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Following the World Health Organization proclaims a pandemic due to a disease that originated in China and advances rapidly across the globe, studies to predict the behavior of epidemics have become increasingly popular, mainly related to COVID-19. The critical point of these studies is to discuss the disease's behavior and the progression of the virus's natural course. However, the prediction of the actual number of infected people has proved to be a difficult task, due to a wide range of factors, such as mass testing, social isolation, underreporting of cases, among others. Therefore, the objective of this work is to understand the behavior of COVID-19 in the state of Ceará to forecast the total number of infected people and to aid in government decisions to control the outbreak of the virus and minimize social impacts and economics caused by the pandemic. So, to understand the behavior of COVID-19, this work discusses some forecast techniques using machine learning, logistic regression, filters, and epidemiologic models. Also, this work brings a new approach to the problem, bringing together data from Ceará with those from China, generating a hybrid dataset, and providing promising results. Finally, this work still compares the different approaches and techniques presented, opening opportunities for future discussions on the topic. The study obtains predictions with R2 score of 0.99 to short-term predictions and 0.93 to long-term predictions.
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Objetivo: Explorar a aplicação de inteligência artificial (IA) na predição da idade óssea a partir de imagens de raios-X. Método: Utilizou-se a Metodologia Interdisciplinar para o Desenvolvimento de Tecnologias em Saúde (MIDTS) para desenvolver uma ferramenta de predição. O treinamento foi realizado com redes neurais convolucionais (CNNs) usando um conjunto de dados de 14.036 imagens de raios-X. Resultados: A ferramenta alcançou um coeficiente de determinação (R²) de 0,94807 e um Erro Médio Absoluto (MAE) de 6,97, destacando sua precisão e potencial de aplicação clínica. Conclusão: O projeto demonstrou grande potencial para aprimorar a predição da idade óssea, com possibilidades de evolução conforme a base de dados aumenta e a IA se torna mais sofisticada.
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The COVID-19 pandemic has posed a significant public health challenge on a global scale. It is imperative that we continue to undertake research in order to identify early markers of disease progression, enhance patient care through prompt diagnosis, identification of high-risk patients, early prevention, and efficient allocation of medical resources. In this particular study, we obtained 100 5-min electrocardiograms (ECGs) from 50 COVID-19 volunteers in two different positions, namely upright and supine, who were categorized as either moderately or critically ill. We used classification algorithms to analyze heart rate variability (HRV) metrics derived from the ECGs of the volunteers with the goal of predicting the severity of illness. Our study choose a configuration pro SVC that achieved 76% of accuracy, and 0.84 on F1 Score in predicting the severity of Covid-19 based on HRV metrics.
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Background: Maternal infections are linked to neurodevelopmental impairments, highlighting the need to investigate SARS-CoV-2-induced immune activation. Objective: This study aimed to evaluate the impact of maternal infection on neurodevelopment and investigate whether cytokine and chemokine profiles predict delays at 24 months. Methods: Conducted in Brazil (January 2021–March 2022), this follow-up study included 18 SARS-CoV-2 positive pregnant women at 35–37 weeks’ gestation, 15 umbilical cord blood samples, and blood samples from 15 children at 6 months and 14 at 24 months. Developmental delay was defined using the Bayley Scales of Infant and Toddler Development, Third Edition, with scores below 90 in cognitive, communication, or motor domains. Results: At 6 months, 33.3% of infants exhibited cognitive delays, 20% communication delays, and 40% motor delays, increasing to 35.71%, 64.29%, and 57.14% at 24 months, respectively. Elevated interferon-gamma and tumor necrosis factor-alpha in cord blood correlated with cognitive delays, while interleukin (IL)-6, IL-8, IL-17, and IL-1β were associated with motor delays. Increased C-X-C motif chemokine ligand 10 and other cytokines were associated with communication delays. Conclusion: Maternal SARS-CoV-2 may impact infant neurodevelopment, as early cytokine elevations correlate with delays, highlighting the importance of early monitoring and interventions to reduce long-term effects. Impact: Prenatal SARS-COV-2 infection in pregnant women is linked to developmental delays in toddlers, with cytokine and chemokine changes associated with neurodevelopmental outcomes at 24 months. This study shows the long-term impact of maternal SARS-COV-2 infection on child development, highlighting inflammatory markers like IFN-γ, TNFα, IL-6, IL-8, IL-17, IL-1β, and CXCL10. Identifying specific cytokines correlating with cognitive, communication, and motor delays suggests potential biomarkers for early intervention. Conducted in Fortaleza, Brazil, the study emphasizes understanding local epidemiological impacts on child development, especially in regions with high infection rates. (Figure presented.)
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