Miracle-dz/UniFAR-FTUs huggingface dataset:
Pressure-Regulated Ventilator Splitting (PReVentS): A COVID-19 Response Paradigm from Yale University

In the current COVID-19 crisis, the US and many countries in the world are suffering acute shortages of modern ventilators to care for desperately ill patients. Since modern ICU ventilators are powerful devices that can deliver very high gas flow rates and pressures, multiple physicians have attempted to ventilate more than one patient on a single ventilator -- so-called 'vent splitting'. Early applications of this approach have utilized simple concatenations of ventilator tubing and T-pieces, to provide flow to more than one patient. Additional approaches using custom flow splitters -- sometimes made using 3D printing technologies -- have also advanced into the clinic with FDA approval. However, heretofore there has been less progress made on controlling individual ventilatory pressures for patients with severe lung disease. Given the inherent variability and instability of lung compliance amongst patients with COVID-19, there remains an important need to provide a means of extending ventilator usefulness to more than one patient, but in a way that provides more tailored pressures that can be titrated over time. In this descriptive report, we provide the basis for a ventilator circuit that can support two patients with individualized peak inspiratory and end-expiratory pressures. The circuit is comprised of exclusively 'off the shelf' materials and is inexpensive to produce. The circuit can be used with typical ICU ventilators, and with anesthesia ventilators used in operating rooms. Inspiratory and end-expiratory pressures for each patient can be titrated over time, without changes for one patient affecting the ventilation parameters of the other patient. Using in-line spirometry, individual tidal volumes can be measured for each patient. This Pressure-Regulated Ventilator Splitting (PReVentS) Yale University protocol operates under a pressure-control ventilatory mode, and may function optimally when patients are not triggering breaths from the ventilator. This method has been tested thus far only in the laboratory with mock lungs, and has not yet been deployed in animals or in patients. However, given the novelty and potential utility of this approach, we deemed it appropriate to provide this information to the broader critical care community at the present time. In coming days and weeks, we will continue to characterize and refine this approach, using large animal models and proof-of-principle human studies.
Next weeks of SARS-CoV-2: Projection model to predict time evolution scenarios of accumulated cases in Spain

Background and objectives: SARS-CoV-2 is a new type of coronavirus that can affect people and causes respiratory disease, COVID-19. It is affecting the entire planet and we focus in Spain, where the first case was detected at the end of January 2020 and in recent weeks it has increased in many cases. We need predictive models in order to be efficient and take actions. The general goal of this work is present a new model of SARS-CoV-2 to predict different scenarios of accumulated cases in Spain. Material and methods: In this short report is used a model proposed previously, based on a parametric model Weibull and in a the library BDSbiost3 developed in R to infer and predict different scenarios of the evolution of SARS-CoV-2 for the accumulated cases in Spain after the spread that affects Spain detected at the end of January of this year. Results: In the analyses presented, projective curves have been generated for the evolution of accumulated cases in which they reach about 4,000 cases or about 15,000 cases, for which the lines of the day in which the value for 90 will be reached can be seen vertically 90, 95 and 99% of the asymptote (maximum number of cases, from that day they will begin to descend or remain the same), that is why the vertical lines would indicate the brake of the disease. For the worst-case scenario, it takes 118, 126 or 142 days to reach the maximum number of cases (n = 15,000) to reach 90, 95 and 99% of the asymptote (maximum number of cases), respectively. This means translated in a time scale that in the worst case the virus will not stop its progress, in Spain, until summer 2020, hopefully before. Comments and conclusions: This model could be used to plan the resources and see if the policies or means dedicated to the virus are slowing the progress of the virus or it is necessary to implement others that are more effective, and can also validate a method for future outbreaks of diseases such as these.
COVID-19 infection during pregnancy: A systematic review to summarize possible symptoms, treatments, and pregnancy outcomes

Abstract Background: With the exponential increase in coronavirus disease 2019 (COVID-19) worldwide, an increasing proportion of pregnant women are now infected during their pregnancy. The aims of this systematic review were to summarize the possible symptoms, treatments, and pregnancy outcomes of women infected with COVID-19 during their pregnancy. Methods: Four databases (Medline, Web of Science, Scopus, and CINAHL) were searched on March 25, 2020, using the following keywords: 'COVID-19', 'nCoV-2019', and 'coronavirus'. Articles included if they reported either the symptoms, treatments for the women who had been infected with the COVID-19 during their pregnancy or pregnancy outcomes. The selected articles' results were summarized employing a narrative synthesis approach. Results: A total of nine studies were selected for this study, comprising 101 infected pregnant women. Other than the infected general population, infected pregnant women reported different symptoms; however, fever (66.7%), cough (39.4%), fatigue (15.2%), and breathing difficulties (14.1%) were common. Infected pregnant women were given different treatments than the general infected population. The C-section was a common (83.9%) mode of delivery among infected pregnant women, and a higher proportion of births were preterm births (30.4%) and low birth weight (17.9%). Conclusions: Pregnant infected women had different symptoms, and they were given dissimilar treatments than the general infected population. Healthcare providers may have appropriately informed about these symptoms and treatments. They, therefore, would be able to handle infection during pregnancy effectively, which would reduce common adverse consequences among infected pregnant women.
Spatial variability in the risk of death from COVID-19 in Italy, 2020

Objectives: Italy is bearing the brunt of the COVID-19 pandemic, as the death toll there has already surpassed that in Wuhan, the city in China where the coronavirus emerged in December 2019. Here we employ statistical methods to assess the severity of COVID-19 pandemic across different regions of Italy. Method: We manually retrieved the daily cumulative numbers of laboratory-confirmed cases and deaths attributed to COVID-19 stratified by region in Italy. We estimated both the crude and time-delay adjusted case fatality ratio across five geographic regions of Italy. Results: The Northwest that includes Lombardy exhibited the highest time-delay-adjusted CFR at 23.0% followed by the Northeast (14.2%), the Center region (11.6%) and the South region (12.1%). The Island region has exhibited the lowest CFR (9.6%). Conclusion: Our estimates in the Northwest and the Northeast are higher than those reported from most affected areas in China. The CFR in Northwest Italy is 1.9-fold higher than that in Wuhan. Our finding reflects the need of urgent medical support in the Northwest region and the appropriate planning and supplies procurement in other regions of Italy focusing on medical care delivery to those who are at the highest risk of poorer outcomes due to COVID-19.
Adaptive cyclic exit strategies from lockdown to suppress COVID-19 and allow economic activity

Many countries have applied lockdown that helped suppress COVID-19, but with devastating economic consequences. Here we propose exit strategies from lockdown that provide sustainable, albeit reduced, economic activity. We use mathematical models to show that a cyclic schedule of 4-day work and 10-day lockdown, or similar variants, can prevent resurgence of the epidemic while providing part-time employment. The cycle pushes the reproduction number R below one by reduced exposure time and by exploiting the virus latent period: those infected during work days reach peak infectiousness during lockdown days. The number of work days can be adapted in response to observations. Throughout, full epidemiological measures need to continue including hygiene, physical distancing, compartmentalization, testing and contact tracing. This conceptual framework, when combined with other interventions to control the epidemic, can offer the beginnings of predictability to many economic sectors.
Optimal Control applied to a SEIR model of 2019-nCoV with social distancing

Does the implementation of social distancing measures have merit in controlling the spread of the novel coronavirus? In this study, we develop a mathematical model to explore the effect of social distancing on new disease infections. Mathematical analyses of our model indicate that successful eradication of the disease is strongly dependent on the chosen preventive measure. Numerical computations of the model solution demonstrate that the ability to flatten the curve becomes easier as social distancing is strictly enforced. Based on our model, we also formulate an optimal control problem and solve it using Pontryagin's Maximum Principle and an efficient numerical iterative method. Our numerical results of an optimal 2019-nCoV treatment protocol that yields a minimum disease burden from this disease indicates that social distancing is vitally important.
Community responses during early phase of the COVID-19 epidemic: a cross-sectional study

Community responses are important for outbreak management during the early phase when preventive interventions are the major options. Therefore, this study aims to examine the behavioral responses of the community during the early phase of the COVID-19 epidemic in the Razavi Khorasan Province of Iran. A cross-sectional online survey was proceeded after confirmed COVID-19 in Iran. The population of the study was 500 residents of Razavi Khorasan areas were randomly surveyed. The research tool was demographic and risk perception questionnaire and Anxiety was assessed using the 7item GAD Scale. The data analyzed using the SPSS statistical version (V.20). The means of age participants was 31.9 11.9. The mean GAD-7 scores were 6.4 5.2 and 92.4% had moderate or severe anxiety (GAD-7 score ≥10). Many respondents reported their health status were very good or good (62.2 %; 311/500). About a quarter of them had respiratory symptoms in the past 14 days and experienced 20% of them travelled outside the Razavi Khorasan Province in the previous. Risk perception toward COVID-19 in the community of the Razavi Khorasan Province was moderate. Most participants are alert to disease progression. This study suggested timely behavioral assessment of the community is beneficial and effective to inform next intervention, and risk communication strategies in epidemic disease.
Survival analysis of hospital length of stay of novel coronavirus (COVID-19) pneumonia patients in Sichuan, China

Objective Allocation of medical resource is essential to a strong public health system in response to COVID-19. Analysis of confirmed COVID-19 patients' hospital length of stay in Sichuan can be informative to decision-making in other regions of the world. Design A retrospective cross-sectional study. Data and Method Data from confirmed COVID-19 cases in Sichuan Province were obtained from the National Notifiable Diseases Reporting System (NNDRS) and field survey. We collected information on demographic, epidemiological, clinical characteristics, and the length of hospital stay for confirmed patients. We conducted an exploratory analysis using adjusted multivariate cox-proportional models. Participants A total of 538 confirmed patients of COVID-19 infection in Sichuan Province from January to March 2020. Outcome measure The length of hospital stay after admissions for confirmed patients. Results From January 16, 2020 to March 4, 2020, 538 human cases of COVID-19 infection were laboratory-confirmed, and were hospitalized for treatment. Among these, 271 (50%) were 45 years of age or above, 285 (53%) were male, 450 (84%) were considered as having mild symptoms. The median hospital length of stay was 19 days (interquartile range (IQR): 14-23, Range: 3-41). Adjusted multivariate analysis showed that longer hospital length of stay was associated with factors aged 45 and over (HR: 0.74, 95% CI: 0.60-0.91), those admitted to provincial hospital (HR: 0.73, 95% CI: 0.54-0.99), and those with serious illness (HR: 0.66, 95% CI: 0.48-0.90); living in areas with more than 5.5 healthcare workers per 1000 population (HR: 1.32, 95% CI: 1.05-1.65) was associated with shorter hospital length of stay. There was no gender difference. Conclusions Preparation control measures of COVID-19 should involve the allocation of sufficient medical resources, especially in areas with older vulnerable populations and in areas that lack basic medical resources.
Initial evidence of higher morbidity and mortality due to SARS-CoV-2 in regions with lower air quality

COVID-19 has spread in all continents in a span of just over three months, escalating into a pandemic that poses several humanitarian as well as scientific challenges. We here investigated the geographical expansion of the infection and correlate it with the annual indexes of air quality observed from the Sentinel-5 satellite orbiting around China, Italy and the U.S.A. Controlling for population size, we find more viral infections in those areas afflicted by Carbon Monoxide (CO) and Nitrogen Dioxide (NO2). Higher mortality was also correlated with poor air quality, namely with high PM2.5, CO and NO2 values. In Italy, the correspondence between poor air quality and SARS-CoV-2 appearance and induced mortality was the starkest. Similar to smoking, people living in polluted areas are more vulnerable to SARS-CoV-2 infections and induced mortality. This further suggests the detrimental impact climate change will have on the trajectory of future epidemics.
Research on the Influence of Information Diffusion on the Transmission of the Novel Coronavirus (COVID-19)

With the rapid development of mobile Internet in China, the information of the epidemic is full-time and holographic, and the role of information diffusion in epidemic control is increasingly prominent. At the same time, the publicity of all kinds of big data also provides the possibility to explore the impact of media information diffusion on disease transmission. This paper explores the mechanism of the influence of information diffusion on the spread of the novel coronavirus, develops a model of the interaction between information diffusion and disease transmission based on the SIR model, and empirically tests the role and mechanism of information diffusion in the spread of COCID-19 by using econometric method. The result shows that there was a significant negative correlation between the information diffusion and the spread of the novel coronavirus; The result of robust test shows that the spread of both epidemic information and protection information hindered the further spread of the epidemic.
A streamlined approach to rapidly detect SARS-CoV-2 infection, avoiding RNA extraction

In the current pandemic, the presence of SARS-CoV-2 RNA in samples by nucleic acid (NA) molecular analysis is the only method available to diagnose COVID-19 disease and to assess patients' infectiveness. Recently, the demand for laboratory reagents has greatly increased; in particular, there is a worldwide shortage of RNA extraction kits. Here, we describe a fast, simple and inexpensive method for the detection of SARS CoV-2 RNA, which includes a pretreatment with Proteinase K and a heating-cooling cycle before the amplification. This method bypasses the RNA extraction step; it leads to a higher amount of available viral RNA compared to the automated extraction methods, and generates the same profile in the subsequent amplification phase.
Intervention strategies against COVID-19 and their estimated impact on Swedish healthcare capacity

Objectives: During March 2020, the COVID-19 pandemic has rapidly spread globally, and non-pharmaceutical interventions are being used to reduce both the load on the healthcare system as well as overall mortality. Design: Individual-based transmission modelling using Swedish demographic and Geographical Information System data and conservative COVID-19 epidemiological parameters. Setting: Sweden Participants: A model to simulate all 10.09 million Swedish residents. Interventions: 5 different non-pharmaceutical public-health interventions including the mitigation strategy of the Swedish government as of 10 April; isolation of the entire household of confirmed cases; closure of schools and non-essential businesses with or without strict social distancing; and strict social distancing with closure of schools and non-essential businesses. Main outcome measures: Estimated acute care and intensive care hospitalisations, COVID-19 attributable deaths, and infections among healthcare workers from 10 April until 29 June. Findings: Our model for Sweden shows that, under conservative epidemiological parameter estimates, the current Swedish public-health strategy will result in a peak intensive-care load in May that exceeds pre-pandemic capacity by over 40-fold, with a median mortality of 96,000 (95% CI 52,000 to 183,000). The most stringent public-health measures examined are predicted to reduce mortality by approximately three-fold. Intensive-care load at the peak could be reduced by over two-fold with a shorter period at peak pandemic capacity. Conclusions: Our results predict that, under conservative epidemiological parameter estimates, current measures in Sweden will result in at least 40-fold over-subscription of pre-pandemic Swedish intensive care capacity, with 15.8 percent of Swedish healthcare workers unable to work at the pandemic peak. Modifications to ICU admission criteria from international norms would further increase mortality.
Perceptions and behavioural responses of the general public during the COVID-19 pandemic: A cross-sectional survey of UK Adults

Objective: To examine risk perceptions and behavioural responses of the UK adult population during the early phase of the COVID-19 epidemic in the UK. Design: A cross-sectional survey Setting: Conducted with a nationally representative sample of UK adults within 48 hours of the UK Government advising the public to stop non-essential contact with others and all unnecessary travel. Participants: 2,108 adults living in the UK aged 18 years and over. Data were collected between March 17 and 18 2020. Main outcome measures: Descriptive statistics for all survey questions, including the number of respondents and the weighted percentages. Logistic regression was used to identify sociodemographic variation in: (1) adoption of social-distancing measures, (2) ability to work from home, and (3) willingness and (4) ability to self-isolate. Results Overall, 1,992 (94.2%) respondents reported taking at least one preventive measure: 85.8% washed their hands with soap more frequently; 56.5% avoided crowded areas and 54.5% avoided social events. Adoption of social-distancing measures was higher in those aged over 70 compared to younger adults aged 18 to 34 years (aOR:1.9; 95% CI:1.1 to 3.4). Those with the lowest household income were six times less likely to be able to work from home (aOR:0.16; 95% CI:0.09 to 0.26) and three times less likely to be able to self-isolate (aOR:0.31; 95% CI:0.16 to 0.58). Ability to self-isolate was also lower in black and minority ethnic groups (aOR:0.47; 95% CI:0.27 to 0.82). Willingness to self-isolate was high across all respondents. Conclusions The ability to adopt and comply with certain NPIs is lower in the most economically disadvantaged in society. Governments must implement appropriate social and economic policies to mitigate this. By incorporating these differences in NPIs among socio-economic subpopulations into mathematical models of COVID-19 transmission dynamics, our modelling of epidemic outcomes and response to COVID-19 can be improved.
Rapid diagnosis of SARS-CoV-2 infection by detecting IgG and IgM antibodies with an immunochromatographic device: a prospective single-center study

Objectives: SARS-CoV-2 infection constitutes a diagnostic challenge in patients from 2-3 weeks after the onset of symptoms, due to the low positivity rate of the PCR, especially in upper respiratory samples. Serologic tests based on ELISA have been developed and evaluated as useful complements to PCR in these situations. However, there is scarce information about the usefulness of rapid tests based on immunochromatography. The aim of our study was to analyze the diagnostic performance of these rapid tests in COVID-19 pneumonia patients. Methods: We evaluated an immunochromatographic test (AllTest COV-19 IgG / IgM kit) which detects IgG and IgM antibodies. First, we performed a validation of the serologic test using serum samples from 45 healthy control patients (group 1) and 55 confirmed by PCR cases of COVID-19 (group 2) in order to establish the specificity and sensitivity, respectively. Then we prospectively employed the test in 63 patients diagnosed with pneumonia of unknown etiology that were SARS-CoV-2 negative by PCR (group 3), to establish the diagnostic performance in these patients. Results: All patients from group 1 (healthy controls) resulted negative for the serologic test (specificity = 100%). Regarding group 2 (PCR positive) patients, the median time from the onset of symptoms was 11 days and the test was positive for either IgM or IgG in 26 out of 55 patients (overall sensitivity = 47.3%). However, in those patients with 14 days or more from onset of symptoms, the sensitivity was 73.9%. Regarding group 3 patients, the median days from onset of symptoms was 17 and the test was positive in 56 out of 63 patients (88.9% positivity rate). In these group 3 patients with 14 days or more from onset of symptoms, the positivity rate was 91.1%. Conclusions: Our study shows that serologic rapid tests can be used as a complement of PCR to diagnose SARS-CoV-2 infection after 14 days from the onset of symptoms. These immunochromatographic devices could be especially useful in hospitalized patients with pneumonia of unknown etiology with 14 or more days from the onset of symptoms and in whom the PCR has been negative.
Predicting Whom to Test is More Important Than More Tests - Modeling the Impact of Testing on the Spread of COVID-19 Virus By True Positive Rate Estimation

I estimate plausible true positive (TP) rates for the number of COVID-19 tests per day, most relevant when the number of test is on the same order of magnitude as number of infected persons. I then modify a standard SEIR model to model current growth patterns and detection rates in South Korea and New York state. Although reducing transmission rates have the largest impact, increasing TP rates by ~10% in New York can have an impact equal to adding tens of thousands of new tests per day. Increasing both TP rates and tests per day together can have significant impacts and likely be more easily sustained than social distancing restrictions. Systematic and standardized data collection, even beyond contact tracking, should be ongoing and quickly made available for research teams to maximize the efficacy of testing.