Due to its impact, COVID-19 has been stressing the academy to search for curing, mitigating, or controlling it. It is believed that under-reporting is a relevant factor in determining the actual mortality rate and, if not considered, can cause significant misinformation. Therefore, this work aims to estimate the under-reporting of cases and deaths of COVID-19 in Brazilian states using data from the InfoGripe. InfoGripe targets notifications of Severe Acute Respiratory Infection (SARI). The methodology is based on the combination of data analytics (event detection methods) and time series modeling (inertia and novelty concepts) over hospitalized SARI cases. The estimate of real cases of the disease, called novelty, is calculated by comparing the difference in SARI cases in 2020 (after COVID-19) with the total expected cases in recent years (2016–2019). The expected cases are derived from a seasonal exponential moving average. The results show that under-reporting rates vary significantly between states and that there are no general patterns for states in the same region in Brazil. The states of Minas Gerais and Mato Grosso have the highest rates of under-reporting of cases. The rate of under-reporting of deaths is high in the Rio Grande do Sul and the Minas Gerais. This work can be highlighted for the combination of data analytics and time series modeling. Our calculation of under-reporting rates based on SARI is conservative and better characterized by deaths than for cases.
Objectives The “Bolsa-Família” Program (PBF) is a Brazilian conditional cash-transfer program in which families should comply with health, education, and social assistance conditionalities. The program aims to fight poverty and hunger, promoting nutrition and health services for low-income populations. This paper presents a database on the coverage of monitoring and compliance with the PBF health conditionalities in Brazil from January 2005 to July 2021. Data description Database on the PBF conditioning cash-transfer program coverage in Brazil from 2005 to 2021. It comprises information on the number of families benefited, health conditionalities, and the follow-up on vaccination and nutrition of children under seven years old. The cities and semesters are the minimal aggregation units.
Objectives Malaria is an infectious disease that annually presents around 200,000 cases in Brazil. The availability of data on malaria is crucial for enabling and supporting studies that can promote actions to prevent it. Therefore, the goal of this paper is to contribute to such studies by offering an integrated dataset containing data on reported and suspected cases of malaria in the Brazilian Legal Amazon comprising the period from the years 2009 to 2019. Data description This paper presents a dataset with all medical records of patients who were tested for malaria in the Brazilian Legal Amazon from 2009 to 2019. The dataset has 40 attributes and 22,923,977 records of suspected cases of malaria. Around 12% of the data correspond to confirmed cases of malaria. The attributes include data regarding the notifications, examinations, as well as personal patient information, which are organized into health regions.
Objectives We present a database on Brazilian spatial, demographic, and socioeconomic characteristics from 1996 to 2020. This database aims for integration and harmonization with epidemiological data from two major studies. It can also be a valuable database for designing and conducting various types of epidemiologic research, such as health inequality studies, ecological studies (mapping and time-trends), and multi-level analysis. Data description The database gathers official information obtained via open sources from the Brazilian Institute of Geography and Statistics, the Institute for Applied Economic Research, and the Ministry of Health. It includes 139,153 observations and 26 attributes aggregated by years and policy-relevant geographic units on geocoding of municipality centroids, total population size, child population by age-group, birth and mortality measures, Brazilian Municipal Human Development Index, Gini coefficient, Gross Domestic Product, and sanitation. We automated all data processing and curation in the free and open software R.
Objectives Neonatal mortality is a global public health problem, and the efforts to reduce child mortality is one of the goals of the 2030 Agenda for Sustainable Development, launched in 2015 by the United Nations. The availability of historical neonatal mortality rates (NMR) data in Brazilian municipalities is crucial to evaluate trends at local, regional and national level, identifying gaps and vulnerable territories. Therefore, the objective of this article is to offer an integrated dataset containing monthly data in a historical series from 1996 to 2017 with information on all births, neonatal deaths, and NMR (total, early and late components) enriched with information related to the municipality. Data description It is a dataset of historical data with information on the number of births, the number of neonatal deaths, the neonatal mortality rate (including early and late), and geographic information for each month (between January 1996 and December 2017) and Brazilian municipality.
Ao analisar séries temporais é possível observar mudanças significativas no comportamento das observações que frequentemente caracterizam a ocorrência de eventos. Eventos se apresentam como anomalias, pontos de mudança, ou padrões frequentes. Na literatura existem diversos métodos para detecção de eventos. Entretanto, a busca por um método adequado para uma série temporal não é uma tarefa simples, principalmente considerando-se que a natureza dos eventos muitas vezes não é conhecida. Neste contexto, este trabalho apresenta Harbinger, um framework para integração e análise de métodos de detecção de eventos. O Harbinger foi avaliado em dados sintéticos e reais, onde foi possível constatar que suas funcionalidades promovem a seleção de métodos e a compreensão dos eventos detectados.
BackgroundDue to its impact, COVID-19 has been stressing the academy to search for curing, mitigating, or controlling it. However, when it comes to control, there are still few studies focused on under-reporting estimates. It is believed that under-reporting is a relevant factor in determining the actual mortality rate and, if not considered, can cause significant misinformation. Therefore, the objective of this work is to estimate the under-reporting of cases and deaths of COVID-19 in Brazilian states using data from the InfoGripe on notification of Severe Acute Respiratory Infection (SARI). MethodologyThe methodology is based on the combination of data analytics (event detection methods) and time series modeling (inertia and novelty concepts) over hospitalized SARI cases. The estimate of real cases of the disease, called novelty, is calculated by comparing the difference in SARI cases in 2020 (after COVID-19) with the total expected cases in recent years (2016 to 2019). The expected cases are derived from a seasonal exponential moving average. ResultsThe results show that under-reporting rates vary significantly between states and that there are no general patterns for states in the same region in Brazil. The states of Minas Gerais and Mato Grosso have the highest rates of under-reporting of cases. The rate of under-reporting of deaths is high in the Rio Grande do Sul and the Minas Gerais. ConclusionsOur work presents the estimation of the under-reporting rates of COVID-19 in Brazilian states. This work can be highlighted for the combination of data analytics and time series modeling. Our calculation of under-reporting rates based on SARI is conservative and better characterized by deaths than for cases.
Malaria is an infectious disease that mainly affects the Legal Amazon. DATASUS includes the Malaria Epidemiological Surveillance Information System. Monitoring this dataset and integrating it with additional data sources, as well as performing proper data preprocessing is crucial to understand the phenomena behind the occurrences and medical care. Therefore, in this paper we make use of the Data Science Platform Applied to Health (PCDaS) as an enabling tool to analyze the evolution of malaria in the Legal Amazon. From its use, we raised research questions that can help in understanding and controlling this disease in Brazil. Resumo.A malária é uma doença infecciosa que atinge principalmente a Amazônia Legal. . Acompanhá-lo e integrar seus dados com fontes adicionais, bem como realizar a preparação dos dados é de vital importância para se compreender os fenômenos por trás das ocorrências e dos atendimentos médicos por meio das notificações realizadas no sistema. Para tanto, neste trabalho fazemos uso da Plataforma de Ciência de Dados aplicada à Saúde (PCDaS) como ferramenta para viabilizar a análise da evolução da malária na Amazônia Legal. A partir do seu uso, levantamos perguntas de pesquisas que podem ajudar na compreensão e no combate da malária no Brasil. IntroduçãoA malária é uma doença infecciosa causada por parasitas protozoários do gênero Plasmódio (Plasmodium) e é transmitida predominantemente a partir da picada do mosquito do gênero Anopheles, quando este já está infectado. Os países tropicais e subtropicais constituem a área endêmica da doença por terem estações chuvosas que proporcionam grande disponibilidade de água limpa parada, onde os mosquitos vetores podem depositar seus ovos e se proliferar [WHO, 2018]. * Os autores agradecem à FAPERJ, à CAPES e ao CNPq pelo financiamento parcial do projeto.
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