Background: Brazil has the second largest COVID-19 number of cases, worldly. Even so, underdiagnosis in the country is massive. Nowcasting techniques have helped to overcome the underdiagnosis. Recent advances in machine learning techniques offer opportunities to refine the nowcasting. This study aimed to analyze the underdiagnosis of COVID-19, through nowcasting with machine learning, in a South of Brazil capital. Methods: The study has an observational ecological design. It used data from 3916 notified cases of COVID-19, from April 14th to June 02nd, 2020, in Florianopolis, Santa Catarina, Brazil. We used machine-learning algorithm to classify cases which had no diagnosis yet, producing the nowcast. To analyze the underdiagnosis, we compared the difference between the data without nowcasting and the median of the nowcasted projections for the entire period and for the six days from the date of onset of symptoms to diagnosis at the moment of data extraction. Results: The number of new cases throughout the entire period, without nowcasting, was 389. With nowcasting, it was 694 (UI95 496-897,025). At the six days period, the number without nowcasting was 19 and 104 (95% UI 60-142) with. The underdiagnosis was 37.29% in the entire period and 81.73% at the six days period. Conclusions: The underdiagnosis was more critical in six days from the date of onset of symptoms to diagnosis before the data collection than in the entire period. The use of nowcasting with machine learning techniques can help to estimate the number of new cases of the disease.

Objective: To analyze the underdiagnosis of COVID-19 through nowcasting with machine learning in a Southern Brazilian capital city. Methods: Observational ecological design and data from 3916 notified cases of COVID-19 from April 14th to June 2nd, 2020 in Florianópolis, Brazil. A machine-learning algorithm was used to classify cases that had no diagnosis, producing the nowcast. To analyze the underdiagnosis, the difference between data without nowcasting and the median of the nowcasted projections for the entire period and for the six days from the date of onset of symptoms were compared. Results: The number of new cases throughout the entire period without nowcasting was 389. With nowcasting, it was 694 (95%CI 496–897). During the six-day period, the number without nowcasting was 19 and 104 (95%CI 60–142) with nowcasting. The underdiagnosis was 37.29% in the entire period and 81.73% in the six-day period. The underdiagnosis was more critical in the six days from the date of onset of symptoms to diagnosis before the data collection than in the entire period. Conclusion: The use of nowcasting with machine learning techniques can help to estimate the number of new disease cases.

The present work raises an investigation about prediction and the feature importance to estimate the COVID-19 infection, using Machine Learning approach. Our work analyzed the inclusion of climatic features, mobility, government actions and the number of cases per health sub-territory from an existing model. The Random Forest with Permutation Importance method was used to assess the importance and list the thirty most relevant that represent the probability of infection of the disease. Among all features, the most important were: i) the variables per region health stand out, ii) period comprised between the date of notification and symptom onset, iii) symptoms features as fever, cough and sore throat, iv) variables of the traffic flow and mobility, and also v) wheathers features. The model was validated and reached an accuracy average of 81.82%, whereas the sensitivity and specificity achieved 87.52% and the 78.67% respectively in the infection estimate. Therefore, the proposed investigation represents an alternative to guide authorities in understanding aspects related to the disease.

A interdisciplinaridade se constitui como uma prática inclusiva de saberes visando à construção de novos conhecimentos científicos ou que atendam à demanda social de perfis profissionais variados e de competências múltiplas. Portanto, o objetivo é analisar o perfil acadêmico dos pesquisadores atuantes na área no tocante às possibilidades interdisciplinares que se apresentam à Ciência da Informação. Trata-se de uma pesquisa descritiva e de abordagem quantitativa a partir dos indicadores das áreas de atuação constantes na Plataforma Lattes. Foram avaliados 2.533 currículos de pesquisadores, considerando a formação acadêmica e a área de atuação. Como resultados, a pesquisa aponta que a área de Ciências Sociais Aplicadas é a que possui maior número de pesquisadores atuantes na Ciência da Informação, tanto na graduação quanto na pós-graduação. Na formação disciplinar dos pesquisadores, a pesquisa mostra que a Biblioteconomia é o curso de graduação mais representativo com 21,97%, seguidos de História 8,16%, Computação 6,85%, Engenharia 6,38%, Administração 6,28% e Comunicação Social 6,11%. Já por meio da análise por área de atuação foi possível concluir que a Administração, a Comunicação, a Ciência da Computação, a Educação e a História são as áreas de maior interação com a Ciência da Informação. Dessa forma, a pesquisa constata que, no cenário atual, a Ciência da Informação apresenta uma forte relação com outras áreas do conhecimento e que, por meio das possibilidades interdisciplinares, influencia e é influenciada no desenvolvimento epistemológico das ciências humanas, sociais e computacionais.

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