Question: What are the longer-term complications of those who recover from COVID-19?

The COVID-19 pandemic has led to over 690,000 confirmed cases and 33,000 deaths worldwide as of March 30, 2020. Public health strategies to control the spread include mitigation and suppression, which focus on reducing interactions. A new approach, shield immunity, uses serological tests to identify and deploy recovered individuals with antibodies to SARS-CoV-2 as focal points for safer

A 78-year-old man with a history of atrial fibration on warfarin recovered from COVID-19 pneumonia and was discharged to a nursing home. One month later, he presented to the emergency department with worsening shortness of breath and cough, and was found to have multiple segmental pulmonary emboli despite being on warfarin and having a supratherapeutic INR. This case highlights the risk of delayed

A meta-analysis was conducted to identify laboratory findings predicting a poor prognosis in COVID-19 patients with diabetes. Diabetes is a significant comorbidity associated with a poor clinical outcome and high mortality rate in COVID-19 patients. The study aimed to determine which laboratory parameters distinguish patients with COVID-19 and diabetes who recovered from those who experienced complications or died.

The passage discusses the challenges and opportunities in recovering from the COVID-19 pandemic, focusing on older adults. Challenges include economic setbacks, health effects, and social issues like ageism, racism, and classism. Opportunities include increased technology use, stronger family connections, respect for self-care, awareness of advance directives, and potential interdisciplinary work on aging society. Ongoing efforts to improve policies and programs for longer, healthier lives may

The passage discusses the challenges and opportunities in recovering from the COVID-19 pandemic, specifically focusing on older adults. Challenges include economic setbacks, health effects, and social issues like ageism, racism, and classism. Opportunities include increased technology use, stronger family connections, self-care awareness, and advance directive importance. Ongoing efforts to improve policies and programs for aging populations may be more productive due to increased public awareness.

This case series reviews the functional status and complications of 14 patients who underwent allograft reconstruction after Type III pelvic resections, which involve the removal of a portion or entire pubis while sparing the hip. The study found that all patients remained ambulatory, but reported varying levels of pain and had operative complications including infection, hernia, hip instability, dislocated total hip arthroplasty, and graft failure. The study

The passage discusses the expected negative impact of the COVID-19 pandemic on behavioral health, including anxiety, depression, and post-traumatic stress symptoms, which may lead to increased alcohol or drug use. The authors propose a four-step services system to address behavioral health concerns, prioritizing prevention and limiting clinical services for extreme reactions. They suggest that interventions will need to change over time and be tailored to different groups, such as COVID-19 survivors, healthcare

This study compared the clinical characteristics of patients who died from COVID-19 and those who recovered from the disease in Wuhan, China. The death group had a older median age, more underlying diseases, longer time from illness onset to hospitalization, higher proportions of dyspnea and expectoration symptoms, lower blood oxygen saturation, higher white blood cell count, lower lymphocyte count and percentage, and higher levels of alanine transaminase, aspartate trans

This study investigated the significance of prophylactic drain placement after laparoscopic distal gastrectomy (LDG) for gastric cancer. Seventy-eight patients were retrospectively analyzed, divided into a 'drain group' with routine use of a prophylactic drain and a 'no-drain group' without drain placement. No significant differences were found between the groups in terms of patient demographics, tumor characteristics, or surg

A report details the cases of four newborns born to mothers who had recovered from COVID-19 during the third trimester of pregnancy at Parma Hospital in Italy. All babies were delivered at term and tested negative for congenital COVID-19 infection. The findings suggest that adverse effects on fetuses from pregnancies complicated by COVID-19 infection in late pregnancy are unlikely.

A report details four newborns born to mothers who recovered from COVID-19 during the third trimester of pregnancy at Parma Hospital in Italy. All infants were delivered at term and did not have congenital COVID-19 infection. The findings suggest that adverse effects on fetuses from pregnancies complicated by COVID-19 infection in late pregnancy are unlikely.

This study compared the clinical characteristics of 109 COVID-19 patients who died during hospitalization and 116 recovered patients in Wuhan, China. The death group had older age, more underlying diseases, longer time from illness onset to hospitalization, higher proportions of dyspnea and expectoration, lower blood oxygen saturation, higher white blood cell count, lower lymphocyte count and percentage, and higher levels of alanine transaminase, as

The passage reports on the characteristics of COVID-19 patients in Wuhan, China during the epidemic outbreak. Of the 403 patients studied, 100 died and 303 recovered. Most non-survivors were males, old, or had chronic diseases. The average time from illness onset to admission was 9 days, but patients with severe or critical illness had longer wait times. Common symptoms included lymphopenia, anemia, hyp

The passage discusses the rehabilitation needs of survivors of COVID-19, acknowledging the limited understanding of the disease's long-term outcomes. The author identifies three areas relevant to rehabilitation: patient presentation with comorbidities, complications, and effects on various body systems; design of inpatient rehabilitation units and staffing considerations; and proposed guidelines for physiotherapy, occupational therapy, and speech-language pathology to aid in respiratory

The passage discusses the rehabilitation needs of survivors of COVID-19, acknowledging the limited understanding of the disease's long-term outcomes. The author identifies three areas relevant to rehabilitation: patient presentation with comorbidities, complications, and effects on various body systems; design of inpatient rehabilitation units with staffing and outpatient considerations; and proposed guidelines for physiotherapy, occupational therapy, and speech-language pathology to aid in

The passage discusses how certain populations, including the elderly, underrepresented minorities, and those with underlying medical conditions, are at higher risk for severe COVID-19 outcomes. Consumption of diets high in saturated fats, sugars, and refined carbohydrates, known as Western diet, contributes to obesity and type 2 diabetes and increases the risk for severe COVID-19 pathology and mortality. These diets activate

A rapid review of studies on the impact of previous pandemics on grief and bereavement found that they caused multiple losses related to death and disruption to social norms, increasing the risk of complicated grief. No research specifically focuses on the bereaved during a pandemic, and innovative ways to promote connection and adapt rituals while maintaining respect are necessary for effective post-bereavement support. Strong leadership and coordination among bereavement organizations are essential.

The passage discusses how certain populations, including the elderly, underrepresented minorities, and those with underlying medical conditions, are at higher risk for severe COVID-19 outcomes. Consumption of Western diets, which are high in saturated fats, sugars, and refined carbohydrates, contributes to obesity and type II diabetes and increases the risk for severe COVID-19 pathology and mortality. These diets activate the immune

The passage discusses the current status of SARS-CoV-2 (COVID-19) infection with declining case numbers in China and some European regions, contradictory mortality data, and the main complication being pneumonia with ARDS. Asymptomatic and oligosymptomatic cases with virus shedding are common, especially in children, and an effective antiviral therapy and vaccine are not yet established. Preparation for

The passage emphasizes the need to address the negative consequences of Covid-19 beyond just reducing infections and treating the ill. It highlights the importance of identifying interventions that improve multiple outcomes simultaneously, or "accelerators," particularly in resource-constrained settings. These accelerators will be crucial for vulnerable groups, such as young children, adolescents, and those with critical needs in multiple areas. The recovery process from the epidemic will be challenging due to limited financial resources,

A retrospective study of 417 COVID-19 patients in Shenzhen identified significant differences in demographics, clinical symptoms, and lab tests between mild-moderate and severe-critical cohorts. A mathematical model was developed using age, BMI, CD4(+) lymphocytes, and IL-6 levels to predict illness severity, with an AUC of 0.911. Risk factors for severe COVID

This study investigated the clinical and laboratory features and short-term outcomes of 102 adult patients with Corona Virus Disease 2019 (COVID-19) admitted to Wuhan University Zhongnan Hospital in Wuhan, China between January 3 and February 1, 2020. The median age was 54 years and 48.0% were female. Thirty-four patients (33.3%) were exposed

The COVID-19 pandemic poses a significant challenge to the medical community, with an urgent need for effective pharmacologic treatments. At present, there are no licensed drugs for COVID-19, and ongoing clinical trials involve both repurposed drugs and new entities. A randomized controlled trial by Cao et al in Wuhan, China, tested lopinavir–ritonavir against standard care, showing no benefits. A single arm study on remdesevir

The passage discusses the role of innovation in recovering from the economic impact of Covid-19. The article also draws lessons from the response to the virus and their implications for managing innovation during the recovery process.

A rapid review of studies on the impact of previous pandemics on grief and bereavement found that they caused multiple losses beyond death itself, affecting the ability to connect with the deceased and increasing the risk of complicated grief. No research specifically focused on supporting the bereaved during a pandemic. Innovative ways to promote connection and adapt rituals while maintaining respect, along with strong leadership and coordination between bereavement organizations, are crucial for post-bereavement support.

A 79-year-old man with end-stage renal disease and cardiovascular disease was admitted to the hospital with a fever and dry cough, later diagnosed with COVID-19 pneumonia through RT-PCR. The patient was treated with oxygen, broad-spectrum antibiotics, antiviral drugs, methylprednisolone, and CRRT every 2 days. After 19 days, the patient tested negative for SARS-Co

A patient with refractory Myasthenia Gravis, undergoing long-term immunosuppressive therapies, contracted COVID-19 and recovered successfully after a 14-day quarantine without complications or changes to her treatment. Individualized care is essential for managing Myasthenia Gravis patients with COVID-19.

A patient with refractory Myasthenia Gravis, undergoing long-term immunosuppressive therapies, contracted COVID-19 during the pandemic. After a 14-day quarantine, she recovered without complications or need for changes to her immunosuppressive therapy. Treatment for Myasthenia Gravis patients with COVID-19 should be individualized.

The study aimed to assess the impact of complicated cholecystitis on the outcome of laparoscopic common bile duct exploration (LCBDE) during one-stage treatment for cholecystitis with CBD stones. Of the 117 patients who underwent LC + LCBDE between 2008 and 2011, 87 had uncomplicated cholecystitis, and 30 had complicated chole

A study was conducted to develop data mining models for predicting COVID-19 patient recovery using South Korea's epidemiological dataset. Six algorithms (decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor) were applied using Python. The decision tree model had the highest accuracy (99.85%) for predicting patient recovery from COVID-19. The models predicted minimum and maximum recovery days, high

This study compared clinical, laboratory, and chest CT findings between critically ill COVID-19 patients who survived and those who died. The deceased patients were older, had higher CRP and NLR levels, and more often had medial or parahilar area involvement and diffuse lung involvement. Common radiological findings included ground-glass opacities, crazy-paving pattern, and air bronchogram. Deceased patients were more likely to have diabetes and emphysema

This study compared clinical, laboratory, and chest CT findings between critically ill COVID-19 patients who survived and those who died. The deceased patients were older, had higher CRP and NLR levels, and more often had medial or parahilar area involvement and diffuse lung involvement. Common radiological findings included ground-glass opacities, crazy-paving pattern, and air bronchogram. Deceased patients were more likely to have comorbidities such as

This study analyzed the clearance time and factors influencing the persistence of 2019 novel coronavirus (2019-nCoV) RNA in different samples from patients with COVID-19. The median time from symptom onset to first negative RT-PCR result for oropharyngeal swabs was 9.5 days, but some patients still tested positive for viral RNA from stool specimens after a median duration of

A study of 253 patients with sudden olfactory dysfunction during the COVID-19 pandemic found that those who tested positive for COVID-19 had a lower rate of total recovery (52.6% vs. 70.3%) and took longer to achieve it (15 days vs. 10 days) than those who tested negative. Among positive-COVID-19 patients, those with hyposmia (partial loss of smell) were more

A single surgeon's initial experience with laparoscopic total gastrectomy (LTG) was evaluated in this study compared to open total gastrectomy (OTG). Between 2009 and 2013, 134 OTG and 74 LTG procedures were performed. Advanced gastric cancer and D2 lymph node dissection were more common in the OTG group. Although LTG had

A retrospective study of 113 deceased and 161 recovered patients with COVID-19 at Tongji Hospital in Wuhan, China, found that deceased patients were older (median age 68 years vs. 51 years), more likely male (73% vs. 55%), and had more chronic hypertension and cardiovascular comorbidities (48% and 14% vs. 24

This study analyzed the clearance time and factors influencing the persistence of 2019-nCoV RNA in different samples from patients with COVID-19. The median time for oropharyngeal swabs to test negative was 9.5 days, but some patients still tested positive for viral RNA in stool specimens after a median duration of 11 days and in urine specimens after throat swabs were negative. CD

A scoping review identified the needs of individuals recovering from their first episode of mental illness. These needs encompassed emotional, psychological, social, informational, functional, practical, and relationship aspects. While some assessment tools have been used, they were primarily designed for people with severe and persistent mental illness and may not fully capture the needs of first-episode patients. Four established tools were found, but they may not adequately reflect all discovered needs and were mostly developed for long-

A prospective study compared the unilateral axillo-breast approach (UABA) and modified anterior chest wall approach (MACWA) in endoscopic thyroidectomy (ET) for benign thyroid lesions. No significant difference was found in clinicopathologic characteristics. The surgical time was longer in UABA group but postoperative pain was lower. UABA had a higher rate of persistent paresthesia, neck contracture, and

This study analyzed the clearance time and factors influencing the persistence of 2019 novel coronavirus (2019-nCoV) RNA in different samples from patients with COVID-19. The median time from symptom onset to first negative RT-PCR result for oropharyngeal swabs was 9.5 days, but stool specimens still tested positive for viral RNA in 16.7% of

A study was conducted to determine the prevalence of disordered eating behavior (DEB) in a Danish cohort of children and adolescents with type 1 diabetes (T1D) and to characterize those with DEB regarding metabolic control and clinical data. The study included 192 participants aged 11-19 years with T1D, of whom 21% had DEB. The prevalence was higher among girls (34.

A study was conducted to determine the prevalence of disordered eating behavior (DEB) in a Danish cohort of children and adolescents with type 1 diabetes (T1D) and to characterize those with DEB regarding metabolic control and clinical data. The study included 192 participants aged 11-19 years with T1D, of whom 40 (21%) had DEB. The prevalence was higher among girls (

A study of 86 patients who underwent abdominal sacrocolpopexy for pelvic organ prolapse between 2014 and 2016 identified gastrointestinal complications and their causes. Prolonged hospital stays were experienced by 27 patients, primarily due to nausea and vomiting. Most recovered with hydration, stopping oral intake, and antiemetics. Three patients required nasogastric decom

The editorial discusses the future outlook for managing COVID-19 effectively, focusing on long-term strategies for controlling coronavirus infection.

A study compared the long-term results of laparoscopic sleeve gastrectomy (LSG) to those of gastric bypass surgeries using a matched group of patients. The LSG group had a shorter surgical time and less blood loss, with a major complication rate of 1.6% and an average hospital stay of 3.0 days. The LSG patients lost 28.3% of their initial body weight after 5 years

A 52-year-old renal transplant recipient, who had undergone the procedure 12 years prior, contracted COVID-19 pneumonia. His symptoms and lab results, along with chest CT scans, were similar to non-transplanted COVID-19 patients. Successful recovery was achieved through reduced immunosuppressant use and low dose methylprednisolone-based therapy. This case provides valuable insight for treating other trans

The study aimed to assess long-term functional outcomes and complications of continent cutaneous diversion using the Lundiana pouch. At 90 days post-surgery, 23% of patients experienced a complication of Clavien-Dindo grade ≥III. At a median follow-up of 13 years, 54% of patients had undergone at least one re-operation, with uretero-intestinal stricture being

A study was conducted to examine the adoption of robotic gastrectomy and compare outcomes between robotic and open gastric resections at Sanchinarro University Hospital from 2011 to 2017. The analysis was based on 20 robotic and 19 open surgery patients with similar demographics. Robotic resection resulted in less blood loss, a shorter hospital stay, and a longer operating time, while complication rates and pathological data

The study compared IgM and IgG levels against SARS-CoV-2 in symptomatic and recovered patients. Hospitalized patients had significantly higher IgG levels against the S1 receptor binding domain (RBD) within days of symptom onset, while those who recovered from milder COVID-19 reached similar levels after 28 days. IgM levels were elevated early in both groups, but decreased in hospitalized cases with longer disease

A retrospective study of 123 patients with lung oligometastases from colorectal cancer, non-small-cell lung cancer, hepatocellular carcinoma, esophageal cancer, and renal-cell carcinoma undergoing radiofrequency ablation was conducted to evaluate long-term survival. The median follow-up was 45.7 months, and the 5-year OS and RFS rates were 62%

A novel coronavirus, SARS-CoV-2, and the resulting disease COVID-19, have emerged in the 21st century, adding to the century's trend of RNA viruses causing pandemics and millions of deaths. The virus is continuing to spread with a moderate case fatality rate and lack of previous human exposure, raising concerns about its potential to become endemic. Continuous monitoring is necessary to prevent the selection of escape mutants and their disse

A novel coronavirus, SARS-CoV-2, and the disease COVID-19 emerged suddenly in the 21st century, causing a rapid outbreak with a moderate case fatality rate. Due to the lack of previous human exposure, there is concern about the virus's potential to establish itself as an endemic disease and acquire mutations that could infect individuals with pre-existing immunity. Continuous monitoring is necessary to prevent the selection of escape mutants and their disse

Twelve patients with fusiform vertebrobasilar aneurysms underwent Pipeline Embolization Device (PED) flow diversion at a single US center using novel strategies. Eleven patients had symptoms, one had a dissecting aneurysm. The average aneurysm size was 13.25 ± 4.5 mm, none were ruptured or previously treated. The average clinical and radiological follow-up durations were

The passage suggests that while the coronavirus outbreak may cause short-term damage to economic activity, a strong rebound is likely once disruption and uncertainty fade. Historical evidence from recessions and natural disasters supports this view, with most long-term impacts contained. However, there are risks to this view, including the potential for a longer period of disruption, policy actions, and vulnerabilities in the corporate sector and trade relations, which could have persistent effects on global activity.

This systematic review and meta-analysis aimed to assess the psychiatric and neuropsychiatric presentations of Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and Coronavirus Disease 2019 (COVID-19). The search identified 1963 studies and 87 preprints, of which 65 peer-reviewed studies and seven preprints met inclusion

This study evaluates the utility and technique of hand-assisted laparoscopic donor nephrectomy (HALDoN), which has been performed since 2006 for minimal invasion and patient safety. Among 72 donors from 2008-2011, all completed the procedure without conversion, and none experienced delayed graft function or ureteric problems. However, longer knife-to-removal times and right nephrect

The study aimed to assess the case fatality risk (CFR) of COVID-19 in mainland China, stratified by region and clinical category, and estimate key time-intervals. Data was collected from publicly available sources from December 29, 2019 to April 17, 2020. The estimated national CFR was 5.65% (95%CI: 5.50%-5.81%

A growing number of COVID-19 patients who tested negative after recovery later tested positive. SARS-CoV-2 RNA has been detected in semen and cerebrospinal fluid of severe COVID-19 patients. As immune privilege sites, testes and central nervous system could allow SARS-CoV-2 to hide, proliferate, and spread again. The possibility of SARS-CoV-2 presence, attack, and re-emerg

Healthcare workers have faced mental health challenges while treating COVID-19 patients, with previous pandemics providing insight into these issues. A rapid review of 40 qualitative studies identified eight themes transcending pandemics, time, and geographical boundaries, including fear, trauma, moral distress, burnout, stigma, social support, organizational support, and self-care. Learning from past experiences can help mitigate negative mental health effects and support the long-term wellbeing of

Ten renal transplant recipients with COVID-19 pneumonia and ten family members with the same condition served as a control group in this retrospective study. Immunosuppressant reduction and low-dose methylprednisolone therapy were used. The transplant recipients had more severe pneumonia, longer virus shedding, and longer illness duration. Five patients experienced transient renal allograft damage, and nine recovered. One patient died due

A retrospective case-control study was conducted to compare the outcomes of COVID-19 patients with ARDS requiring mechanical ventilation who received Tocilizumab treatment versus those who did not. The study group had a higher average HS score, indicating more severe inflammation, and higher levels of IL-6, triglycerides, AST, and ferritin. The length of stay was longer and survival rate was higher in the Tociliz

A study using a national inpatient database found that lower volume hospitals and surgeons performing robotic colorectal surgery had significantly more complications, longer length of stays, and higher costs compared to high volume providers. High volume providers had lower rates of postoperative bleeding and ileus. The results suggest that the majority of robotic colorectal procedures are being performed by lower volume providers, negatively impacting quality outcomes measures. It is recommended that robotics be limited to high

The demand for internationally comparable health estimates has increased due to new development goals and funding institutions, leading to debates about their validity and legitimacy. Global health estimates are influenced by social, economic, political, and ideational contexts and are often produced far from the localities where they will be used. While internationally standardized indicators are necessary, locally relevant and socially robust data are essential for effective use in health decision-making. Engaging local actors and building trust through

The passage discusses the search for a coronavirus cure using antibodies from people who have recovered from COVID-19. The antibodies, found in their blood, can be used to create various therapies, including convalescent plasma and monoclonal antibody therapies. Scientists believe these approaches could lead to novel therapies specifically designed to treat the infection faster than repurposing existing drugs. Multiple methods are being pursued to harvest and manufacture these antib

Intestinal/multivisceral transplantation has become the standard treatment for children with irreversible intestinal failure and complications from long-term parenteral nutrition. Indications include short bowel length, absence of ileocecal valve, colonic resection, and malabsorptive syndromes. Cholestasis from total parenteral nutrition increases the risk of transplant for those with persistent hyperbilirubinem

The COVID-19 pandemic occurred during economic uncertainties, deepening existing issues in the global economy. To recover, structural reform, new technology, and re-integration are crucial, supported by strong public policies and a new "global social contract."

A study was conducted to examine the impact of a longer alimentary limb length on weight reduction after Roux-en-Y gastric bypass (RYGB) surgery. The analysis included 730 patients who underwent primary laparoscopic RYGB between 2001 and 2011, with 360 receiving a short limb (100 cm) and 370 a long limb (15

This study investigated the characteristics and serological patterns of COVID-19 convalescent plasma donors to identify optimal donor selection criteria. The study found that S-RBD-specific IgG antibodies increased after 4 weeks from the onset of symptoms, with higher levels in donors who had fever exceeding 38.5°C or lasting longer than 3 days. Of the 49 convalescent plasma donors, 90%

The world is experiencing a disaster with COVID-19, caused by severe acute respiratory syndrome coronavirus 2, which has infected 1,773,084 people and killed 111,652 globally as of April 13, 2020. Underlying diseases like diabetes and hypertension increase the risk of severe disease or mortality from COVID-19. Patients with endocrine diseases such as diabetes m

The study investigated the shedding duration of SARS-CoV-2 RNA in upper (nasopharyngeal swab) and lower (sputum) respiratory tract specimens among 68 adult COVID-19 patients. The results showed that SARS-CoV-2 RNA was detected in sputum specimens for a significantly longer duration (median 34 days, IQR 24-4

A study conducted using a Delphi process identified factors important to patients when deciding to start treatment for hepatitis C (HCV). Treatment effectiveness, longer life, fear of complications, financial issues, quality of life, and impact on society were considered important. Patients prioritized fear of harming others over physical symptoms. Medication and doctor costs were the most significant costs for patients. The study highlights the importance of considering psychological and socioeconomic challenges beyond virologic cure

A retrospective study of 70 patients who developed pneumothorax after endoscopic lung volume reduction with valves found that 18% of patients experienced this complication. Most patients required chest tube insertion (87%), some required valve removal (44%), and a few underwent surgical intervention (19%). Despite the pneumothorax, patients showed modest but significant improvements in lung function parameters. Only 21

A retrospective study was conducted on Covid-19 in people living with HIV, revealing severe lymphopenia and decreased CD4+ T cell counts. Elevated inflammatory markers such as C-reactive protein, fibrinogen, D-dimer, interleukin-6, interleukin-8, and TNF-alpha were common. Out of 72 hospitalized individuals, 19 (26.

A study was conducted to examine the long-term growth effects of cryptosporidiosis in neonatal beef calves. Cryptosporidiosis, caused by Cryptosporidium spp., can lead to diarrhea, dehydration, and death in calves. The parasite's tough outer wall allows it to survive in moist conditions for several months and resist common farm disinfectants. While most cases are self-limiting

The COVID-19 pandemic has highlighted the relationship between social and economic inequality, health equity, and population health. To improve population health, economic recovery, and prepare for future pandemics, the essay suggests near-term and long-term interventions such as universal health insurance, paid sick leave, upgraded wage insurance policies, tax reform, investments in parental leave, child care, and education, and upgraded government record systems. These policies aim to reduce health disparities and promote social

The passage discusses the economic unsustainability of current social distancing measures to control COVID-19 in the US and the need for models to explore relaxation options. The authors present models inspired by information cascades, assuming containment of the virus in isolated locales is no longer possible. They predict COVID-19 contagion trends in different geographic regions under various policy scenarios and apply their model to over 30 highly impacted states in the US. Results can be found

A novel coronavirus, SARS-CoV-2, caused COVID-19 pandemic in December 2019 with respiratory symptoms. Patients can experience various degrees of illness from asymptomatic to respiratory failure with ARDS. A potential complication is pulmonary fibrosis, leading to chronic breathing difficulties, disability, and reduced quality of life. The cause is not fully understood but may be related to the immune response. Previous SARS

A retrospective study compared the incidence and severity of acute appendicitis (AA) before and during the COVID-19 pandemic in four high-volume centers in Jerusalem, Israel. The study identified 378 patients, with 237 in the pre-COVID-19 group and 141 in the COVID-19 group. The weekly incidence of AA decreased by 40.7% following the onset of COVID-

Community-acquired pneumonia (CAP), a global health issue, is linked to a substantial number of cardiovascular events, primarily arrhythmias, heart failure, and myocardial infarction. These events occur in all-cause CAP and CAP caused by influenza, and more recently, in COVID-19 infections. Research focuses on understanding the mechanisms of these cardiac events in pneumococcal CAP and COVID-19,

The study investigated the shedding duration of SARS-CoV-2 RNA in upper (nasopharyngeal swabs) and lower (sputum) respiratory tract specimens among 68 adult COVID-19 inpatients. The results showed that SARS-CoV-2 RNA was detected in sputum specimens for a significantly longer duration (median: 34 days, IQR:

A mathematical model is proposed to help countries decide when to re-open their economies during the COVID-19 pandemic. Factors to consider include healthcare capacity, population size, physical distancing measures, socio-economic disparities, lockdown regulations, and testing accuracy. The model suggests observing the total number of recoveries in relation to total cases to assess the risk of re-opening. The global surge in COVID-19 cases and resulting economic devastation highlight the

A study was conducted to analyze the impact of India's historic 21-day national lockdown on the total number of COVID-19 cases using epidemiological forecasting models and Bayesian estimation algorithms. The results showed that the lockdown prevented roughly 343,000 (up to 1.8 million) and 2.4 million (up to 38.4 million) COVID-19 cases nationwide compared to social distancing alone by

A retrospective study of 55 COVID-19 patients in northeast China who fully recovered analyzed their treatment processes. Fifty-three patients (96.36%) received antiviral therapy, with umifenovir for mild cases (median treatment: 14 days) and lopinavir/ritonavir for severe cases (median treatment: 17.5 days). Twenty-nine patients (52.72

A study was conducted to analyze the short-term outcomes of laparoscopic surgery for ileocecal Crohn's disease in obese patients. The study included 100 patients with a mean age of 39.7 years, of whom 42 were male. The overall complication rate was 20%, with three anastomotic leaks and seven reoperations within the first 30 days post-surgery. The median length of

The COVID-19 pandemic caused by SARS-CoV-2 is drawing scientific attention to potential long-term endocrine complications. Based on past experience with SARS-CoV, which caused SARS in 2003, this article explores the possible effects of SARS-CoV-2 on the endocrine system.

A retrospective study was conducted to understand the relevance of Person-Generated Health Data (PGHD) for early detection, monitoring, and management of COVID-19 using data from a large-scale digital participatory surveillance study. Three cohorts were derived: individuals diagnosed with flu before the pandemic, those diagnosed during the pandemic, and those diagnosed with COVID-19. Self-reported symptoms, demographic data, comorbidities, and physiological parameters from commercial

A qualitative systematic review was conducted to understand factors influencing ART adherence and engagement decisions among HIV-positive individuals in Africa. Ninety-five studies were included, revealing three main themes. First, HIV-positive people face numerous challenges, including poverty, social identity, stigma, and medical pluralism, which can hinder ART initiation and maintenance. Second, the healthcare system is perceived as uninviting and punishing, leading some individuals to disengage

During the COVID-19 pandemic in Spain, the number of Acute Care Surgery procedures significantly decreased in three tertiary care hospitals. The mean number of patients per day and hospital was 58.9% lower during the pandemic period. The time from symptoms onset to patient arrival at the Emergency Department was longer, and the types of surgical procedures changed. Acute cholecystitis and complications from previous elective procedures decreased, while bowel

A study conducted at Mashhad University of Medical Sciences, Iran, compared the demographic and clinical characteristics of 334 deceased and 733 recovered Covid-19 patients. The mean age of deceased patients was significantly higher than that of recovered patients, with the highest mortality rate observed in older age groups. Most patients in the age groups of 30-39 and 40-49 years recovered. The median duration of hospitalization was 9

The author expresses gratitude and compassion towards those supporting him and his wife during self-isolation, including frontline workers and family members. He extends compassion to communities affected by the COVID-19 pandemic and socio-political disasters, such as young women bee-keepers in Uganda. In the UK context, he expresses concern over funding cuts for public health services and their impact on the country's response to the pandemic. The author also reflects on changes in

The American Water Works Association (AWWA) celebrates its 139th birthday during the global COVID-19 pandemic. Water professionals are rising to the challenge by ensuring access to safe, reliable water, which is critical in halting the spread of the virus. Handwashing, a simple yet essential practice, has become a societal obsession due to its importance in personal hygiene and public health. Water utility workers and businesses are considered essential, and utilities are suspending

A case series study of 158 pregnant women with COVID-19 infection at two New York City hospitals from March 13 to April 19, 2020, found that 22% had moderate or severe disease. These women were more likely to have underlying medical comorbidities, asthma, leukopenia, elevated aspartate transaminase and ferritin, and symptoms of cough and chest pain or pressure. Ten

A case series study of 158 pregnant women with COVID-19 infection in New York City from March 13 to April 19, 2020, found that 22% had moderate or severe disease, with higher likelihood of underlying medical comorbidities, asthma, leukopenia, elevated aspartate transaminase and ferritin, and symptoms of cough and chest pain or pressure. Thirteen women required hospitalization

A study compared short-term outcomes of laparoscopic total gastrectomy (LTG) and open total gastrectomy (OTG) in gastric cancer patients. Seventy matched cases were analyzed. Although LTG operation time was similar to OTG, esophagojejunostomy time was longer in LTG (43 vs 14 min, P < .05) and anastomotic complications were more


Question: What new public datasets are available related to COVID-19?

This paper explores the use of transfer learning from non-COVID19 lung lesions to aid in the segmentation of COVID-19 infection areas in CT images. Using a publicly available COVID-19 dataset and three non-COVID19 datasets, the study evaluates four transfer learning methods with 3D U-Net as a standard encoder-decoder. The results demonstrate the benefits of transferring knowledge from non-COVID19 lung lesions and the

A dataset of over 10,000 news article links related to the Coronavirus pandemic in Swiss media, published since early January 2020, is released for mining and analysis. The collection aims to provide insightful information for further research on the Swiss media's reaction to the pandemic.

The passage introduces CoAID (COVID-19 Healthcare Misinformation Dataset), which aims to help researchers combat misinformation related to COVID-19. The dataset includes 1,896 news articles, 183,564 user engagements, 516 social platform posts, and ground truth labels. It is available at https://github.com/cuilimeng/CoAID. The spread of COVID-19 mis

A study was conducted to explore public opinions towards COVID-19 on social media by comparing sentiment and discussed topics between California and New York. A dataset of COVID-19-related tweets was collected from March 5, 2020 to April 2, 2020, and two individual datasets were created based on geolocation tags. Sentiment analysis revealed more negative sentiment scores in California tweets, while topic modeling identified "protective measures" as

The COVID-19 Open Research Dataset Challenge released a corpus of scholarly articles in response to the pandemic's accelerating literature growth. The challenge calls for machine learning approaches to help bridge the gap between researchers and publications. This study uses BERT and GPT-2, pre-trained NLP models, for text summarization on the dataset. Evaluation was done using ROUGE scores and visual inspection, resulting in abstractive and comprehensive summaries based on article keywords. These

This study explores the use of deep learning for early identification of COVID-19 from chest X-ray (CXR) images. The authors discuss the potential challenges and limitations, specifically the small size of available COVID data and the potential bias introduced by transfer-learning from larger non-COVID datasets. They present results from a medium-sized COVID CXR dataset collected during the pandemic peak in Northern Italy, contributing to the validation of preliminary results and the assessment of deep learning's ability

The Genome Detective Coronavirus Typing Tool is a web-based application that quickly and accurately identifies and characterizes SARS-related coronavirus (SARS-CoV-2) sequences from next-generation sequencing datasets. It can process up to 2000 sequences per submission and classify SARSr-CoV and SARS-CoV-2 with high accuracy. The tool allows tracking of new viral mutations and

A new open access Twitter dataset has been created, focusing on institutional and news media accounts related to COVID-19. The dataset includes 69 accounts, with 17 being government and international organizations, and 52 being news media from North America, Europe, and Asia. This data aims to increase availability for social science research on the global crisis.

A new system called POCOVID-Net was developed for automatic detection of COVID-19 using point-of-care ultrasound (POCUS) imaging. The dataset used consists of 1103 images from 64 videos, with 654 COVID-19, 277 bacterial pneumonia, and 172 healthy control cases. POCOVID-Net, a deep convolutional neural network, achieved an accuracy of

The Genome Detective Coronavirus Typing Tool is a web-based application that quickly and accurately identifies and characterizes SARS-related coronavirus (SARS-CoV-2) sequences from next generation sequencing datasets. It can process up to 2,000 sequences per submission and classify SARSr-CoV and SARS-CoV-2 with high accuracy. The tool allows tracking of new viral mutations and a

A new public health dataset and algorithm were developed for recognizing flu-like symptoms (sneeze and cough) from videos. Due to the high complexity and large variation of these actions, as well as numerous similar background actions, this is a challenging problem. The proposed algorithm uses two types of Action Matching Kernels to integrate space-time layout and Bag-of-Words representations. The approach achieves competitive performance compared to state-of-the-arts and is evaluated on both

This passage discusses the ongoing COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2, and the need to analyze data to understand the trend of disease spreading and death. The authors propose an algorithm to analyze the spread factor (f) and assess risk factors using datasets from ourworldindata.org. They compare the performance of vanilla, stacked, bidirectional, and multilayer Long Short Term Memory (LSTM) models for forecasting

The passage discusses the ongoing COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2, and the need to analyze data to understand the trend of disease spreading and death. The authors propose an algorithm to analyze the spread factor (f) and assess risk factors using datasets from ourworldindata.org. They compare the performance of vanilla, stacked, bidirectional, and multilayer Long Short Term Memory (LSTM) models for forecasting

A COVID-19 Instagram dataset has been made available by the researchers for the research community. The dataset, which includes multilingual content collected since March 30, 2020, aims to help understand the dynamics of the coronavirus pandemic's impact on Instagram as a major social media platform. It could also be useful in studying the propagation of misinformation related to the outbreak.

A research paper explores the information of 133 RNA viruses using mathematical and computational tools, including distance metrics, Kolmogorov complexity, and Shannon information. The paper discusses computational tools like hierarchical clustering and multidimensional scaling for identifying patterns in the data. The authors apply these methods to examine RNA data, comparing normalized compression distance, entropy, and Jensen–Shannon divergence in two and three dimensions. The findings

A new question answering dataset called CovidQA has been created specifically for COVID-19 content, with 124 question-article pairs as of the current version 0.1 release. The dataset was built manually using knowledge from Kaggle's COVID-19 Open Research Dataset Challenge. It is the first publicly available resource of its kind and can be used to evaluate the zero-shot or transfer capabilities of existing models on COVID-19 topics. The dataset's

The passage describes a study on developing an automated segmentation pipeline for identifying COVID-19 infected regions in chest CT scans using a 3D U-Net architecture. Due to limited publicly available COVID-19 imaging data, the method generates unique and random image patches through preprocessing and data augmentation to prevent overfitting. The study reports high accuracy and robustness with Dice similarity coefficients of 0.956 for lungs and 0.761 for

A collection of 31 transcriptome datasets from the NCBI Gene Expression Omnibus, related to viral respiratory tract infections and vaccinations, has been made publicly available for research investigators to study transcriptional responses. The datasets were retrieved from whole blood and peripheral blood mononuclear cells, and quality control checks were performed. Users can access the data through a custom web application, generate customized graphical views, and browse a single gene

A new method for automated theme-based visualization of COVID-19 research is presented in this paper. Using advanced data modeling, information mapping, and trend analysis, the authors developed a top-down and bottom-up browsing and search interface for quick discovery of research topics and resources. The method was applied to two COVID-19 publication datasets, revealing increased research efforts in social distancing, cross-domain initiatives, evolving medical topics, and the virus's trajectory in different

A study aimed to evaluate the use of convolutional neural networks (CNNs) for automatic detection of Covid-19 from X-ray images. Two datasets of X-ray images were used, including confirmed Covid-19 cases, common bacterial pneumonia, and normal conditions. Transfer learning was adopted to improve detection in small medical image datasets. The best results obtained were 96.78% accuracy, 98.66% sensitivity, and 9

The CoronaVis project aims to analyze tweets about the COVID-19 pandemic to understand topic modeling, subjectivity, and human emotions. The data, collected from tweets in the USA, can provide insights into public behavior and assist in managing the economic and social crisis during the pandemic. The project has generated data analytics over time and made a cleaned and processed dataset, named CoronaVis Twitter dataset, available to the research community to help find valuable insights and create applications to combat the pandemic and

Aim is to develop a medical dialogue system for COVID-19 consultations due to hospital closures and shortage of medical professionals. Two COVID-19 dialogue datasets, CovidDialog, were collected and used to train dialogue generation models based on Transformer, GPT, and BERT-GPT. Due to small dataset size, transfer learning was used to mitigate overfitting by finetuning pretrained models on CovidDialog datasets. Experiments showed promising results, but more advanced

A multilingual dataset of social media posts related to COVID-19, consisting of microblogs in English from Twitter, Japanese from Twitter, and Chinese from Weibo, has been released. The data covers posts from January 20, 2020, to March 24, 2020. The dataset is expected to help in efficient communication of precautions related to COVID-19 through text-mining analysis, such as daily word clouds

Researchers explored the COVID-19 Open Research Dataset (CORD-19) to investigate the virus's impact on people with intellectual disabilities. They identified 259 relevant articles, which they clustered into five topics: mental health, viral diseases, diagnoses and treatments, maternal care and paediatrics, and genetics. The CORD-19 dataset offers valuable information on both COVID-19 and intellectual disabilities, and researchers suggest

A new method is presented in this paper for summarizing and predicting time series data, specifically applied to COVID-19 data. The algorithm uses a hierarchical approach to generate shapelets, or patterns, from clustered data and assigns labels to individual time series based on nearest neighbor search with dynamic time warping. The labeled sequences allow for prediction of individual time series. The method is evaluated on two COVID-19 datasets, one for daily net cases and one for daily deaths

A study proposes using deep learning (DL) with chest X-Ray images for COVID-19 screening due to limitations of PCR tests. However, the availability of COVID-19 datasets is insufficient for training deep neural networks. The researchers introduce domain extension transfer learning (DETL) and use a pre-trained deep convolutional neural network on a large chest X-Ray dataset for classifying between normal, other disease, pneumonia, and COVID-19. The

A study proposes using deep learning (DL) with chest X-Ray images for COVID-19 screening due to limitations of PCR tests. However, the availability of COVID-19 datasets is insufficient for training deep neural networks. The researchers introduce domain extension transfer learning (DETL) and use a pre-trained deep convolutional neural network on a large chest X-Ray dataset for classifying normal, other disease, pneumonia, and COVID-19 cases. The

A new dataset called FakeCovid has been introduced, consisting of 5182 fact-checked news articles about COVID-19 collected from April 1, 2020, to May 15, 2020. The articles were sourced from 92 fact-checking websites and manually categorized into 11 types. The dataset is available in 40 languages from 105 countries. A classifier was developed to

This study analyzed transcriptomic data sets to identify potential factors contributing to the coagulopathies observed in COVID-19 patients. The researchers found that SARS-CoV-2 infection induces the activation of the extrinsic blood coagulation cascade and suppression of the plasminogen activation system in lung epithelial cells, which is unique to SARS-CoV-2 infection and not seen with influenza A virus. These findings suggest

A study used topic modeling analysis on coronavirus-related literature from the COVID-19 Open Research Dataset to identify research hotspots on COVID-19 and other coronavirus infections. Eight topics emerged: clinical characterization, pathogenesis research, therapeutics research, epidemiological study, virus transmission, vaccines research, virus diagnostics, and viral genomics. COVID-19 research focuses more on clinical characterization, epidemiological study, and virus transmission

This paper examines the relationship between media coverage and public response to the COVID-19 pandemic in Italy, United Kingdom, United States, and Canada. The study collects data from 227,768 online news articles and 13,448 YouTube videos from mainstream media, 107,898 user posts and 3,829,309 comments on Reddit, and 278,456,892

Vietnam, with its proximity to China and high volume of trade, was at risk for a COVID-19 outbreak. However, the country's prompt and effective response, with 239 confirmed cases and no fatalities as of April 4, 2020, is being studied for lessons on policy response, social media, and science journalism. A web crawl engine collected 14,952 news items, revealing Vietnam's political readiness and

CO-Search is a semantic search engine designed to help find scientific answers from COVID-19 literature during the pandemic. It uses a Siamese-BERT encoder with TF-IDF and BM25 vectorizers for retrieval, and a multi-hop question-answering module and multi-paragraph abstractive summarizer for ranking. The system was trained on a bipartite graph of document paragraphs and citations, and performed well on the TREC-CO

The coronavirus epidemic has led to an overwhelming amount of information on social media, including advertisements on Facebook from various sectors like public health, news media, politics, and business. These advertisements incorporate the virus into their messaging, with some instances of possible misinformation such as bioweapons conspiracy theories and unverifiable claims by politicians. The study aims to make the dataset available to the community to improve quality control for public health communication and public discourse.

The passage discusses the use of the K-Means clustering algorithm to identify spatial variations in COVID-19 epidemiological data and background characteristics. The analysis was conducted on country-specific data from the WHO COVID-19 situation report 107, including case fatality rates, positive cases per 10,000 population, proportion of population aged over 65, and percentage of GDP spent on public health. The algorithm grouped 8

A new named entity recognition (NER) dataset called CORD-NER has been created for the COVID-19 Open Research Dataset Challenge (CORD-19) corpus. This comprehensive dataset covers 75 fine-grained entity types, including common biomedical entities and new types related to COVID-19 studies. The annotation for this dataset is a combination of four sources using different NER methods, and its quality surpasses SciSpacy by over 1

The COVID-19 pandemic is causing extensive human suffering and leading to a large amount of new scientific research and data. Artificial Intelligence (AI) can be utilized to process this data and published papers to discover new insights and potential treatments for COVID-19, including accelerating drug discovery and vaccine development. AI can also help disseminate accurate information and reduce the spread of false information about the pandemic. The potential of AI in fighting COVID-19 is significant in saving lives and limiting economic

This study aims to correlate the rates of COVID-19 infection and total deaths with various factors including GDP, physicians, hospital beds, and testing rates. Data was collected from the WHO COVID-19 situation report and the World Bank database. The analysis was conducted using descriptive statistics in Jupyter notebook and XL-Stat software. The results showed a strong correlation between COVID-19 infection and total deaths (r: 0.89), a

The passage discusses the analysis of COVID-19 data in Western Europe, focusing on the number of deaths following infection. The data shows an exponential increase with varying absolute rates and time periods. Inspection of daily death increments reveals similarities to China's data and suggests that strict contact measures in Italy and Spain have led to a decrease in daily deaths since early April, with an estimated 40-50 days needed for the number to become negligible. Using data from Germany

Public health officials monitor public concern about epidemics using a two-step sentiment classification approach on Twitter messages. In the first step, Personal tweets are distinguished from News (Non-Personal) tweets. In the second step, Personal Negative tweets are separated from Personal Non-Negative tweets. Training data is generated using an emotion-oriented, clue-based method in the first sub-step of both steps. Three Machine Learning models are trained and tested in the second sub

The COVID-19 pandemic led to an unprecedented scientific response, with a large amount of academic information generated. This study analyzed the distribution and availability patterns of COVID-19 related academic information using data from PubMed, Rxiv preprint server, and Scopus. Preliminary results suggest potential issues with information velocity, availability, and collaboration in the early stages of the pandemic. Standard publication systems were overloaded, data availability was problematic, and collaboration appeared deficient. The

A new mutation of coronavirus, covid-19, declared a pandemic by WHO, is causing alarm and leading to increased social media activity in Bogota, Colombia. Text mining techniques are being used to analyze the symptom-related publications of Colombian residents within a 50 km radius to understand the spread of covid-19 in the area.

Aim is to develop a COVID-19 medical dialogue system due to high demand for online consultations and shortage of medical professionals. Two dialogue datasets, CovidDialog, collected for training. Used Transformer, GPT, and BERT-GPT models, leveraging transfer learning to mitigate data deficiency. Finetuned pretrained models on CovidDialog tasks. Generated responses were doctor-like, relevant, and clinically informative based on evaluation results. Data and code

A large-scale dataset of over 152 million COVID-19 related tweets from January 1st to April 4th is presented for open scientific research. This curated dataset will enable researchers to study emotional and mental responses to social distancing, identify sources of misinformation, and measure sentiment towards the pandemic in near real time. The integration of social dynamics data into biomedical, biological, and epidemiological analyses is necessary to fully understand the impact of the pandemic

A large-scale dataset of over 152 million COVID-19 related tweets from January 1st to April 4th is presented for scientific research. This curated dataset will enable researchers to study emotional and mental responses to social distancing, identify sources of misinformation, and measure sentiment towards the pandemic in near real time. The integration of social dynamics data into biomedical, biological, and epidemiological analyses is necessary to fully understand the impact of the pandemic.

The COVID-19 pandemic has led to an unprecedented volume of scientific publications. This research analyzes the daily growth of scientific production on COVID-19 in various databases and repositories, including Dimensions, Web of Science Core Collection, Scopus-Elsevier, Pubmed, and eight repositories. Dimensions has the most publications (9435) with a global growth rate of 500 per day and publications doubling every 1

A study using quantitative statistical analysis on COVID-19 data reveals significant relationships between the disease spread and country-level economic and health indicators. Four major country clusters have been identified based on COVID-19 development and economic and health factors. Potential under-reporting traits and economic factors affecting diagnosis, reporting, and treatment have been identified. The study describes four disease development scenarios and highlights limitations in reporting and measuring COVID-19, recommending further research.

A study examining anxiety and distress among the first quarantined community in the USA, which was a Modern Orthodox Jewish community, found that community organizations were trusted more than government agencies and media for COVID-19 information. Community organizations provided tangible needs, social support, virtual religious services, and disseminated information. Elevated distress and anxiety were linked to inadequate and inconsistent health-related information and concerns about future stigma. The study highlights the importance of religious institutions

The World Health Organization declared COVID-19 a pandemic in March 2020. Comorbidities, including hypertension, increase the susceptibility and risk of unfavorable outcomes in COVID-19 patients. Several studies have explored the relationship between COVID-19 and hypertension, discussing potential risks such as antihypertensive therapy and management approaches.

A new study presents a Classification-Aware Neural Topic Model (CANTM) to help combat the overwhelming amount of COVID-19 disinformation. The researchers developed the largest available manually annotated COVID-19 disinformation category dataset. CANTM combines classification and topic modeling under a variational autoencoder framework, improving classification performance with minimal resources and scalability. The classification-aware topics aid researchers and users in understanding the results.

This study summarizes the compilation and analysis of epidemiological data on the COVID-19 outbreak from January 13-31, 2020, using data from a Chinese health-care social network and global news media sources. The data included 507 patients with COVID-19, of whom 364 were from mainland China and 143 were from outside China. The median age was 46 years and most patients were male.

The passage introduces BIP4COVID19, an open dataset created to facilitate exploration of coronavirus-related literature by providing scientific impact indicators for relevant articles. The large volume of articles published since the pandemic began makes identifying useful knowledge a challenge. BIP4COVID19 aims to help by offering various impact measures and a publicly accessible web interface for easy exploration.

CoV-Seq is a new webserver designed to facilitate the analysis of SARS-CoV-2 genomes. It automatically predicts gene boundaries and identifies genetic variants, presenting them in an interactive genome visualizer and allowing for download for further analysis. The tool is implemented in Python and Javascript and is available at http://covseq.baidu.com/. The source code can be found on GitHub. The tool aims to make SARS-

The passage describes a study that proposes a Deep Learning model named CoroNet to detect and diagnose COVID-19 from chest X-ray images. The model is based on Xception architecture and trained on a dataset of COVID-19 and other chest pneumonia X-ray images. The experimental results show that CoroNet achieved an overall accuracy of 89.6%, with high precision and recall rates for COVID-19 cases. The model can be a helpful tool

The passage describes a study that proposes a deep learning model called CoroNet to detect and diagnose COVID-19 from chest X-ray images. With the rapid increase in COVID-19 cases and limited testing resources, the authors aim to help radiologists and clinicians with an automated solution. CoroNet is based on Xception architecture and trained on a dataset of COVID-19 and other chest pneumonia X-ray images from two public databases. The model achieved

A new COVID-19 CT dataset with 20 cases and over 1800 annotated slices has been made publicly available to promote the development of efficient deep learning methods for lung and infection segmentation. The dataset includes three benchmarks for current research interests such as few-shot learning, domain generalization, and knowledge transfer. Unified training, validation, and testing dataset splits, as well as evaluation metrics and code, have been provided for fair comparisons among different

This study analyzes age distribution patterns in six groups of Indian COVID-19 patient populations based on their likely geographical origin of infection. The results showed distinct age profiles among the groups, with some clusters forming differently between groups from Asia, the European Union, and the United Kingdom compared to those from North America, the Middle East, and other regions. Age profiles were found to be highly predictive of the group a population belongs to, and there may be differences in recovery and fatality rates across

A publicly available knowledge base, TweetsCOV19, contains over 8 million tweets about COVID-19 from October 2019 to April 2020. Metadata and extracted entities, hashtags, user mentions, sentiments, and URLs are semantically annotated using RDF/S vocabularies for various knowledge discovery tasks. The dataset facilitates research in sentiment analysis, event detection, and entity recognition,

A study was conducted to construct an enhanced COVID-19 dataset by processing two existing databases and using natural language processing methods to add local weather conditions and research sentiment. The dataset contains 183 samples and 43 features. Extreme Gradient Boosting (XGBoost) was applied to the enhanced dataset, achieving 95% accuracy in predicting patient survival. The most important factors were found to be country-wise research sentiment, age, and local weather.

A study aimed to develop a method using deep learning techniques for quick detection of COVID-19 in chest X-ray images. An object detection architecture was proposed, trained, and tested on a dataset of 1500 images of non-infected and infected patients with COVID-19 and pneumonia. The method achieved a sensitivity of 94.92% and specificity of 92.0% in COVID-19 detection using the SDD3

The NHS Pathways triage system data on self-reported potential COVID-19 cases in England from 18th March 2020 to 14th May 2020 are analyzed to reflect the ongoing epidemic trends and capture a broader severity profile of cases. The analysis shows that rates of growth/decline and effective reproduction number estimated from these data can be useful in monitoring transmission. The correlation between NHS Pathways reports and COVID-

A new publicly available database, COVID-19-CT-CXR, has been created, containing chest X-rays (CXR) and computed tomography (CT) scans of COVID-19 patients extracted from biomedical literature. The database includes 1,327 CT and 263 CXR images with associated text, and has been shown to improve deep learning performance for COVID-19 diagnosis. The dataset is available at https://github.com

An AI system named $\alpha$-Satellite has been proposed to provide hierarchical community-level risk assessment for the COVID-19 pandemic. The system uses large-scale real-time data from various sources to assess risk indexes for specific locations in a hierarchical manner (e.g., state, county, city). The system and benchmark datasets are publicly accessible on the developers' website. The goal is to help individuals make protective actions while minimizing disruptions

A study was conducted on the expression of ACE2 and TMPRSS2, the SARS-CoV-2 receptor and serine protease, in prostate epithelial cells. The analysis of a public prostate single-cell RNA sequencing dataset revealed co-expressing cells in epithelial cells, with a higher proportion in club and hillock cells. Similar findings were observed in lung epithelial cells. ACE2 expression was

A narrative review summarizes the current state of research on COVID-19, the virus behind the global pneumonia outbreak in Wuhan, China. The discovery of the virus raised public health concerns due to its high transmission ability and increasing death toll. This review analyzed over 20 scientific articles using keywords like "Coronavirus Outbreak" and "COVID-19" to provide insights into the research available and recommend future studies. The results indicate that human-to-human transmission

A dataset is presented to help policy makers respond to COVID-19 by providing data for mitigation and suppression strategies. The dataset, available at [https://github.com/JieYingWu/COVID-19\_US\_County-level\_Summaries], includes county-level time-series data from the JHU CSSE COVID-19 Dashboard and over 300 variables on population, demographics, housing, education

A study was conducted to develop a deep learning model, CovIDNet, for detecting Covid-19 related pneumonia from chest X-ray images. The model achieved high accuracy (98.44%) in recognizing Covid-19 and other classes during validation using open-source data, with 100% precision for Covid-19 and 97% precision for others. The authors noted that the model's accuracy might decrease slightly when classifying Covid

This study aims to assess the effectiveness of lockdown measures in reducing new COVID-19 infections across countries over time. Using a quantitative panel analysis, the results indicate that lockdowns are effective in reducing new cases, particularly within the first 10-20 days of implementation. The study concludes that lockdowns continue to reduce the R0 (number of people infected by each infected person) even after 20 days.

An open-source COVID-CT dataset containing 349 COVID-19 CT images from 216 patients and 463 non-COVID-19 CTs was built to address the scarcity of publicly available COVID-19 CT datasets for AI-powered diagnosis research. The dataset was confirmed useful by a senior radiologist and led to the development of diagnosis methods based on multi-task learning and self-supervised learning, achieving an F1 score of

A new SIR-type model for COVID-19 is presented in this paper, which integrates direct and fomite transmission, as well as the impact of various household structures. The study explores the effect of expanding physical distancing and strengthening mass control measures, such as public curfews, lockdowns, workplace clearances, and school closures, on reducing the number of active cases through parameter estimation.

A cascaded learning strategy is proposed for a comprehensive AI-based screening platform to predict COVID-19 infection from chest x-ray images. Due to the limited availability of COVID-19 CXR images, a large non-COVID-19 pneumonia dataset is used to improve the sensitivity and specificity of the deep neural network (DNN) model. The cascaded learning scheme generalizes the original well-trained model, resulting in good classification performance on both the

A citizen science initiative was launched in Kerala, India to process and visualize COVID-19 data from government bulletins and media outlets. The Department of Health Services initially released unstructured daily updates, which were refined and enriched to create reusable datasets. These datasets were used to provide real-time analysis and daily updates through a user-friendly bilingual dashboard. The data was deposited in a public repository for future analytical efforts, and the initiative

A new Bayesian approach is presented for inferring the transmission dynamics of COVID-19 using a probabilistic compartmental model with data of daily confirmed cases. This method extends the classical susceptible-infectious-recovered model by accounting for undocumented infections and allowing epidemiological parameters to vary over time. The disease transmission rate is estimated using a Gaussian process prior, which captures nonlinear changes without specific parametric assumptions. The algorithm utilizes parallel-temper

The study aimed to predict the COVID-19 pandemic outcomes using artificial intelligence (AI) based Recurrent Neural Networks (RNNs) with Long Short Term Memory (LSTM) units. Data from the World Health Organization and Johns Hopkins University was used to create the training dataset. Two prediction models were developed, and the predicted values were compared with observed data using root mean squared logarithmic errors (RMSLE). The results indicated that the COVID-19

The passage discusses the global concern over the COVID-19 outbreak caused by the SARS-CoV-2 virus, and the need for accurate and timely epidemiological data to guide intervention strategies. The article presents an exploratory data analysis approach using the first publicly available COVID-19 Daily Dataset of Bangladesh from IEDCR, WHO, DGHS, and MHFW. The analysis used Visual Exploratory Data Analysis (V-EDA

The Covid-19 coronavirus continues to spread globally, with Germany reporting varying numbers of infected individuals by federal land, city, age group, and date. Researchers are analyzing the Covid-19 Tracking Germany dataset to identify differences in morbidity across Germany. The study aims to cluster regions based on the gravity of the current situation and considers the potential connection between BCG vaccination and coronavirus morbidity. However, the dataset lacks sufficient information for definitive conclusions

Researchers have used Weibo, China's largest social media platform, to analyze COVID-19-related public opinion information and track changes in public opinion during different stages of the pandemic. A large collection of sentiment-oriented microblogs was analyzed using a sentiment classifier to identify and group users' opinions. The study found that public opinions varied based on key factors such as users' locations and education levels. Empirical results showed that public attitudes towards staying at home and quarantine also

The COVID-19 pandemic produces vast amounts of daily incidence and mortality data, which can be analyzed to understand disease trends and public health responses. Both daily new cases and mortality data in the US exhibit periodic oscillatory patterns. Analyzing testing data in NYC and LA shows that oscillation in cases is mainly due to testing practices, not increased infections on certain days. The apparent oscillation in mortality is mostly an artifact of reporting and disappears in datasets that record

A study conducted during the first two weeks of the COVID-19 lockdown in Tunisia aimed to determine the impact of the lockdown on consumer awareness, attitudes, and behaviors related to food wastage. The survey included 284 respondents, 89% of whom were aware of food waste and believed the lockdown would impact their waste levels and grocery shopping habits. Despite this, 85% of respondents declared they would not discard any purchased food,

A new deep learning algorithm is proposed for the automated diagnosis of COVID-19 using chest CT images, addressing the need for a quick and accurate diagnostic system due to the global pandemic. The method uses contrastive learning to train an encoder on large lung datasets and adopts a prototypical network for classification, requiring only a few samples for training. The proposed model outperforms competing methods on two publicly available and annotated COVID-19 CT datasets.

A new data-driven approach called Conditional Predictive Informativity (CPI) is presented for identifying strong warning signs of diseases, specifically focusing on COVID-19 in the current pandemic environment. This methodology allows for the enumeration of questions regarding informative combinations of symptoms and traits to help medical analysis and potentially individual diagnosis. The process is applied to data from various health reports and online sources, which is publicly available on Github.

A new data-driven approach called Conditional Predictive Informativity (CPI) is presented for identifying strong warning signs of diseases, specifically focusing on COVID-19 in the current pandemic. This methodology allows for the enumeration of informative questions regarding symptom combinations and helps prioritize stronger warning signs for disease diagnosis. The CPI ranking is applied to data from various health reports and online sources, which is publicly available on Github.

The COVID-19 pandemic has highlighted health disparities and weaknesses in healthcare and political systems, leading to increased mental health concerns including suicide, which has higher rates than in the past 50 years. Historical pandemics have had downstream consequences including anxiety, depression, and suicidal behaviors. Both frontline workers and the public may experience long-term stress and trauma from the virus's potential fatalities and loss of loved ones.

A new open source deep learning model called COVID-Net and dataset called COVIDx are introduced for detecting COVID-19 cases from chest X-ray images. COVID-Net is one of the first open source network designs for this purpose and is accompanied by COVIDx, a large open access dataset of 13,975 CXR images with 13,870 patient cases, including the largest number of publicly available COVID-19 positive cases. The study also investig

A study was conducted on open access (OA) publications and social media attention they receive compared to non-OA papers in the context of COVID-19 literature. The analysis used a dataset of 11,686 publications from Dimensions and found that 67.5% of them were openly accessible. OA publications received the most social media attention, with 37.6% being free due to toll journals providing access. MedRxiv contributed to a

The paper introduces a new method called FS-opa (Feature Sensitivity criterion-based sampling strategy from Optimization based on Phylogram Analysis) for analyzing large digital health datasets. FS-opa uses a criterion-based sampling strategy and can automatically identify principal features without requiring expert knowledge. It was evaluated using mental disorder electronic health records in Brazil and integrated with Cox regression for survival analysis. FS-opa can mine entire datasets

The passage describes an observational study called EAVE II aimed at monitoring the progress of the COVID-19 epidemic in Scotland and evaluating the effectiveness of therapeutic interventions, vaccines, and antimicrobials against SARS-CoV-2 infection. The study will use a prospective observational cohort of approximately 5.4 million individuals and a national linked dataset of patient-level primary care, out-of-hours, hospitalization

A meta-analysis using the Meta-MUMS tool compared the incidence, mortality, and recovery rates of COVID-19 and SARS infectious diseases in 17 countries with COVID-19 infections and 8 countries with SARS infections. The analysis, which used a random-effects model, found that the incidence rate of COVID-19 infections was higher than SARS infections, but the recovery and mortality event rates

iBioProVis is an interactive tool for visualizing the compound bioactivity space of target proteins using t-Distributed Stochastic Neighbor Embedding (t-SNE) and Principal Component Analysis (PCA). It takes target protein identifiers and optional compound SMILES as input, and plots the distribution of compounds in a 2-D map based on their structural similarity and cognate targets. Similar compounds are embedded near each other on the

The passage summarizes an article aimed at trainee audiences, discussing the relationship between COVID-19 and headaches. The article explains that COVID-19, caused by the novel coronavirus, is associated with neurological symptoms, including headaches. The pandemic has led to changes in clinical practice, such as increased use of telemedicine due to limitations on in-person visits. The article explores possible mechanisms of headache in COVID-19 patients, including indirect, potential

Labs and companies are developing antibody tests for COVID-19 to determine the true number of cases and potential immunity to new infections. However, large-scale data from these tests, such as the percentage of immune individuals in hard-hit areas like Wuhan, China, is currently lacking or not publicly available. Scientists anticipate this information will become available as more tests become accessible.

A new deep learning-based method called CoroNet is proposed for identifying COVID-19 from chest X-ray images. The method uses a semi-supervised learning approach with AutoEncoders to extract infected legions in chest X-rays and extract relevant features for each class (healthy, non-COVID pneumonia, and COVID-19). The deep architecture is then used to train a classifier for automatic diagnosis. The semi-supervised approach allows efficient

A group of volunteers addressed the issue of inaccessible and outdated information about South African healthcare facilities in response to the COVID-19 pandemic. They focused on improving data quality, completeness, source validation, and visualization to create a comprehensive mapping and data platform for the public and healthcare workers. The project aims to develop a Progressive Web Application for easy access to updated information on COVID-19 hospital bed capacity, locations of designated facilities, and other related data.

The passage summarizes a study that used a dynamic model to forecast the trend of the COVID-19 epidemic in Wuhan, China after the quarantine is lifted. The study projects a declining trend of the epidemic with 65,733 total infections and 2,821 deaths if the current quarantine strategy continues. To prevent a second outbreak, early lifting of the quarantine on March 21 requires a high facial mask usage of 85

The passage describes a study where authors used a multilayer perceptron (MLP) artificial neural network (ANN) to model the spread of COVID-19 infections using a publicly available dataset. The dataset contained information on infected, recovered, and deceased patients in 406 locations over 51 days. The dataset was transformed into a regression dataset and used to train the MLP with hyperparameters varied using a grid search algorithm. The best models had