Abstract. This work aims to recognize, from programs developed by students and from a network of variables that represents the field of programming

The coronavirus disease 2019 (COVID-19) pandemic has had deleterious effects among the obstetric population. Pregnant and postpartum women constitute a high-risk group for severe COVID-19. Vaccination reduces the risk of infection, but it is not known whether women who become infected despite vaccination have a milder course of disease than those who had not been vaccinated. This retrospective cohort study evaluated whether vaccination reduces the severity of COVID-19 infection, as measured by severe maternal morbidity and mortality among hospitalized pregnant and postpartum individuals. A total of 2284 pregnant and postpartum women hospitalized with severe COVID-19 were included. Those who did and who did not receive COVID-19 vaccination were compared. The rates of intensive care unit admission, intubation, and mortality were significantly lower among subjects in the vaccinated group (p < 0.001, p < 0.001 and p < 0.001, respectively). The numbers of patients who needed to be vaccinated to avoid one case of intensive care unit admission, intubation, or death due to COVID-19 were 7, 7, and 9, respectively. The COVID-19 vaccine offers protective effects against intensive care unit admission, intubation, and death in hospitalized pregnant and postpartum women with severe SARS-CoV-2-induced SARS.

Since the first official case of COVID-19 was reported, many researchers around the world have spent their time trying to understand the dynamics of the virus by modeling and predicting the number of infected and deaths. The rapid spread and highly contagiousness motivate the necessity of monitoring cases in real-time, aiming to keep control of the epidemic. As pointed out by [3] , some pitfalls like limited infrastructure, laboratory confirmation and logistical problems may cause reporting delay, leading to distortions of the real dynamics of the confirmed cases and deaths. The aim of this study is to propose a suitable statistical methodology for modeling and forecasting daily deaths and reported cases of COVID-19, considering key features as overdispersion of data and correction of notification delay. Both, reporting delays and forecasting consider a Bayesian approach in which the daily deaths and the confirmed cases are modelled using the negative binomial (NB) distribution in order to accommodate the population heterogeneity. For the correction of notification delay, the mean number of occurrences regarding time t notified at time (mean delayed notifications) is associated to the temporal and the delay lag evolution of the notification process through a log link. With regard to daily forecasting, the functional form adopted for the number of deaths and reported cases of COVID-19 is related to the sigmoid growth equation. A variable regarding week days or days off was considered in order to account for possible reduction of the records due to the lower offer of tests on days off. To illustrate the methodology, we analyze data of deaths and infected cases of COVID-19 in Espírito Santo, Brazil. We also obtain long-term predictions.

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The objective of this study is to compare the demographic characteristics and symptoms in pregnant and postpartum women who died from Severe Acute Respiratory Syndrome (SARS) caused by COVID-19 or by nonspecific cause in different states of Brazil. This is a retrospective cohort study and the analysis was conducted on SARS death records between 02/16/2020 and 04/17/2021, obtained from the Information System for the Epidemiological Surveillance of Influenza (Sistema de Informação da Vigilância Epidemiológica da Gripe, SIVEP-Gripe). Pregnant and postpartum women, aged between 10 and 55 years, who died from SARS, were included and classified into two groups: SARS due to confirmed COVID-19 or SARS due to nonspecific cause. The cases were analyzed according to the women’s demographic and epidemiological characteristics, clinical symptoms, risk factors and disease evolution. As results, 19,333 pregnant and postpartum women were identified. From these, 1,279 died (1,026 deaths from COVID-19 and 253 deaths from SARS with nonspecific cause). The groups showed significant differences in age, education, race, and occurrence of obesity and chronic lung disease. The group of women who died from confirmed COVID-19 presented a significantly higher frequency of symptoms of fever, cough, fatigue, loss of taste, and loss of smell, as well as a higher rate of admission to the intensive care unit (ICU). Data analysis draws attention to the high number of cases of SARS without a causal diagnosis, the low access to ICU and orotracheal intubation (OTI), which might be explained by the demographic and regional inequalities in the access to healthcare.