Urban Green Spaces in Brazil: Challenges and Opportunities in the Context of the COVID-19 Pandemic

During the COVID-19 pandemic, urban green spaces were considered less prone to contagion, and thus people adopted them as alternative sites for improving mental health. The One Health concept advocated by health organizations worldwide supports the idea that the well-being of urban residents is strongly linked with physical activity in green areas. As the world grapples with the physical and mental health consequences of the COVID-19 pandemic, it becomes clearer that access to urban green spaces is a human rights issue. This study compared previously-mapped urban green spaces in five metropolitan regions in Brazil with the results of an extensive survey of municipal managers concerning possible increase in demand of population for green spaces. Urban green spaces of over 625 m2 were mapped in 117 municipalities, the total area of 4170 km2 representing 37.4% of the urban spaces analyzed in the five metropolitan regions. Out of these 117 municipalities, 49 had data available concerning demands of green spaces in the pandemic context. Overall, 20 municipalities (representing all five metropolitan regions) stated that there was an increase in visitation in urban green spaces, and 13 more indirectly suggested possible demands. When sustainability transitions are understood as geographical processes that happen in concrete places, urban green spaces then represent real locations where sustainable transitions can begin. The unequal distribution of these spaces also brings into consideration a social justice perspective, as well as aspects of public health that involve climate change resilience and epidemiological risk (SDG 11). © 2023, Scientific Publishing House of the Cardinal Stefan Wyszynski University. All rights reserved.

Forecasting COVID-19 pandemic using an echo state neural network-based framework

Forecasts can help in the decision-making process. Epidemiological forecasts are no different, they can help to evaluate the scenario and possible direction of disease spread, for guiding possible interventions. In this work, Echo State Networks (ESNs) are evaluated for COVID-19 (Coronavirus Disease 2019) cases and deaths forecasting ten days ahead. The chosen locations for the experiment are five states in Brazil, namely Sao Paulo (SP), Bahia (BA), Minas Gerais (MG), Rio de Janeiro (RJ), and Ceara (CE), the states with the most COVID-19 cases as of December 31, 2020. The results are evaluated using performance indexes RMSE (Root-mean-square error), MAE (Mean absolute error), and MAPE (Mean absolute percentage error). Results are compared with a common forecasting technique called ARIMA (Autoregressive Integrated Moving Average). The error signals are compared using Wilcoxon Signed-Rank Test, to evaluate the difference statistically. ESNs presented overall good results for a ten day horizon forecast regarding used performance metrics, but for the number of cases, ARIMA outperformed ESNs regarding RMSE, MAE, and MAPE in all but one state. For the number of deaths however, ESNs outperformed ARIMA in most states when the MAE is taken into account. ESNs are shown to be a solid forecasting model when compared with ARIMA, presenting comparable results and in some cases outperforming it.

Ensemble Learning Models Coupled with Urban Mobility Information Applied to Predict COVID-19 Incidence Cases

The coronavirus disease (COVID-19), according to the World Health Organization, by July 15th, 2021, has infected more than 188 million people, and more than 4 millions have died from it in the worldwide. It is important to forecast the incidence of cases in a short-term horizon to help the public health system develop strategic planning to deal with the COVID-19. In this chapter, several artificial intelligence (AI) models including extreme gradient boosting, extreme learning machine, long short-term memory, and support vector regression are used stand-alone, and coupled with the ensemble empirical mode decomposition (EEMD) employed to decompose the time-series into intrinsic mode functions and residual signals. All AI techniques are evaluated in the task of forecasting daily incidence COVID-19 cases in ten Brazilian states, with a high number of cases by September 4th, 2020, with seven and fourteen-days-ahead. Previous COVID-19 incidence cases and urban mobility information were employed as systems input for all forecasting models. The models’ effectiveness are evaluated based on the performance criteria. In general, the EEMD approach outperformed the compared models regarding the accuracy in 65% of the cases. Regarding the exogenous variables, urban mobility information indeed plays a key role in the forecasting task. Therefore, due to the efficiency of evaluated models to forecasting cumulative COVID-19 cases up to fourteen-days-ahead, the adopted models can be recommended as promising for forecasting and can be used to assist in development of public policies to mitigate the effects of COVID-19 outbreak. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

What is the position of the international precision medicine community in relation to the challenges raised by the COVID-19 pandemic?

Precision medicine can be defined as a movement of transformation of contemporary biomedicine that orients academic research activity, business models, and the development of health products and services designed individually for the user, based on patients' genetic information and other biomedical markers. In recent years, this community has been quite active in the international scientific scenario. However, during the COVID-19 pandemic, it is still not clear which positions or strategies these groups have adopted to respond to the health crisis. This article aims to understand how the international precision medicine community is reacting to the COVID-19 pandemic and the basis for their approaches and potential solutions suggested for mitigation of the negative effects from the increase in SARS-CoV-2 infections. A search was thus conducted in 28 documents from 18 selected sources, analyzing the narratives adopted by precision medicine experts in scientific articles, editorials, commentaries, perspectives, newspaper stories, newsletters and online conferences of the Personalized Medicine Coalition (PMC). The objective was to understand how these groups envisage a new sociotechnical configuration to respond to the pandemic and its effects.