Who is Tweeting about COVID-19

During the COVID-19 pandemic many people used social media to seek information about the disease. In addition, these platforms were used to share news and opinions on issues related to COVID-19, such as vaccines and isolation policies. Extracting useful information for public health from these platforms efficiently poses some challenges due to the characteristics of social networks. Therefore, this article presents a method combining clustering and natural language processing for extracting information about users and their posts on social networks about COVID-19. The results show that the combination of clustering methods and textual analysis can reveal valuable information for public health using data from social networks. Future studies can employ this proposed method in order to get real-time information during future epidemics © 2022 IEEE.

Critical Success Factors in Combating Dengue and COVID-19: Lessons for Future Pandemics

The COVID-19 pandemic has affected the entire world, causing millions of deaths. In addition to this disease, many countries also periodically face outbreaks of other diseases, such as dengue. Although the two diseases have their specific characteristics, there may be common factors affecting them. Knowing these factors is essential for governments to plan actions to mitigate the impacts of future epidemics. This research aims to analyze data from several dimensions to identify the critical success factors for the fight against dengue and COVID-19. For this, Data Science techniques were applied to data from 645 cities in the State of São Paulo, Brazil. The results provide important information that may explain why some locations have been more successful than others in fighting those diseases, as well as identifying the common factors that may also impact other diseases. © 2022 IEEE.

Molecular Docking in silico Analysis of Brazilian Essential Oils Against Host Targets and SARS-CoV-2 Proteins

The inhibitory activity of thirty-one sesquiterpenes identified from Brazilian essential oils (Copaifera langsdorffii Desf., Croton cajucara Benth. and Siparuna guianensis Aublet.) were analyzed by in silico molecular docking. The compounds were characterized by gas chromatography-mass spectrometry (GC-MS) and gas chromatography with flame-ionization detection (GC-FID), and then, applied against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) proteins and human angiotensin-converting enzyme 2 (hACE2). Applying molecular docking and AutoDock Vina software, a total of 496 individual interactions were detected for sesquiterpenes along with SARS-CoV-2 proteins and hACE2 human angiotensin converting enzyme-2 protein. The findings showed considerable binding affinity of sesquiterpenes with the tested macromolecules. In that, beta-selinene from C. langsdorffii displayed the best energy (-7.2 kcal mol(-1)) and showed strong interactions with the amino acids of the SARS-CoV-2 M-Pro protein. Spathulenol from C. cajucara strongly interacted with human ACE2, with a binding energy of -7.1 kcal mol(-1). Meanwhile, gamma-eudesmol from S. guianensis presented the lowest binding energy (-7.5 kcal moL(-1)) by interacting with the SARS-CoV-2 M-Pro complex. Additionally, measurements were performed aiming to evaluate the best sesquiterpenes binding interactions with the main proteins and its homologue files. According to results, these Brazilian essential oils hold antiviral potential being a rich source for further in vitro and in vivo studies focusing on herbal therapeutic adjuvants against SARS-CoV-2 infections.

Childhood Trauma Exposure Increases Long COVID Risk (preprint)

BackgroundA proportion of those who contract COVID-19 will develop long COVID (i.e., symptoms that persist for three months or more). Childhood trauma contributes to a pro-inflammatory state in adulthood evidenced by high morbidity and early mortality, but it has not yet been investigated as a risk factor for long COVID. MethodsParticipants (N=338) completed online measures of premorbid health, COVID-19 positivity, symptoms, recovery, depression, anxiety, and post-traumatic stress disorder (PTSD). Questionnaires about childhood and recent traumatic experiences were completed by half of the sample (N=162). ResultsFifty-three percent of participants developed long COVID, of whom over 60% endorsed exercise intolerance and protracted myalgias, headaches, brain fog, and shortness of breath. Participants who experienced at least one childhood traumatic event were 3-fold more likely to develop the syndrome (OR=3.11, 95% CI, 1.49 to 6.48), while risk was nearly 6-fold increased for two or more events (OR=5.67, CI, 2.44 to 13.13). Regression models showed childhood trauma (OR=5.32, CI, 1.44 to 19.68), older age (OR=1.11, CI, 1.06 to 1.16), female sex (OR=4.02, CI, 1.34 to 12.12), along with chest pain (OR=8.77, CI, 2.80 to 27.43), brain fog (OR=3.33, CI, 1.16 to 9.57) and phantosmia (OR=5.90, CI, 1.40 to 24.86) during acute illness accurately classified long COVID status in 87% of participants. InterpretationsEarly adversity is a risk-factor for long COVID, likely due to altered immune response, central sensitization, and peripheral dysfunction. Childhood trauma, a crucial social determinant of health, should be routinely assessed in COVID-19 survivors and may aid in determining prognosis.

Combining clustering and classification algorithms for automatic bot detection: A case study on posts about COVID-19

In the last decade, there has been a great insertion of bots in several social media. Among the potentially harmful effects of these software agents, there are: the spread of computer viruses and different internet scams, and the spread of fake news, with emphasis on political-electoral and public health-related news. This work presents a new approach for bots' detection on Twitter, combining the use of feature selection, clustering, and classification algorithms. The proposed approach was compared with more conventional ones (for example, without the use of clustering) and the premise used in this work proved to be true: the use of clustering, together with the features selection, allowed the production of better classification models in order to identify not only the bots who have an activity profile considered non-human (extremely active on Twitter) but also other bots whose profiles are more similar to humans' ones. The best results of automatic detection of bots reached an overall accuracy of 96.8% and F1 score equal to 0.622. As an additional advantage, these values were achieved by decision-tree models, which can be considered explainable artificial intelligence models. © 2021 ACM.

COVID-19 with and without pneumonia: clinical outcomes in the internal medicine ward

The clinical features of COVID-19 differ substantially upon the presence (or absence) of viral pneumonia. The aim of this article was to describe the clinical characteristics of COVID-19 patients admitted to the Internal Medicine ward, as divided into those with and without pneumonia. This single-center prospective cohort study was conducted in a tertiary teaching public hospital in Buenos Aires City named Hospital General de Agudos Carlos G. Durand. Baseline data collection was performed within 48 hours of admission and patients were followed until discharge or in-hospital death. Epidemiological, clinical, laboratory, and radiological characteristics together with treatment data were obtained from the medical records. Of the 417 included, 243 (58.3%) had pneumonia. Median age was 43 years (IQR:32-57) and 222 (53.2%) were female. The overall crude case-fatality rate was 3.8%. None of the COVID-19 patients without pneumonia developed critical disease, required invasive mechanical ventilation nor died during hospitalization. However, 7 (4%) developed severe disease during follow-up. Among patients with COVID-19 pneumonia, in-hospital mortality rate was 6.6%, severe disease developed in 81 (33.3%), critical disease in 23 (9.5%), and 22 (9.1%) were admitted to the intensive care unit. A largely good prognosis was observed among COVID-19 patients without pneumonia, still, even among this group, unfavorable clinical progression can develop and should be properly monitored. Critical illness among patients with COVID-19 pneumonia was frequent and observed rates from this cohort provide a sound characterization of COVID-19 clinical features in a major city from South America.

Coronavirus from cities to forests: mapping vulnerable interfaces and hotspots for SARS-CoV-2 spillover from humans to biodiversity

Background: With the continuous spreading of SARS-CoV-2 globally, the probability for interactions between humans who are infected and wildlife tends to grow intensely, as well as the likelihood of viral spillover from humans to biodiversity. This aspect is of great concern for wildlife conservation and human health, because the list of highly susceptible animal groups that have contracted SARS-CoV-2 (bats, mustelids, and primates) is large and, once infected, these groups can act as vectors and reservoirs, becoming a substrate for viral mutations and recombinations and boosting the risk of new strains emerging, which can return to humans as new diseases. Little is known about the inducing factors facilitating coronavirus spillover from one species to another, but it can be argued that interface zones between wild fauna and humans, which are narrow edges between anthropic (cities, roads, parks, ecotourism sites, and agricultural frontiers) and sylvatic habitat, are zones of increased interaction between humans and wild animals, and thus have a higher probability of viral spillover events than other areas. In a similar context, the habitat compression by forest fragmentation also brings species and infected beings closer, reducing their home ranges and intensifying the risk of spillover among wild populations. Therefore, on the basis of the premise for zoonosis—the greater human–animal interaction, the greater risk of viral spillover—we aimed to identify the most and least susceptible areas to viral spillover in Brazil. Methods: We developed an approach combining ecological modelling (Biomod2: modelling habitat suitability for 158 bat and 49 primate species) and geographical information systems (by using demographic indicators, roads, and related variables) to map the most and least susceptible areas to spillover in Brazil. This map indicates priority areas for serological surveillance of fauna for monitoring the spillover and circulation of SARS-CoV-2 strains and variants in Brazilian biodiversity. Findings: Among our most relevant preliminary results, we found that forested areas surrounding the São Paulo Metropolitan Area are among the most susceptible areas for spillover. This resulted from the combination of high contaminated human density and high density of non-human primates interacting with humans in these transitional areas. Interpretation: Because of the high resolution of the results, the map can be useful for action planning and decision making in conservation and health, since susceptible areas denote not only a greater risk of virus jumping from humans to animals, but also of coronaviruses returning from fauna to humans in new viral strains. Funding: Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP;2019/12988-7 and 2018/14389-0).

COVID-19 con y sin neumonía: resultados en la sala de medicina interna

The clinical features of COVID-19 differ substantially upon the presence (or absence) of viral pneumonia. The aim of this article was to describe the clinical characteristics of COVID-19 patients admitted to the Internal Medicine ward, as divided into those with and without pneumonia. This single-center prospective cohort study was conducted in a tertiary teaching public hospital in Buenos Aires City named Hospital General de Agudos Carlos G. Durand. Baseline data collection was performed within 48 hours of admission and patients were followed until discharge or in-hospital death. Epidemiological, clinical, laboratory, and radiological characteristics together with treatment data were obtained from the medical records. Of the 417 included, 243 (58.3%) had pneumonia. Median age was 43 years (IQR:32-57) and 222 (53.2%) were female. The overall crude case-fatality rate was 3.8%. None of the COVID-19 patients without pneumonia developed critical disease, required invasive mechanical ventilation nor died during hospitalization. However, 7 (4%) developed severe disease during follow-up. Among patients with COVID-19 pneumonia, in-hospital mortality rate was 6.6%, severe disease developed in 81 (33.3%), critical disease in 23 (9.5%), and 22 (9.1%) were admitted to the intensive care unit. A largely good prognosis was observed among COVID-19 patients without pneumonia, still, even among this group, unfavorable clinical progression can develop and should be properly monitored. Critical illness among patients with COVID-19 pneumonia was frequent and observed rates from this cohort provide a sound characterization of COVID-19 clinical features in a major city from South America. Las características clínicas del COVID-19 difieren sustancialmente según la presencia (o ausencia) de neumonía viral. El objetivo de este artículo fue describir las características clínicas de los pacientes con COVID-19 internados en el servicio de Clínica Médica, divididos en pacientes con y sin neumonía. Fue un estudio de cohorte prospectivo, con base en un único centro, realizado en un hospital público de la ciudad de Buenos Aires: Hospital General de Agudos Carlos G. Durand. La recolección basal de datos se realizó dentro de las 48 horas del ingreso y los pacientes fueron seguidos hasta el alta o la muerte hospitalaria. Las características epidemiológicas, clínicas, de laboratorio y radiológicas junto con los datos del tratamiento se obtuvieron de la historia clínica. De los 417 incluidos, 243 (58.3%) tenían neumonía. La mediana de edad fue de 43 años (RIC: 32-57) y 222 (53.2%) eran mujeres. La tasa global de letalidad fue del 3.8%. Ninguno de los pacientes con COVID-19 sin neumonía desarrolló enfermedad crítica, requirió ventilación mecánica invasiva ni falleció durante la hospitalización. Sin embargo, 7 (4%) desarrollaron enfermedad grave durante el seguimiento. Entre aquellos con neumonía COVID-19, la tasa de mortalidad hospitalaria fue del 6.6%, se desarrolló enfermedad grave en 81 (33.3%), enfermedad crítica en 23 (9.5%) y 22 (9.1%) fueron trasladados a la unidad de cuidados intensivos. Los pacientes con COVID-19 sin neumonía presentaron buen pronóstico;sin embargo, incluso en este grupo, se observaron algunos con progresión clínica desfavorable, por lo que se requirió seguimiento adecuado. En los pacientes con neumonía por COVID-19, el desarrollo de enfermedad crítica fue frecuente y las tasas observadas en esta cohorte proporcionan una caracterización sólida de las características clínicas de los pacientes con COVID-19 en una importante ciudad de América del Sur.