Rapid protocols to support COVID-19 clinical diagnosis based on hematological parameters

Purpose: In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in the construction of specific treatments for the disease. Objective: In this work, we propose rapid protocols for clinical diagnosis of COVID-19 through the automatic analysis of hematological parameters using evolutionary computing and machine learning. These hematological parameters are obtained from blood tests common in clinical practice. Method: We investigated the best classifier architectures. Then, we applied the particle swarm optimization algorithm (PSO) to select the most relevant attributes: serum glucose, troponin, partial thromboplastin time, ferritin, D-dimer, lactic dehydrogenase, and indirect bilirubin. Then, we assessed again the best classifier architectures, but now using the reduced set of features. Finally, we used decision trees to build four rapid protocols for Covid-19 clinical diagnosis by assessing the impact of each selected feature. The proposed system was used to support clinical diagnosis and assessment of disease severity in patients admitted to intensive and semi-intensive care units as a case study in the city of Paudalho, Brazil. Results: We developed a web system for Covid-19 diagnosis support. Using a 100-tree random forest, we obtained results for accuracy, sensitivity, and specificity superior to 99%. After feature selection, results were similar. The four empirical clinical protocols returned accuracies, sensitivities and specificities superior to 98%. Conclusion: By using a reduced set of hematological parameters common in clinical practice, it was possible to achieve results of accuracy, sensitivity, and specificity comparable to those obtained with RT-PCR. It was also possible to automatically generate clinical decision protocols, allowing relatively accurate clinical diagnosis even without the aid of the web decision support system. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.

Marginalization and Underrepresentation in Virtual Exchange: Reasons and Remedies

The recent expansion of virtual exchange (VE) in lieu of the Covid-19 pandemic and the ongoing advance of technology has resulted in considerably larger numbers of VE participants for those in certain areas and contexts, yet not all would-be participants have been so fortunate. In some regions and in various contexts, challenges in VE implementation have resulted in disadvantaged populations in terms of underrepresentation and marginalization in global VE networks. To illuminate such challenges, a mixed-method approach was utilized in the current study, beginning with a global survey to elucidate reasons for underrepresentation in terms of political, governmental, institutional, administrative, technological, pedagogical, cultural and personal challenges. Thereafter, semi-structured interviews with instructors, administrators, and educational decision makers were conducted to gain further insights. Although VE is now well established as an impactful mode of studying abroad, various region-specific challenges remain. We conclude with recommendations on how to overcome the challenges especially in those underrepresented regions and populations. © Journal of International Students.

Flood risk governance in Brazil and the UK: facilitating knowledge exchange through research gaps and the potential of citizen-generated data

Purpose The study aims to identify the gaps and the potentialities of citizen-generated data in four axes of warning system: (1) risk knowledge, (2) flood forecasting and monitoring, (3) risk communication and (4) flood risk governance. Design/methodology/approach Research inputs for this work were gathered during an international virtual dialogue that engaged 40 public servants, practitioners, academics and policymakers from Brazilian and British hazard and risk monitoring agencies during the Covid-19 pandemic. Findings The common challenges identified were lack of local data, data integration systems, data visualisation tools and lack of communication between flood agencies. Originality/value This work instigates an interdisciplinary cross-country collaboration and knowledge exchange, focused on tools, methods and policies used in the Brazil and the UK in an attempt to develop trans-disciplinary innovative ideas and initiatives for informing and enhancing flood risk governance.

The Covid-19 cross-frontier network: pandemic research and monitoring from the triple frontier between Brazil, Colombia, and Peru

The cross-border network can be understood as a process of effective articulation and collaboration between researchers and university institutions in the three countries that form the triple border between Brazil, Colombia, and Peru. Its fundamental objective is to systematize information, research, monitor, and publicize results on the pandemic in a joint way, that is, to think of these localities and border municipalities of the three countries as a single cross-border region. This perspective of research and data analysis aims to break with the methodological nationalism so present in research in international border areas. This poses a number of challenges in systematizing joint information, as the institutions of each nation-state develop different methodologies and different time frames for the publication of official data, in addition to problems of underreporting in some cases. These difficulties became very present in the process of producing bulletins from covid 19 in the region, as evidenced here during the dialogue with the researchers.

Using discrete Ricci curvatures to infer COVID-19 epidemic network fragility and systemic risk

The damage of the novel Coronavirus disease (COVID-19) is reaching an unprecedented scale. There are numerous classical epidemiology models trying to quantify epidemiology metrics. To forecast epidemics, classical approaches usually need parameter estimations, such as the contagion rate or the basic reproduction number. Here, we propose a data-driven, parameter-free, geometric approach to access the emergence of a pandemic state by studying the Forman-Ricci and Ollivier-Ricci network curvatures. Discrete Ollivier-Ricci curvature has been used successfully to forecast risk in financial networks and we suggest that those results can provide analogous results for COVID-19 epidemic time-series. We first compute both curvatures in a toy-model of epidemic time-series with delays, which allows us to create epidemic networks. We also compared our results to classical network metrics. By doing so, we are able to verify that the Ollivier-Ricci and Forman-Ricci curvatures can be a parameter-free estimate for identifying a pandemic state in the simulated epidemic. On this basis, we then compute both Forman-Ricci and Ollivier-Ricci curvatures for real epidemic networks built from COVID-19 epidemic time-series available at the World Health Organization (WHO). This approach allows us to detect early warning signs of the emergence of the pandemic. The advantage of our method lies in providing an early geometrical data marker for the pandemic state, regardless of parameter estimation and stochastic modelling. This work opens the possibility of using discrete geometry to study epidemic networks.

Prioritizing COVID-19 tests based on participatory surveillance and spatial scanning.

OBJECTIVES: This study aimed to identify, describe and analyze priority areas for COVID-19 testing combining participatory surveillance and traditional surveillance. DESIGN: It was carried out a descriptive transversal study in the city of Caruaru, Pernambuco state, Brazil, within the period of 20/02/2020 to 05/05/2020. Data included all official reports for influenza-like illness notified by the municipality health department and the self-reports collected through the participatory surveillance platform Brasil Sem Corona. METHODS: We used linear regression and loess regression to verify a correlation between Participatory Surveillance (PS) and Traditional Surveillance (TS). Also a spatial scanning approach was deployed in order to identify risk clusters for COVID-19. RESULTS: In Caruaru, the PS had 861 active users, presenting an average of 1.2 reports per user per week. The platform Brasil Sem Corona started on March 20th and since then, has been officially used by the Caruaru health authority to improve the quality of information from the traditional surveillance system. Regarding the respiratory syndrome cases from TS, 1588 individuals were positive for this clinical outcome. The spatial scanning analysis detected 18 clusters and 6 of them presented statistical significance (p-value < 0.1). Clusters 3 and 4 presented an overlapping area that was chosen by the local authority to deploy the COVID-19 serology, where 50 individuals were tested. From there, 32 % (n = 16) presented reagent results for antibodies related to COVID-19. CONCLUSION: Participatory surveillance is an effective epidemiological method to complement the traditional surveillance system in response to the COVID-19 pandemic by adding real-time spatial data to detect priority areas for COVID-19 testing.

Transcranial direct current stimulation as a strategy to manage COVID-19 pain and fatigue

The novel coronavirus disease (COVID-19) has infected millions of people worldwide and generated many sequels in the survivors, such as muscular pain and fatigue. These symptoms have been treated through pharmacological approaches;however, infected people keep presenting physical limitations. Besides, the COVID-19 damage to the central nervous system has also been related to the presence of some physical impairment, so strategies that focus on diverse brain areas should be encouraged. Transcranial Direct Current Stimulation (tDCS) is a non-pharmacological tool that could be associated with pharmacological treatments to improve the central nervous system function and decrease the exacerbation of the immune system response. tDCS targeting pain and fatigue-related areas could provide an increase in neuroplasticity and enhancements in physical functions. Moreover, it can be used in infirmaries and clinical centers to treat COVID-19 patients.

Psychological impact of COVID-19 on healthcare workers: prevalence systematic review

Objective: To synthesize the prevalence of psychological and mental health outcomes among healthcare professionals who are responsible for treating patients with COVID-19. Methods: Systematic literature review. The literature search was carried out in the PubMed, CINAHL and Scopus databases. Studies written in English, Portuguese and Spanish and that were published between December 1st 2019 and July 31st 2020 were included. The systematic review was performed using fixed-effect meta-analysis of binary data with STATA (R) 15.0 and inverse-variance method using Freeman-Tukey double arcsine transformation. Results: The search strategy identified 38,657 records. Only five of those studies were selected and were included in the final review corpus. The meta-analysis conducted showed that the prevalence of depression reached 27.5% (95%CI=25.9-29.3;p<0.001), the prevalence of anxiety was 26.8% (95%CI=25.1-28.5;p<0.001), that of insomnia 35.8% (95%CI=33.8-37.9;p=0.03) and the prevalence of stress amounted to 51.9% (95%CI=49.6-54.3;p<0.001). Three of the studies included in the review show that healthcare professionals have also reported significant levels of vicarious traumatization, posttraumatic stress, somatization, and obsessive-compulsive symptoms. Conclusion: The COVID-19 pandemic is found to have a very significant psychological impact on healthcare workers and is quite likely to lead to an important prevalence of depression, anxiety, insomnia, and stress. Frontline healthcare professionals are a particularly vulnerable group and deserve special attention/ intervention.