Pandemic News Links / Current News Updates

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Limiting gatherings could significantly reduce the number of infections

11/3/20 ... tions.aspx

There have been many documented cases of Covid-19 "super-spreading" events, in which one person infected with the SARS-CoV-2 virus infects many other people. But how much of a role do these events play in the overall spread of the disease? A new study from MIT suggests that they have a much larger impact than expected.

The study of about 60 super-spreading events shows that events where one person infects more than six other people are much more common than would be expected if the range of transmission rates followed statistical distributions commonly used in epidemiology.

Based on their findings, the researchers also developed a mathematical model of Covid-19 transmission, which they used to show that limiting gatherings to 10 or fewer people could significantly reduce the number of super-spreading events and lower the overall number of infections.

" Super-spreading events are likely more important than most of us had initially realized. Even though they are extreme events, they are probable and thus are likely occurring at a higher frequency than we thought. If we can control the super-spreading events, we have a much greater chance of getting this pandemic under control."

- James Collins, Study Senior Author, Termeer Professor of Medical Engineering and Science, Institute for Medical Engineering and Science, Department of Biological Engineering, Massachusetts Institute of Technology

MIT postdoc Felix Wong is the lead author of the paper, which appears this week in the Proceedings of the National Academy of Sciences.

Extreme events

For the SARS-CoV-2 virus, the "basic reproduction number" is around 3, meaning that on average, each person infected with the virus will spread it to about three other people. However, this number varies widely from person to person.

Some individuals don't spread the disease to anyone else, while "super-spreaders" can infect dozens of people. Wong and Collins set out to analyze the statistics of these super-spreading events.

"We figured that an analysis that's rooted in looking at super-spreading events and how they happened in the past can inform how we should propose strategies of dealing with, and better controlling, the outbreak," Wong says.

The researchers defined super-spreaders as individuals who passed the virus to more than six other people. Using this definition, they identified 45 super-spreading events from the current SARS-CoV-2 pandemic and 15 additional events from the 2003 SARS-CoV outbreak, all documented in scientific journal articles.

During most of these events, between 10 and 55 people were infected, but two of them, both from the 2003 outbreak, involved more than 100 people.

Given commonly used statistical distributions in which the typical patient infects three others, events in which the disease spreads to dozens of people would be considered very unlikely.

For instance, a normal distribution would resemble a bell jar with a peak around three, with a rapidly-tapering tail in both directions. In this scenario, the probability of an extreme event declines exponentially as the number of infections moves farther from the average of three.

However, the MIT team found that this was not the case for coronavirus super-spreading events. To perform their analysis, the researchers used mathematical tools from the field of extreme value theory, which is used to quantify the risk of so-called "fat-tail" events.

Extreme value theory is used to model situations in which extreme events form a large tail instead of a tapering tail. This theory is often applied in fields such as finance and insurance to model the risk of extreme events, and it is also used to model the frequency of catastrophic weather events such as tornadoes.

Using these mathematical tools, the researchers found that the distribution of coronavirus transmissions has a large tail, implying that even though super-spreading events are extreme, they are still likely to occur.

"This means that the probability of extreme events decays more slowly than one would have expected," Wong says. "These really large super-spreading events, with between 10 and 100 people infected, are much more common than we had anticipated."

Stopping the spread

Many factors may contribute to making someone a super-spreader, including their viral load and other biological factors. The researchers did not address those in this study, but they did model the role of connectivity, defined as the number of people that an infected person comes into contact with.

To study the effects of connectivity, the researchers created and compared two mathematical network models of disease transmission. In each model, the average number of contacts per person was 10. However, they designed one model to have an exponentially declining distribution of contacts, while the other model had a fat tail in which some people had many contacts.

In that model, many more people became infected through super-spreader events. Transmission stopped, however, when people with more than 10 contacts were taken out of the network and assumed to be unable to catch the virus.

The findings suggest that preventing super-spreading events could have a significant impact on the overall transmission of Covid-19, the researchers say.

"It gives us a handle as to how we could control the ongoing pandemic, which is by identifying strategies that target super-spreaders," Wong says. "One way to do that would be to, for instance, prevent anyone from interacting with over 10 people at a large gathering."

The researchers now hope to study how biological factors might also contribute to super-spreading.


Massachusetts Institute of Technology

Journal reference:

Wong, F & Collins, J, J (2020) Evidence that coronavirus superspreading is fat-tailed. Proceedings of the National Academy of Sciences (PNAS).
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Review: One in five COVID-19 patients may only show gastrointestinal symptoms

11/3/20 ... ptoms.aspx

Almost one in five patients with COVID-19 may only show gastrointestinal symptoms, according to a review of academic studies published in the journal Abdominal Radiology. The findings of the review suggest abdominal radiologists need to remain vigilant during the pandemic while imaging patients.

Gastrointestinal symptoms associated with COVID-19 vary widely but can include loss of appetite, nausea, vomiting, diarrhea and generalized abdominal pain. The researchers who conducted the review report that 18 per cent of patients presented with such symptoms, while 16 per cent of COVID-19 cases may only present with gastrointestinal symptoms.

" There's a growing amount of literature showing that abdominal symptomatology is a common presentation for COVID-19."

- Mitch Wilson, radiologist and clinical lecturer in the University of Alberta's Faculty of Medicine & Dentistry

The researchers, who also included Gavin Low, associate professor of radiology and diagnostic imaging, and medical student Kevin Lui, examined findings from 36 studies published through July 15 to reach their conclusions.

In addition to gastrointestinal symptoms, they also determined potential signs radiologists should look for while conducting abdominal imaging that could be evidence of COVID-19 infection. Those signs include inflammation of the small and large bowel, air within the bowel wall (pneumatosis) and bowel perforation (pneumoperitoneum). The signs are quite rare, said the researchers, and could indicate patients with advanced disease.

"Seeing these things is not necessarily telling us a patient has COVID-19," said Wilson. "It could be from a variety of potential causes. But one of those potential causes is infection from the virus, and in an environment where COVID-19 is very prevalent, it's something to consider and potentially raise as a possibility to the referring physician."


University of Alberta Faculty of Medicine & Dentistry

Journal reference:

Lui, K., et al. (2020) Abdominal imaging findings in patients with SARS-CoV-2 infection: a scoping review. Abdominal Radiology.
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Living with children does not increase adults’ risk of severe COVID-19, say researchers

11/4/20 ... chers.aspx

Researchers working on behalf of NHS England have found no evidence that adults who live with school-age children are at any increased risk of severe outcomes following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) – the agent that causes coronavirus disease 2019 (COVID-19).

The large population-based study was conducted by researchers from the London School of Hygiene and Tropical Medicine (LSHTSM), the University of Oxford and The Phoenix Partnership, to investigate the growing concern that children may serve as a major reservoir for the spread of SARS-CoV-2.

“This is the first population-based study to investigate whether the risk of recorded SARS-CoV-2 infection and severe outcomes from COVID-19 differ between adults living in households with and without school-aged children during the UK pandemic,” writes the team.

Among working-age adults (aged 65 years or younger), living with children aged 0 to 11 years was not associated with any increased risk for SARS-CoV-2 infection or COVID-19-related hospitalizations, compared with working-age adults who did not live with children.

Among this age group, living with children aged 12 to 18 years was associated with a slightly increased risk of infection, but not with COVID-19 outcomes. In addition, living with children of any age lowered the risk of deaths due to causes unrelated to COVID-19.

Among adults older than 65 years, no associations were identified between living with children and any outcomes related to SARS-CoV-2.

Laurie Tomlinson and colleagues say the findings have implications for determining the benefits versus harms of children attending school while the pandemic continues.

A pre-print version of the paper is available on the server medRxiv*, while the article undergoes peer review.

The role children play in transmission is unclear

Modeling studies of other respiratory tract infections have pointed to children as a major source of spread during the initial phase of an epidemic, partly due to their frequent engagement in social contacts.

However, a growing body of evidence suggests that in the case of SARS-CoV-2, children may be less susceptible, less infectious, and no more likely to transmit the virus than adults.

One suggested mechanism for a lower susceptibility among children is cross-reactive immunity to SARS-CoV-2, acquired through infection with seasonal human coronaviruses (hCoVs). These infections, which cause the common cold, are more frequent among children than among adults, with the highest infection rates observed among young children.

“If recent hCoV infection is protective against SARS-CoV-2 infection or COVID-19, then adults living with children may be at a lower risk than those living without children,” say Tomlinson and team.

On the other hand, children may introduce SARS-CoV-2 to into their households and adults living in close contact with children may be at an increased risk of infection, they add.

“In the face of increasing transmission in many countries and the need for policy decisions about school opening, quantifying the overall impact of living with children on the risk of SARS-CoV-2 infections and severe outcomes from COVID-19 is important,” write the researchers.

What did the team do?

Tomlinson and colleagues used UK electronic health records linked to data on household members to investigate whether the risk of SARS-CoV-2 infection and severe COVID-19 outcomes differed between adults who live with children and those who do not.

The study used primary care data and linked information on hospital and intensive care unit (ICU) admissions and death records from patients registered with general practices, representing 40% of England.

The team used multivariate Cox regression to calculate the risk of outcomes between February 1st and August 3rd, 2020, after adjusting for sex, age, index of multiple deprivations, body mass index, smoking status, ethnicity, and number of adults in the household.

The final cohort included 9,157,814 working-age adults (65 years and younger) and 2,567,671 adults aged over 65 years.

What did the researchers find?

Among working-age adults, sharing a house with children aged 0 to 11 years was not associated with any increased risk for recorded SARS-CoV-2 infection or COVID-19-related hospital or ICU admission. However, the team did observe a 25% reduced risk of COVID-19-related death.

Among the same age group, living with children aged 12 to 18 years was associated with an 8% increased risk of recorded SARS-CoV-2 infection, but not any increased risk of other COVID-19 outcomes. Living with children of any age was also associated with a reduced risk of death from causes unrelated to COVID-19.

Among adults older than 65 years, there was no evidence of an association between living with children and any outcomes related to SARS-CoV-2, irrespective of the children’s age group.

In all analyses, additional adjustment for comorbidities did not significantly change the results and no consistent differences in risk of infection or severe outcomes were observed on comparing periods before and after school closures.

The findings have important implications

“Our results demonstrate no evidence of serious harms from COVID-19 to adults in close contact with children, compared to those living in households without children,” write the researchers.

“These findings, in consideration alongside other evidence, have implications for determining the benefit-harm balance of children attending school in the COVID-19 pandemic,” concludes the team.

*Important Notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:

Tomlinson L, et al. Association between living with children and outcomes from COVID-19: an OpenSAFELY cohort study of 12 million adults in England. medRxiv, 2020. doi:, ... 20222315v1
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Case study reports prolonged infectious SARS-CoV-2 shedding from an asymptomatic leukemia patient

11/4/20 ... tient.aspx

The majority of people infected with SARS-CoV-2 appear to actively shed infectious virus for about 8 days, but there is a wide range of variability from person to person. Understanding how long people can remain actively infected is important, because it provides new details about a disease and a virus that are still not well understood and informs public health decisions. Researchers report November 4 in the journal Cell an unusual case of one woman with leukemia and a low antibody count who was infected with the coronavirus for at least 105 days, and infectious for at least 70, while remaining asymptomatic the entire time.

" At the time we started this study, we really didn't know much about the duration of virus shedding. As this virus continues to spread, more people with a range of immunosuppressing disorders will become infected, and it's important to understand how SARS-CoV-2 behaves in these populations."

- Vincent Munster, senior author, virologist at the National Institute of Allergy and Infectious Diseases

Munster, an expert in emerging infectious diseases, began publishing research on SARS-CoV-2 in January. He was contacted in April by infectious disease specialist Francis Riedo, a study co-author, about a patient in Kirkland, Washington, who had been infected very early in the COVID-19 pandemic. Riedo's patient had had numerous positive PCR tests for the virus over a period of weeks, and he wanted to know if she was still shedding virus.

The patient, a 71-year-old woman, was immunocompromised due to chronic lymphocytic leukemia and acquired hypogammaglobulinemia. She never showed any symptoms of COVID-19. She was found to be infected with the virus when she was screened after being admitted to the hospital for severe anemia and her doctors recognized that she had been a resident of a rehabilitation facility experiencing a large outbreak.

Munster's lab at NIAID's Rocky Mountain Laboratories in Hamilton, Montana, began studying samples that were regularly collected from the patient's upper respiratory tract. They found that infectious virus continued to be present for at least 70 days after the first positive test, and the woman didn't fully clear the virus until after day 105. "This was something that we expected might happen, but it had never been reported before," Munster says.

The investigators believe the patient remained infectious for so long because her compromised immune system never allowed her to mount a response. Blood tests showed that her body was never able to make antibodies. At one point she was treated with convalescent plasma, but Munster doesn't think the treatment had an effect because of its low concentration of antibodies. Despite her inability to mount an antibody response, she never went on to develop COVID-19.

The team performed deep sequencing on all the virus samples obtained from the patient to see how the virus might have changed over the course of the patient's infection. Samples collected at various times displayed different dominant gene variants. However, the investigators don't think that these mutations played a role in how long the virus persisted, because they saw no evidence of natural selection. Selection would have been implicated if one of the variants had appeared to provide the virus with a survival benefit and had become the dominant variant, but none of them did. They also tested whether or not the mutations affected the ability or speed of the virus to replicate and found no differences.

Munster says that as far as he knows, this is the longest case of anyone being actively infect-ed with SARS-CoV-2 while remaining asymptomatic. "We've seen similar cases with influenza and with Middle East respiratory syndrome, which is also caused by a coronavirus," he notes. "We expect to see more reports like ours coming out in the future."


Cell Press

Journal reference:

Avanzato, V.A., et al. (2020) PCase Study: Prolonged infectious SARS-CoV-2 shedding from an asymptomatic immunocompromised cancer patient. Cell.
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Novel approach for modeling spread of infectious disease

11/4/20 ... sease.aspx

Nearly 48 million people worldwide have been infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes COVID-19 disease. The virus spreads rapidly from person to person, and since its emergence in late December 2019, the dynamics of its spread has been studied extensively.

Researchers M. Shayak from the Department of Theoretical and Applied Mechanics, Mechanical and Aerospace Engg, Cornell University, New York State, and Mohit Sharma from the Department of Population Health Sciences, Weill Cornell Medicine, New York State, USA, delved into the dynamics of the spread of infectious diseases in the context of the current pandemic. Their study titled, "A New Approach to the Dynamic Modeling of an Infectious Disease," was released pre-publication on the medRxiv* server.

Background and purpose of research

The rapid spread of SARS-CoV-2 across the world despite the early shutdown of borders and the cessation of international travel has sparked interest and given rise to changes in knowledge regarding the spread of infections. The research duo feels that mathematical modeling remains the "only scientific tool that allows us to predict the disease's trajectories in advance and take intervention measures accordingly."

According to the researchers, there are four approaches for such modeling. These include:

Lumped parameter or compartmental model used over a century earlier to model the spread dynamics of plague - this study used this model.
Agent-based model - considers the individuals in a population as "lattice sites on a network." Used by the London School of Hygiene and Tropical Medicine, Imperial College, and Los Alamos National Laboratory.
Stochastic differential equation model – combines the features of both the above methods. Examples include the Cornell University model and the Jadavpur University model.
Data-driven model – Takes the existing data on the spread of COVID-19 over the past week or month and uses machine learning to generate a prediction or forecast of spread.

The baseline mode

In the baseline model, the researchers present the derivation and solutions of the baseline model. The team assumes permanent immunity i.e., all recovered cases are insusceptible to further infection for all time. The population of cases divided into three parts.

contact traced cases
untraced symptomatic cases
untraced asymptomatic cases

This study showed that the baseline model itself was capable of generating a diverse range of epidemic trajectories. These match the course of the pandemic seen around the world in real-time, they wrote. They call this baseline model "realistic" and advantageous when compared to conventional lumped parameter models. The team wrote, "Henceforth, we focus on the extension of the baseline model to various scenarios which can and do arise in reality, in terms of both public health interventions and immune response."

Public health effects

The team declares that as private researchers, they did not have access to all public health data but attempt to use the available data to predict disease spread trajectories and effects on population health. They gathered data such as numbers of hospitalizations and deaths as well as cumulative cases.

Some of the variables they took into account were the age and vulnerability of the populations and the structure of the transmission of the infection. They classified society into two classes – young people and older people. Young were those who were least vulnerable, irrespective of age. This included even immunocompetent 60-year olds with no known comorbidities. Old were those who were vulnerable, even including 20-year olds with known immune disorders.

Their calculations showed, "Not only is the unmasked population infected almost entirely, but also there is more than 50 percent infection level among the masked people." They added, "This type of statistic can be used by public health authorities to encourage mask use – by not masking, not only are you increasing your own chances of catching corona but you are subjecting the law-abiding people to extra risk as well."

They also found superspreaders to be of two types:

people who interact with others a lot
people who have exceptionally high viral loads and infect almost whomever they come into contact with

Effects of immunity

The researchers assumed that prior infection would provide permanent immunity and prevent further infections.

The team considered the case where immunity against the disease lasts for a fixed, limited duration first. They predicted a trajectory of the infections and their recoveries in this scenario.

In case of a complex immune response to the infection. They assumed three different immune responses, "sterilizing immunity which completely prevents reinfection, severity-reducing immunity which mitigates the symptoms during reinfection and transmissibility-reducing immunity which mitigates the patient's transmissibility during reinfection." They also take into consideration a severe form, "antibody-dependent enhancement (ADE) in which case a reinfection takes a more severe form than the original infection."

Using these scenarios, the team predicted trajectories of the infection in the populations.

Conclusions and implications for future

Using the mathematical models, the team says that it is possible to create models that combine different variations including mask use, contact tracing, and complex immune responses to the infection.

Authors conclude, "Infectious disease has always been a part of human existence, and with the advent of jetliner travel, pathogens can be carried halfway across the world in a matter of hours. With current trends continuing, it is highly probable that pandemics are here to stay. We hope that the same may be said of our model as well."

*Important Notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:

A New Approach to the Dynamic Modeling of an Infectious Disease, B Shayak, Mohit Manoj Sharma, medRxiv 2020.10.30.20223305; doi:, ​ ... 20223305v1
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Providing the best psychiatric care within a safe environment during COVID-19 pandemic

11/4/20 ... demic.aspx

The very heart of inpatient care for psychiatric patients is socialization, group therapy, shared meals, and a standard two people per room. Then COVID-19 hit with the accompanying public health warnings to isolate, socially distance, and wear masks.

That sent clinicians and staff from The University of Texas Health Science Center at Houston (UTHealth) moving quickly to create a strategy for the UTHealth Harris County Psychiatric Center (UTHealth HCPC) that provided the best psychiatric care within a safe environment in the middle of an epic pandemic. That strategy was published in the October issue of Psychiatry Research.

"When COVID-19 began, we were left with the question of how to manage a highly infectious virus in a freestanding psychiatric hospital. There was no existing published guideline on how to do this," said first author Lokesh Shahani, MD, MPH, assistant professor in the Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences at McGovern Medical School at UTHealth and chief medical officer of UTHealth HCPC.

The largest provider of inpatient psychiatric care in the Greater Houston area, the center is a 274-bed, safety-net hospital that provides care to around 9,000 patients each year. It is led by Executive Director Jair C. Soares, MD, PhD, senior author of the paper, and Pat R. Rutherford, Jr. Chair in Psychiatry in the Faillace Department of Psychiatry and Behavioral Sciences.

By mid-March, with rising Harris County cases, hospital leaders realized that the chances of a patient arriving who was infected with COVID-19 were rapidly growing.

" Our mission is to treat patients in the community who have psychiatric conditions and we didn't want to turn anyone away. The only way to do that was to create an isolated COVID-19 unit."

- Stephen Glazier, COO of UTHealth HCPC

To create the space, new patients were not admitted to that unit and remaining patients were moved into other units as spots became available. Glazier asked for volunteers willing to staff the unit day and night. Shahani, who is board certified in infectious disease as well as psychiatry, decided to lead the infection control initiative.

"I am honored to work alongside our dedicated nursing staff who stepped up and volunteered to take care of our COVID-19 patients," Shahani said. "Staff safety was our priority and we made sure everyone was trained in appropriately using PPE and had access to the same."

The team turned for advice and leadership from co-author Luis Ostrosky, MD, professor of infectious diseases at McGovern Medical School, and vice chair of Healthcare Quality at McGovern Medical School. "We had to keep staff safe as well as patients," Shahani said. "Being a part of UTHealth and able to consult with Dr. Ostrosky was invaluable for us."

Ostrosky is the COVID-19 Incident Commander for UTHealth. "COVID-19 continues to challenge the way we work. Knowledge acquisition and flexibility have been key in making changes and adapting to our new reality," Ostrosky said. "From transporting patients in helicopters, to figuring out waiting rooms, to making psychiatric care safe, UTHealth is here for our community's needs."

Complicating the process for the team were patients who refused to be tested for the coronavirus or understand why they had to wear a mask.

"In a medical hospital, patients are able to have a private room with attached bathroom, which we don't have, and they are tested for the virus. Psychiatric patients don't always consent for testing because of their severe mental illness, and 40% refused testing," Shahani said. "Wearing a face mask and adhering to hand hygiene are other measures needed to keep people safe, but people with chronic severe mental illness don't have the ability to always follow guidelines such as that."

The team decided to focus on screening for symptoms, fever, contact, and travel history. They used extreme caution: anyone who was suspected of having the virus was isolated.

The first test for the new unit came April 17 when an asymptomatic patient required isolation because of recent travel and exposure. Since then, over 100 patients have been treated in the COVID-19 unit, with 52% of them testing positive for the virus.

"We've had community partners who needed a safe place to treat patients and we have been able to step in and accept these patients," Shahani said. "We have been safely delivering psychiatric care during the pandemic."

"We could not be any prouder of the outstanding team of very committed, compassionate clinicians and staff we have at UTHealth HCPC," Soares said. "They stepped up to help us continue to function at very high levels through this unprecedented crisis with the pandemic."


University of Texas Health Science Center at Houston

Journal reference:

Shahani, L., et al. (2020) Universal SARS-CoV-2 testing versus symptom based screening and testing in inpatient psychiatric setting. Psychiatry Research.
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People treated with statins have 22% to 25% lower risk of dying from COVID-19

11/4/20 ... ID-19.aspx

Coronavirus has infected more than 40 million people around the world and has caused more than a million deaths in less than a year. Moreover, it is still not clear why some people who contract the virus show no symptoms whereas others may die or suffer very severe consequences.

Although age, illnesses and previous treatments can be used to give a prognosis in some cases, it is still not possible to state for certain how each case of coronavirus will evolve. One of the treatments that have been discussed in regard to their role in the evolution of COVID-19 has been statins. This drug helps to reduce cholesterol in the blood and thus prevent cardiovascular diseases. It is currently taken by one in four people and is the most widely used medicine among the general public.

Now, a research by the Universitat Rovira i Virgili (URV) and Pere Virgili Institut (IISPV) led by Lluís Masana has found that people who are being treated with statins have a 22% to 25% lower risk of dying from COVID-19. The research results have been published in the European Heart Journal - Cardiovascular Pharmacotherapy.

The study was carried out through the Network of Lipid and Arteriosclerosis Units of Catalonia and collected information from 2,159 patients infected with SARS-COv-2 from 19 hospitals in Catalonia during the first wave of the pandemic from March to May. The researcher evaluated one hundred clinical variables per patient such as age, sex, previous illnesses, cholesterol levels, evolution of the virus, treatments used for COVID-19, and so on.

The researchers then compared death rates of patients being treated with statins with death rates among those who were not and they also analysed the effect of withdrawing statins when the patient was admitted to hospital. "In our comparison, we adjusted the groups so that they were comparable in terms of age, sex and the existence of earlier illnesses", explained Masana, who has coordinated the study from the Lipid and Arteriosclerosis Research Unit at the URV's Department of Medicine and Surgery, which is a member of the CIBERDEM Network bringing together research groups working on diabetes and metabolism in Spain. Masana is also a researcher at the Sant Joan University Hospital in Reus.

The percentage of patients who died in the group not treated with statins was 25.4%, whereas it was 19.8% among those who were, that is to say 22% lower. "The data indicate that treatment with statins prevents one in five deaths", indicated Masana. Furthermore, if treatment with this medicine continued during hospitalization, mortality fell by up to 25%, thus preventing one in four deaths.

Consequently, Lluís Masana went on to say that "not only do these findings demonstrate that treatment with statins has no negative on the evolution of COVID-19, they also show that it significantly reduces patient mortality".

One of the indirect effects of the pandemic is that some people have stopped taking preventive measures aimed at combatting chronic diseases or maintaining general health, and this has been the case with statins.

"Some health professionals have even advised their withdrawal in the belief that they could worsen the effects of COVID-19", said Masana. In this regard, in addition the virus to directly causing death in some patients, complications and overall mortality can increase due to the withdrawal of these drugs and regular monitoring of the use of this medicine.

" In the case of statins, we have demonstrated that fear of the pandemic should never be used as an excuse to suspend treatment."

- Lluís Masana, Researcher

Although the research was never intended to demonstrate that administering statins to COVID-19 patients would reduce the risk of death, it does open the way for studies that may confirm this finding.


Universitat Rovira i Virgili

Journal reference:

Masana, L., et al. (2020) Effect of statin therapy on Sars-CoV-2 infection-related mortality in hospitalized patients. European Heart Journal - Cardiovascular Pharmacotherapy.
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Texas A&M study identifies four significant predictors of COVID-19 cases

11/4/20 ... cases.aspx

In March 2020, New York City, an icon of America, was unfortunately named an early epicenter of the novel coronavirus. Now seven months later, America faces a new surge in coronavirus cases and researchers at Texas A&M University hope to provide information and context to help with the battle ahead.

Rich Whittle, a doctoral student at Texas A&M, cites in a recent study that by April 2020, New York City accounted for more than a third of the nation's confirmed cases, with a transmission rate five times higher than the rest of the country.

Whittle wanted to look at these early stages of the pandemic spread in New York neighborhoods to discover if there were any socioeconomic factors that could be associated with the high positivity rate of COVID-19.

" The world is waking up to the first global pandemic in a while, but it's not going to be the last, so understanding the contact patterns and socioeconomic factors that lead to high-detected case numbers is important for public health."

- Rich Whittle, a doctoral student at Texas A&M

The study, published in BMC Medicine, identified four significant predictors of COVID-19 cases in New York City: neighborhoods with higher population densities led to an increase in the positivity rate; neighborhoods with younger populations (under 18 years old) also led to an increase; households with a higher income led to a decrease; and race showed a significant association with detected COVID-19 cases - both a lower percentage of white population and higher percentage of Black population led to increased positivity rates.

"From what is available in the early stages, this is what we're seeing from the data, and we know those early stages are really important to keep this and future pandemics under control," said Dr. Ana Diaz Artiles, assistant professor in the Department of Aerospace Engineering at Texas A&M and co-author of the study.

The study used spatial modeling techniques to look at data from roughly 60,000 cases during the first month of the pandemic in New York City.

"I'm really interested in spatial statistics. When I was in the military, I worked in geospatial intelligence so I have a background interest in that," said Whittle. "And I was taking Dr. Diaz Artiles' stats class at the time, so I thought I could combine those two interests and have a look at an ecological study related to COVID-19."

Whittle initiated the study as his final class project for Diaz Artiles' spring Design of Experiments and Statistical Methods course (AERO 689).

"This class gives the opportunity to solve problems that the students are interested in," said Diaz Artiles. "These classes are really useful for students not only in terms of learning statistical tools, but to apply them in practical applications that could even lead to impactful results and publications."

In addition to academic interest, Whittle was motivated to pursue the study because of the value the results could provide, both now and in the future.

"There's a need to understand the beginning stage of the pandemic," said Whittle. "And I think in America, certainly now, there's a lot of discontent. There's a definite public interest in understanding the response in the initial stages of the pandemic."

Whittle and Diaz Artiles emphasize that understanding the early factors and influences of past pandemics, such as the H1N1 pandemic of 2009 and the one we face today, is important in helping to inform future management.

"Hopefully our study will provide a better understanding of the main factors that impact the spread of the disease, thus improving future decision making in the early stages of a pandemic," said Diaz Artiles.


Texas A&M University

Journal reference:

Whittle, R.S & Diaz-Artiles, A (2020) An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City. BMC Medicine.
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Re: Pandemic News Links / Current News Updates

Post by trader32176 »

Black, Latinx hospital workers at highest risk for SARS-CoV-2 infection, study finds

11/4/20 ... finds.aspx

Support staff and Black and Latinx hospital employees with and without patient care responsibilities are at highest risk for SARS-CoV-2 infection in health care settings, a Rutgers study found.

After screening 3,904 employees and clinicians at a New Jersey hospital between late April and late June for the SARS-CoV-2 virus and for lgG-antibodies to the virus, whose presence suggests past recent infection, the study, published in the journal Open Forum Infectious Diseases, found that these employees are at higher risk than previously thought.

" The risk to workers in health care settings with little or no patient contact has attracted relatively little attention to date, but our results suggest potentially high infection rates in this group. By contrast and to our surprise, physicians, nurses and emergency medical technicians showed much lower infection rates."

- Emily S. Barrett, lead author, associate professor at Rutgers School of Public Health and a member of the Environmental and Occupational Health Sciences Institute

Health care workers who live in highly impacted communities may have been susceptible to becoming infected outside of the hospital during the early surge of COVID-19, according to co-lead author Daniel B. Horton, an assistant professor at Rutgers Robert Wood Johnson Medical School and a member of the Institute for Health, Health Care Policy and Aging Research.

"In the early phase of the pandemic, support staff in the hospital may also have had less access to personal protective equipment or less enforcement of safety protocols," he said. "Going forward, as cases of COVID-19 in the hospital rise again, protecting these and all hospital workers from infection both in and out of the hospital is critical."

In the hospital-based study, researchers found that 13 participants tested positive for the virus and 374 tested positive for the antibody, which suggests recent past infection -- nearly 10 percent of those studied -- and that Black and Latinx workers had two times the odds of receiving a positive test for the virus or antibody compared to white workers.

Phlebotomists had the highest proportionate rate of positive tests--nearly 1 in 4 tested--followed by those employed in maintenance/housekeeping, dining/food services and interpersonal/support roles. By comparison, positivity rates were lower among doctors (7 percent) and nurses (9 percent).

Regardless of whether the infections originated in the hospital or in the community, Barrett said, the results suggest a need to enact safety protocols for hospital employees to protect the health care workforce from future waves of infection.

"The 40 percent of infected health care workers who reported having had no symptoms of infection could be a potential source of SARS-CoV-2 spread in hospitals even if their infections were initially acquired in the community," she said.


Rutgers University

Journal reference:

Barrett, E.S., et al. (2020) Risk factors for SARS-CoV-2 infection in hospital workers: results from a screening study in New Jersey, U.S. in Spring 2020. Open Forum Infectious Diseases.
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Re: Pandemic News Links / Current News Updates

Post by trader32176 »

Will an effective COVID-19 vaccine return life to normal in the US?

11/4/20 ... he-US.aspx

A new study has shown that the extent to which social distancing restrictions and face mask usage could be relaxed in the United States during the coronavirus disease 2019 (COVID-19) pandemic would greatly depend on how effective a vaccine is and the proportion of the population it covered.

The team - from China, the United States and Australia - conducted a modeling study showing that only a vaccine that was almost 100% effective would suppress the epidemic enough for the US population to return to pre-pandemic life and lose the need for social distancing and face masks.

If a highly effective vaccine could not be obtained, the use of a moderately (80%) effective vaccine in combination with 30 to 40% adoption of face mask usage may be a plausible alternative, say the researchers.

“Vaccination combined with a modest level of non-pharmaceutical measures, such as face mask use in common public spaces (shopping malls and transportation), might be a viable option to continue suppressing the epidemic in the long term,” write Lei Zhang from Xi’an Jiaotong University Health Science Center and colleagues.

The researchers say the study findings could be used to guide plans for the rollout of vaccines and the ongoing implementation of non-pharmaceutical interventions (NPIs).

A pre-print version of the paper is available on the server medRxiv*, while the article undergoes peer review.

The United States has been one of the most severely impacted countries

Since the first cases of COVID-19 were first identified in Wuhan, China, late last year (2019), the unprecedented spread of the pandemic has had devastating effects on global public health and the economy.

The impact in the United States has been particularly severe, with the number of confirmed infections now having reached more than 9.4 million and the number of deaths more than 232,000.

Given that a number of candidate vaccines have now entered phase III clinical trials, hopes are growing that social distancing restrictions and the requirement for facemask usage could soon be relaxed so that Americans can start to return to life as they know it.

However, the extent to which these control measures could be eased would depend on the effectiveness of the potential vaccines, which is not currently known.

“To allow careful planning about what restrictions may need to be continued, research is urgently needed to project how the effectiveness of a potential vaccine may affect the trajectory of the COVID-19 pandemic in the US,” say the researchers.

It is also essential to determine how the current NPIs could be incorporated within an overall control strategy that accounts for the varying efficacy of different vaccines, they add.

What did the researchers do?

The team developed dynamic simulation models of COVID-19 transmission for the four most severely affected states in the US, namely New York, Texas, Florida, and California.

The models were designed to account for the differences in social distancing and face mask policies that apply in each state.

The researchers used the models to evaluate the level of vaccine effectiveness and coverage that would be needed to avert COVID-19 cases and deaths in scenarios where social contact was to return to pre-pandemic levels and face mask usage was reduced.

Each of the state-level models was calibrated based on the most recent daily and cumulative COVID-19 data (from January 26th to September 15th, 2020) obtained from the Johns Hopkins University Coronavirus resource center.

What did the models predict?

In the absence of a vaccine, the spread of COVID-19 could be suppressed in the four states by maintaining the current social distancing measures and levels of face mask usage.

However, returning social contact to pre-pandemic levels, without changing the current requirements for face mask use, would result in new outbreaks, say Zhang and colleagues.

This would lead to between 0.8 and 4 million infections and 15,000 to 240,000 deaths across the four states within just one year.

In this scenario, the adoption of vaccination would help to decrease the number of infections and deaths, even if the vaccine effectiveness and coverage were relatively low, say the researchers.

However, if the rate of face mask use fell by 50%, then introducing a weak vaccine (only 50% effective) with low coverage would not be enough to suppress the epidemic.

With the level of face mask usage halved in these states, a weak vaccine would require a population coverage of 55 to 94% to suppress the epidemic, whereas a moderately effective vaccine (80%) would require 32 to 57% coverage and a strong vaccine (100% effective) would only require 24 to 46% coverage.

However, if the use of face masks stopped altogether, a weak vaccine would not be sufficient to suppress the epidemic, even with high coverage, and further major outbreaks would occur.

A moderate vaccine, on the other hand, would suppress the epidemic at a coverage of 48 to 78%, while a strong vaccine would suppress it at a coverage of 33 to 58%.

What do the researchers advise?

“Given that the willingness to take a COVID-19 vaccine in the US has been estimated at only 58%, only a strong vaccine with high effectiveness of nearly 100% would be sufficient to suppress the epidemic alone and enable relaxation of social distancing and face mask requirement,” write Zhang and colleagues.

However, if a strong vaccine is not attainable, a moderately effective vaccine and a face mask usage rate of around 30-40% would be a plausible alternative to achieve this target, they add.

“Findings from this study provide timely information that can be used by policymakers to plan for the potential release of a COVID-19 vaccine and understand its effect across different regions in the US under different social distancing and face mask use scenarios,” concludes the team.

*Important Notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:

Zhang L, et al. Projected COVID-19 epidemic in the United States in the context of the effectiveness of a potential vaccine and implications for social distancing and face mask use. medRxiv, 2020. doi:, ... 20221234v1
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