COVID-19's intra-urban inequalities and social vulnerability in a medium-sized city

Background: Social conditions are related to the impact of epidemics on human populations. This study aimed to investigate the spatial distribution of cases, hospitalizations, and deaths from COVID-19 and its association with social vulnerability. Methods: An ecological study was conducted in 81 urban regions (UR) of Juiz de Fora from March to November 2020. Exposure was measured using the Health Vulnerability Index (HVI), a synthetic indicator that combines socioeconomic and environmental variables from the Demographic Census 2010. Regression models were estimated for counting data with overdispersion (negative binomial generalized linear model) using Bayesian methods, with observed frequencies as the outcome, expected frequencies as the offset variable, and HVI as the explanatory variable. Unstructured random-effects (to capture the effect of unmeasured factors) and spatially structured effects (to capture the spatial correlation between observations) were included in the models. The models were estimated for the entire period and quarter. Results: There were 30,071 suspected cases, 8,063 confirmed cases, 1,186 hospitalizations, and 376 COVID-19 deaths. In the second quarter of the epidemic, compared to the low vulnerability URs, the high vulnerability URs had a lower risk of confirmed cases (RR=0.61;CI95% 0.49-0.76) and a higher risk of hospitalizations (RR=1.65;CI95% 1.23-2.22) and deaths (RR=1.73;CI95% 1.08-2.75). Conclusions: The lower risk of confirmed cases in the most vulnerable UR probably reflected lower access to confirmatory tests, while the higher risk of hospitalizations and deaths must have been related to the greater severity of the epidemic in the city's poorest regions.

Towards an IoT Architecture to Pervasive Environments Through Design Science

From a computational perspective, the Covid-19 pandemic experienced around the world shows different lessons for society. Among the lessons, collecting data efficiently and effectively in pervasive environments proved to be one of the greatest challenges. Citizens and government agencies need data that portray the reality of the situation experienced in different environments, such as university campuses, neighborhoods and cities. This article presents as a contribution a computational architecture for the development of applications for pervasive environments using IoT and based on simulation. This research was carried out based on design science research through our proposed architecture. Experimental results showed interesting scenarios that could be differentiated for government agencies, considering IoT sensors, during a pandemic such as Covid-19. This work presents a scenario based on the contamination of Covid-19 in a pervasive and intelligent environment. In addition, the architectural approach provides support to scenarios visualization, which allows interesting parameters to decisions. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.