COVID-19 outbreaks surveillance through text mining applied to electronic health records.

BACKGROUND: The COVID-19 pandemic has caused significant disruptions to everyday life and has had social, political, and financial consequences that will persist for years. Several initiatives with intensive use of technology were quickly developed in this scenario. However, technologies that enhance epidemiological surveillance in contexts with low testing capacity and healthcare resources are scarce. Therefore, this study aims to address this gap by developing a data science model that uses routinely generated healthcare encounter records to detect possible new outbreaks early in real-time. METHODS: We defined an epidemiological indicator that is a proxy for suspected cases of COVID-19 using the health records of Emergency Care Unit (ECU) patients and text mining techniques. The open-field dataset comprises 2,760,862 medical records from nine ECUs, where each record has information about the patient's age, reported symptoms, and the time and date of admission. We also used a dataset where 1,026,804 cases of COVID-19 were officially confirmed. The records range from January 2020 to May 2022. Sample cross-correlation between two finite stochastic time series was used to evaluate the models. RESULTS: For patients with age 18 years, we find time-lag () = 72 days and cross-correlation () ~ 0.82, = 25 days and ~ 0.93, and = 17 days and ~ 0.88 for the first, second, and third waves, respectively. CONCLUSIONS: In conclusion, the developed model can aid in the early detection of signs of possible new COVID-19 outbreaks, weeks before traditional surveillance systems, thereby anticipating in initiating preventive and control actions in public health with a higher likelihood of success.

A global empirical study on how street networks facilitate driving longer distances.

We simulated over 200 cities worldwide to investigate how the street network affects vehicle routes. We demonstrate that there is a ubiquitous super-linear relationship between time and distance when optimal route are chosen. More precisely, the average speed will be higher for longer trips when compared to shorter trips, showing that the street network makes driving further faster. We attribute this phenomenon to the spatial arrangement of extensive street segments that eliminate deceleration points. These results underscore the importance for cities to consider the distribution of deceleration-free streets while mitigating any negative impact on sustainability. To ensure efficient transportation planning and engineering, innovative approaches are necessary to facilitate the flow of goods and services while adhering to sustainable mobility principles.

Unveiling the paths of COVID-19 in a large city based on public transportation data.

Human mobility plays a key role in the dissemination of infectious diseases around the world. However, the complexity introduced by commuting patterns in the daily life of cities makes such a role unclear, especially at the intracity scale. Here, we propose a multiplex network fed with 9 months of mobility data with more than 107 million public bus validations in order to understand the relation between urban mobility and the spreading of COVID-19 within a large city, namely, Fortaleza in the northeast of Brazil. Our results suggest that the shortest bus rides in Fortaleza, measured in the number of daily rides among all neighborhoods, decreased [Formula: see text]% more than the longest ones after an epidemic wave. Such a result is the opposite of what has been observed at the intercity scale. We also find that mobility changes among the neighborhoods are synchronous and geographically homogeneous. Furthermore, we find that the most central neighborhoods in mobility are the first targets for infectious disease outbreaks, which is quantified here in terms of the positive linear relation between the disease arrival time and the average of the closeness centrality ranking. These central neighborhoods are also the top neighborhoods in the number of reported cases at the end of an epidemic wave as indicated by the exponential decay behavior of the disease arrival time in relation to the number of accumulated reported cases with decay constant [Formula: see text] days. We believe that these results can help in the development of new strategies to impose restriction measures in the cities guiding decision-makers with smart actions in public health policies, as well as supporting future research on urban mobility and epidemiology.

ELMO, a new helmet interface for CPAP to treat COVID-19-related acute hypoxemic respiratory failure outside the ICU: a feasibility study.

OBJECTIVE: To assess the feasibility of using a new helmet interface for CPAP, designated ELMO, to treat COVID-19-related acute hypoxemic respiratory failure (AHRF) outside the ICU. METHODS: This was a proof-of-concept study involving patients with moderate to severe AHRF secondary to COVID-19 admitted to the general ward of a public hospital. The intervention consisted of applying CPAP via the ELMO interface integrated with oxygen and compressed air flow meters (30 L/min each) and a PEEP valve (CPAP levels = 8-10 cmH2O), forming the ELMOcpap system. The patients were monitored for cardiorespiratory parameters, adverse events, and comfort. RESULTS: Ten patients completed the study protocol. The ELMOcpap system was well tolerated, with no relevant adverse effects. Its use was feasible outside the ICU for a prolonged amount of time and was shown to be successful in 60% of the patients. A CPAP of 10 cmH2O with a total gas flow of 56-60 L/min improved oxygenation after 30-to 60-min ELMOcpap sessions, allowing a significant decrease in estimated FIO2 (p = 0.014) and an increase in estimated PaO2/FIO2 ratio (p = 0.008) within the first hour without CO2 rebreathing. CONCLUSIONS: The use of ELMOcpap has proven to be feasible and effective in delivering high-flow CPAP to patients with COVID-19-related AHRF outside the ICU. There were no major adverse effects, and ELMO was considered comfortable. ELMOcpap sessions significantly improved oxygenation, reducing FIO2 without CO2 rebreathing. The overall success rate was 60% in this pilot study, and further clinical trials should be carried out in the future.(ClinicalTrials.gov identifier: NCT04470258 [http://www.clinicaltrials.gov/]).

ELMO, a new helmet interface for CPAP to treat COVID-19-related acute hypoxemic respiratory failure outside the ICU: a feasibility study / ELMO, uma nova interface do tipo capacete para CPAP no tratamento da insuficiência respiratória aguda hipoxêmica por COVID-19 fora da UTI: estudo de viabilidade

ABSTRACT Objective: To assess the feasibility of using a new helmet interface for CPAP, designated ELMO, to treat COVID-19-related acute hypoxemic respiratory failure (AHRF) outside the ICU. Methods: This was a proof-of-concept study involving patients with moderate to severe AHRF secondary to COVID-19 admitted to the general ward of a public hospital. The intervention consisted of applying CPAP via the ELMO interface integrated with oxygen and compressed air flow meters (30 L/min each) and a PEEP valve (CPAP levels = 8-10 cmH2O), forming the ELMOcpap system. The patients were monitored for cardiorespiratory parameters, adverse events, and comfort. Results: Ten patients completed the study protocol. The ELMOcpap system was well tolerated, with no relevant adverse effects. Its use was feasible outside the ICU for a prolonged amount of time and was shown to be successful in 60% of the patients. A CPAP of 10 cmH2O with a total gas flow of 56-60 L/min improved oxygenation after 30-to 60-min ELMOcpap sessions, allowing a significant decrease in estimated FIO2 (p = 0.014) and an increase in estimated PaO2/FIO2 ratio (p = 0.008) within the first hour without CO2 rebreathing. Conclusions: The use of ELMOcpap has proven to be feasible and effective in delivering high-flow CPAP to patients with COVID-19-related AHRF outside the ICU. There were no major adverse effects, and ELMO was considered comfortable. ELMOcpap sessions significantly improved oxygenation, reducing FIO2 without CO2 rebreathing. The overall success rate was 60% in this pilot study, and further clinical trials should be carried out in the future. (ClinicalTrials.gov identifier: NCT04470258 [http://www.clinicaltrials.gov/])

RESUMO Objetivo: Avaliar a viabilidade do uso de uma nova interface do tipo capacete para CPAP, denominada ELMO, para o tratamento da insuficiência respiratória aguda (IRpA) hipoxêmica por COVID-19 fora da UTI. Métodos: Estudo de prova de conceito envolvendo pacientes com IRpA hipoxêmica moderada a grave secundária à COVID-19, internados na enfermaria geral de um hospital público. A intervenção consistiu na aplicação de CPAP por meio da interface ELMO integrada a fluxômetros de oxigênio e ar comprimido (30 L/min cada) e a uma válvula de PEEP (níveis de CPAP = 8-10 cmH2O), formando o sistema ELMOcpap. Os pacientes foram monitorados quanto a parâmetros cardiorrespiratórios, eventos adversos e conforto. Resultados: Dez pacientes completaram o protocolo do estudo. O sistema ELMOcpap foi bem tolerado, sem efeitos adversos relevantes. Seu uso foi viável fora da UTI por tempo prolongado e mostrou-se bem-sucedido em 60% dos pacientes. Uma CPAP de 10 cmH2O com fluxo total de gás de 56-60 L/min melhorou a oxigenação após sessões de ELMOcpap de 30-60 min, permitindo redução significativa da FIO2 estimada (p = 0,014) e aumento da PaO2/FIO2 estimada (p = 0,008) na primeira hora, sem reinalação de CO2. Conclusões: O uso do ELMOcpap mostrou-se viável e eficaz no fornecimento de CPAP de alto fluxo a pacientes com IRpA hipoxêmica por COVID-19 fora da UTI. Não houve nenhum efeito adverso importante, e o ELMO foi considerado confortável. As sessões de ELMOcpap melhoraram significativamente a oxigenação, reduzindo a FIO2 sem reinalação de CO2. A taxa global de sucesso foi de 60% neste estudo piloto, e novos ensaios clínicos devem ser realizados. (ClinicalTrials.gov identifier: NCT04470258 [http://www.clinicaltrials.gov/])

Tracing contacts to evaluate the transmission of COVID-19 from highly exposed individuals in public transportation.

We investigate, through a data-driven contact tracing model, the transmission of COVID-19 inside buses during distinct phases of the pandemic in a large Brazilian city. From this microscopic approach, we recover the networks of close contacts within consecutive time windows. A longitudinal comparison is then performed by upscaling the traced contacts with the transmission computed from a mean-field compartmental model for the entire city. Our results show that the effective reproduction numbers inside the buses, [Formula: see text], and in the city, [Formula: see text], followed a compatible behavior during the first wave of the local outbreak. Moreover, by distinguishing the close contacts of healthcare workers in the buses, we discovered that their transmission, [Formula: see text], during the same period, was systematically higher than [Formula: see text]. This result reinforces the need for special public transportation policies for highly exposed groups of people.

Predicting dengue outbreaks at neighbourhood level using human mobility in urban areas.

Dengue is a vector-borne disease transmitted by the Aedes genus mosquito. It causes financial burdens on public health systems and considerable morbidity and mortality. Tropical regions in the Americas and Asia are the areas most affected by the virus. Fortaleza is a city with approximately 2.6 million inhabitants in northeastern Brazil that, during the recent decades, has been suffering from endemic dengue transmission, interspersed with larger epidemics. The objective of this paper is to study the impact of human mobility in urban areas on the spread of the dengue virus, and to test whether human mobility data can be used to improve predictions of dengue virus transmission at the neighbourhood level. We present two distinct forecasting systems for dengue transmission in Fortaleza: the first using artificial neural network methods and the second developed using a mechanistic model of disease transmission. We then present enhanced versions of the two forecasting systems that incorporate bus transportation data cataloguing movement among 119 neighbourhoods in Fortaleza. Each forecasting system was used to perform retrospective forecasts for historical dengue outbreaks from 2007 to 2015. Results show that both artificial neural networks and mechanistic models can accurately forecast dengue cases, and that the inclusion of human mobility data substantially improves the performance of both forecasting systems. While the mechanistic models perform better in capturing seasons with large-scale outbreaks, the neural networks more accurately forecast outbreak peak timing, peak intensity and annual dengue time series. These results have two practical implications: they support the creation of public policies from the use of the models created here to combat the disease and help to understand the impact of urban mobility on the epidemic in large cities.

A universal approach for drainage basins.

Drainage basins are essential to Geohydrology and Biodiversity. Defining those regions in a simple, robust and efficient way is a constant challenge in Earth Science. Here, we introduce a model to delineate multiple drainage basins through an extension of the Invasion Percolation-Based Algorithm (IPBA). In order to prove the potential of our approach, we apply it to real and artificial datasets. We observe that the perimeter and area distributions of basins and anti-basins display long tails extending over several orders of magnitude and following approximately power-law behaviors. Moreover, the exponents of these power laws depend on spatial correlations and are invariant under the landscape orientation, not only for terrestrial, but lunar and martian landscapes. The terrestrial and martian results are statistically identical, which suggests that a hypothetical martian river would present similarity to the terrestrial rivers. Finally, we propose a theoretical value for the Hack's exponent based on the fractal dimension of watersheds, γ = D/2. We measure γ = 0.54 ± 0.01 for Earth, which is close to our estimation of γ ≈ 0.55. Our study suggests that Hack's law can have its origin purely in the maximum and minimum lines of the landscapes.

Management Platforms and Protocols for Internet of Things: A Survey.

Internet of Things (IoT) management systems require scalability, standardized communication, and context-awareness to achieve the management of connected devices with security and accuracy in real environments. Interoperability and heterogeneity between hardware and application layers are also critical issues. To attend to the network requirements and different functionalities, a dynamic and context-sensitive configuration management system is required. Thus, reference architectures (RAs) represent a basic architecture and the definition of key characteristics for the construction of IoT environments. Therefore, choosing the best technologies of the IoT management platforms and protocols through comparison and evaluation is a hard task, since they are difficult to compare due to their lack of standardization. However, in the literature, there are no management platforms focused on addressing all IoT issues. For this purpose, this paper surveys the available policies and solutions for IoT Network Management and devices. Among the available technologies, an evaluation was performed using features such as heterogeneity, scalability, supported technologies, and security. Based on this evaluation, the most promising technologies were chosen for a detailed performance evaluation study (through simulation and deployment in real environments). In terms of contributions, these protocols and platforms were studied in detail, the main features of each approach are highlighted and discussed, open research issues are identified as well as the lessons learned on the topic.

LOADng-IoT: An Enhanced Routing Protocol for Internet of Things Applications over Low Power Networks.

The Internet of Things (IoT) is an emerging paradigm that proposes the connection of objects to exchange information in order to reach a common objective. In IoT networks, it is expected that the nodes will exchange data between each other and with external Internet services. However, due to deployment costs, not all the network devices are able to communicate with the Internet directly. Thus, other network nodes should use Internet-connected nodes as a gateway to forward messages to Internet services. Considering the fact that main routing protocols for low-power networks are not able to reach suitable performance in the displayed IoT environment, this work presents an enhancement to the Lightweight On-demand Ad hoc Distance-vector Routing Protocol-Next Generation (LOADng) for IoT scenarios. The proposal, named LOADng-IoT, is based on three improvements that will allow the nodes to find Internet-connected nodes autonomously and dynamically, decreasing the control message overhead required for the route construction, and reducing the loss of data messages directed to the Internet. Based on the performed assessment study, which considered several number of nodes in dense, sparse, and mobility scenarios, the proposed approach is able to present significant results in metrics related to quality-of-service, reliability, and energy efficiency.

A worldwide model for boundaries of urban settlements.

The shape of urban settlements plays a fundamental role in their sustainable planning. Properly defining the boundaries of cities is challenging and remains an open problem in the science of cities. Here, we propose a worldwide model to define urban settlements beyond their administrative boundaries through a bottom-up approach that takes into account geographical biases intrinsically associated with most societies around the world, and reflected in their different regional growing dynamics. The generality of the model allows one to study the scaling laws of cities at all geographical levels: countries, continents and the entire world. Our definition of cities is robust and holds to one of the most famous results in social sciences: Zipf's law. According to our results, the largest cities in the world are not in line with what was recently reported by the United Nations. For example, we find that the largest city in the world is an agglomeration of several small settlements close to each other, connecting three large settlements: Alexandria, Cairo and Luxor. Our definition of cities opens the doors to the study of the economy of cities in a systematic way independently of arbitrary definitions that employ administrative boundaries.

Human mobility in large cities as a proxy for crime.

We investigate at the subscale of the neighborhoods of a highly populated city the incidence of property crimes in terms of both the resident and the floating population. Our results show that a relevant allometric relation could only be observed between property crimes and floating population. More precisely, the evidence of a superlinear behavior indicates that a disproportional number of property crimes occurs in regions where an increased flow of people takes place in the city. For comparison, we also found that the number of crimes of peace disturbance only correlates well, and in a superlinear fashion too, with the resident population. Our study raises the interesting possibility that the superlinearity observed in previous studies [Bettencourt et al., Proc. Natl. Acad. Sci. USA 104, 7301 (2007) and Melo et al., Sci. Rep. 4, 6239 (2014)] for homicides versus population at the city scale could have its origin in the fact that the floating population, and not the resident one, should be taken as the relevant variable determining the intrinsic microdynamical behavior of the system.

Collaboration networks from a large CV database: dynamics, topology and bonus impact.

Understanding the dynamics of research production and collaboration may reveal better strategies for scientific careers, academic institutions, and funding agencies. Here we propose the use of a large and multidisciplinary database of scientific curricula in Brazil, namely, the Lattes Platform, to study patterns of scientific production and collaboration. Detailed information about publications and researchers is available in this database. Individual curricula are submitted by the researchers themselves so that coauthorship is unambiguous. Researchers can be evaluated by scientific productivity, geographical location and field of expertise. Our results show that the collaboration network is growing exponentially for the last three decades, with a distribution of number of collaborators per researcher that approaches a power-law as the network gets older. Moreover, both the distributions of number of collaborators and production per researcher obey power-law behaviors, regardless of the geographical location or field, suggesting that the same universal mechanism might be responsible for network growth and productivity. We also show that the collaboration network under investigation displays a typical assortative mixing behavior, where teeming researchers (i.e., with high degree) tend to collaborate with others alike.