Rio de Janeiro city, where profits were significantly achieved.

Intra-host evolution during SARS-CoV-2 persistent infection (preprint)

Prolonged infection of SARS-CoV-2 represents a challenge to the development of effective public health policies to control the COVID-19 pandemic. The reason why some people have persistent infection and how the virus survives for so long are still not fully understood. For this reason, we aimed to investigate the intra-host evolution of SARS-CoV-2 during persistent infection. Thirty-three patients who remained RT-PCR positive in the nasopharynx for at least 16 days were included in this study. Complete SARS-CoV-2 sequences were obtained for each patient at two time points. Phylogenetic, populational, and computational analysis of viral sequences confirmed persistent infection with evidence for a transmission cluster in health care professionals that shared the same workplace. A high number of missense variants targeting crucial structural and non-structural proteins such as Spike and Helicase was found. Interestingly, longitudinal acquisition of substitutions in Spike protein mapped many SARS-CoV-2 predicted T cell epitopes. Furthermore, the mutational profiles observed were suggestive of RNA editing enzyme activities, indicating innate immune mechanisms of the host cell. Viral quasispecies analysis corroborates persistent infection mainly by increasing richness and nucleotide diversity over time. Altogether, our findings highlight a dynamic and complex landscape of host and pathogen interaction during persistent infection suggesting that the host's innate immunity shapes the increase of intra-host diversity with possible implications for therapeutic strategies and public health decisions during the COVID-19 pandemic.

Clinical and laboratory characteristics in outpatient diagnosis of COVID-19 in healthcare professionals in Rio de Janeiro, Brazil (preprint)

AimsThis study aimed to identify the symptoms associated with early stage SARS-CoV-2 (COVID-19) infections in healthcare professionals (HCP) using both clinical and laboratory data. MethodsA total of 1,297 patients, admitted between March 18 and April 8, 2020, were stratified according to their risk of developing COVID-19 using their responses to a questionnaire designed to evaluate symptoms and risk conditions. ResultsAnosmia/hyposmia (p <0.0001), fever (p<0.0001), body pain (p<0.0001), and chills (p=0.001) were all independent predictors for COVID-19, with a 72% estimated probability for detecting COVID-19 in nasopharyngeal swab samples. Leukopenia, relative monocytosis, decreased eosinophil values, CRP, and platelets were also shown to be significant independent predictors for COVID-19. ConclusionsThe significant clinical features for COVID-19 were identified as anosmia, fever, chills, and body pain. Elevated CRP, leukocytes under 5,400 x 109/L, and relative monocytosis (>9%) were common among patients with a confirmed COVID-19 diagnosis. These variables may help, in the absence of RT-PCR tests, to identify possible COVID-19 infections during pandemic outbreaks. SummaryFrom March 19 to April 8 2020, 1,297 patients attended the Polyclinic Piquet Carneiro for COVID-19 detection. Healthcare professional data was analyzed, significant clinical features were anosmia, fever, chills and body pain. Elevated CRP, leukopenia and monocytosis were common in COVID-19.

Modeling the initial phase of SARS-CoV-2 deposition in the respiratory tract mimicked by the 11C radionuclide (preprint)

The knowledge on the deposition and retention of the viral particle of SARS-CoV-2 in the respiratory tract during the very initial intake from the ambient air is of prime importance to understand the infectious process and COVID-19 initial symptoms. To give some light on that, we propose to use a modified version of a widely tested lung deposition model developed by the ICRP, in the context of the ICRP Publication 66, that provides deposition patterns of microparticles in different lung compartments. In the model, we mimicked the "environmental decay" of the virus, determined by controlled experiments related to normal speeches, by the radionuclide 11C that presents comparable decay rates. Our results confirm clinical observations on the high virus retentions observed in the extrathoracic region and the lesser fraction on the alveolar section (in the order of 5), which relevance is a subject to be investigated.