The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic hit almost all cities in Brazil in early 2020 and lasted for several months. Despite the effort of local state and municipal governments, an inhomogeneous nationwide response resulted in a death toll amongst the highest recorded globally. To evaluate the impact of the nonpharmaceutical governmental interventions applied by different cities—such as the closure of schools and businesses in general—in the evolution and epidemic spread of SARS-CoV-2, we constructed a full-sized agent-based epidemiological model adjusted to the singularities of particular cities. The model incorporates detailed demographic information, mobility networks segregated by economic segments, and restricting bills enacted during the pandemic period. As a case study, we analyzed the early response of the City of Natal—a midsized state capital—to the pandemic. Although our results indicate that the government response could be improved, the restrictive mobility acts saved many lives. The simulations show that a detailed analysis of alternative scenarios can inform policymakers about the most relevant measures for similar pandemic surges and help develop future response protocols.

Waterborne illnesses, including those caused by Salmonella, are an increasing public health challenge, particularly in developing countries. Potential sources of salmonellosis include fruits and vegetables irrigated/treated with surface water, leading to human infections. Salmonella causes millions of gastroenteritis cases annually, but early detection through routine water quality surveillance is time-consuming, requires specialized equipment, and faces limitations, such as coverage gaps, delayed data, and poor accessibility. Climate change-driven extreme events such as floods and droughts further exacerbate variability in water quality. In this context, remote sensing offers an efficient and cost-effective alternative for environmental monitoring. This study evaluated the potential of Sentinel-2 satellite imagery to predict Salmonella occurrence in the Maipo and Mapocho river basins (Chile) by integrating spectral, microbiological, climatic, and land use variables. A total of 1851 water samples collected between 2019 and 2023, including 704 positive samples for Salmonella, were used to develop a predictive model. Predicting Salmonella in surface waters using remote sensing is challenging for several reasons. Satellite sensors capture environmental proxies (e.g., vegetation cover, surface moisture, and turbidity) but not pathogens. Our goal was to identify proxies that reliably correlate with Salmonella. Twelve spectral indices (e.g., NDVI, NDWI, and MNDWI) were used as predictors to develop a predictive model for the presence of the pathogen, which achieved 59.2% accuracy. By spatially interpolating the occurrences, it was possible to identify areas with the greatest potential for Salmonella presence. NDWI and AWEI were most strongly correlated with Salmonella presence in high-humidity areas, and spatial interpolation identified the higher-risk zones. These findings reveal the challenges of using remote sensing to identify environmental conditions conducive to the presence of pathogens in surface waters. This study highlights the methodological challenges that must be addressed to make satellite-based surveillance an accessible and effective public health tool. By integrating satellite data with environmental and microbiological analyses, this approach can potentially strengthen low-cost, proactive environmental monitoring for public health decision-making in the context of climate change.

In comparison with many remote parts of the Earth, the watershed Sibinacocha Lake is poorly studied region and the various types of life that habit this region are highly dependent on the waters of this glaciated basin. The present study evaluates the snow cover in this basin associated with changes in air temperatures and precipitation during the period 1984 to 2019. It used the ERA5 high-resolution reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and 35 images from the Landsat satellite. The results showed a statistically significant positive trend in maximum air temperatures whose signal is in agreement with previous studies. Seasonal analysis of maximum temperatures showed positive trends in all seasons, indicating that the highest trends occur during winter and the lowest trends during the southern fall. In addition, there was a 26% decrease in snow cover in general in the basin, and it was evidenced that the greater decreases (53%, approximately) occurred in regions below the altitude of 5300 m. In fact, from this contribution of the study comes the need to carry out more research in this region, especially to support decisions covering agriculture, water resources management and biodiversity studies.

O presente trabalho objetiva avaliar se o acesso à saúde pública em territórios influenciados diretamente pela mineração é maior quando comparado com municípios fora desse tipo de território. Foram coletados dados de 2018, referentes aos municípios mineradores (Grupo 1) e não mineradores (Grupo 2), nas dimensões de acesso à saúde pública. Realizou-se o teste não paramétrico de Mann-whitney comparando os 2 grupos e uma regressão linear múltipla com o Índice de Acesso à Atenção Primária, criado pelos autores. Constatou-se que na Dimensão Pagamento, o PIB Per Capita, a Taxa de Beneficiários de Planos de Saúde e a Taxa de Beneficiários do Programa Bolsa Família obtiveram valores distintos quando se comparam os grupos. Observou-se também influência da mineração na relação da taxa de nascidos vivos com baixo peso, importante indicador de desenvolvimento humano de um município. Sendo essa taxa menor em municípios mineradores. Já na regressão não foi possível observar a influência da mineração no acesso a atenção primária. Desse modo, acredita-se que para aperfeiçoamento e melhor uso dos recursos da CFEM e outros impostos advindos da presença das mineradoras nos municípios, sejam criados diálogos entre a empresa, órgãos governamentais e o cidadão para criação de políticas públicas eficientes para melhorias no acesso à saúde pública.

Background: For the control of tuberculosis (TB), it must be adopted specific measures in areas of high transmission. Thus, it was aimed to identify the spatial pattern of new tuberculosis cases in Juazeiro do Norte-CE/ Brazil, from 2001 to 2012. Methods and Findings: It is a hybrid design, ecological study and temporal trend. The new cases reported with TB were included as subjects of research. It was outlined the socio demographic profile; the spatial analysis of cases was made through the Kernel technique and the nearest neighbor method with simulation. Among 914 new TB cases, there was a predominance of males (56,0%), aged between 20 to 39 years (42,0%), with incomplete elementary school (43,2%), pulmonary clinical form (89,1%). 79,1% of patients achieved a cure and 5,3% abandoned the treatment. In the studied period, it was identified homogeneous spatial distribution and non-random pattern, with the highest concentration of cases in the southern region of the city. Conclusion: The identification of spatial pattern becomes relevant, in order that it can contribute to the strengthening of the TB control by providing information that optimizes activities such as: active search, health education, notification of new cases and supervising the treatment performed by health professionals.

This study was carried out in the Sibinacocha lake watershed in the Peruvian Andes. In this region the long-term meteorological data are scarce and there are few studies of flow forecasts. Based on this evidence, in this study we present the monthly flow simulation, using statistical models and data-oriented model, with the purpose of evaluating the performance of these methodologies. The results of the comparative statistical analyses indicated that the data-oriented models, specifically the Recurrent Neural Networks, provided great improvements over the other models applied, specifically the ability to capture the minimum and maximum monthly flow, resulting in excellent statistical values (R²=0.85, d=0.96), thus suggesting this methodology as a possible application for flow forecasts.