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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.

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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.

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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.

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.