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Archive for the ‘Seasonality & Environmental Factors in Resurgence’ Category

COVID-19 Sequel: Neurological Impact of Social isolation been linked to poorer physical and mental health

Reporter: Aviva Lev-Ari, PhD, RN

UPDATED on 4/13/2021

Toward Understanding COVID-19 Recovery: National Institutes of Health Workshop on Postacute COVID-19

 

Abstract

Over the past year, the SARS-CoV-2 pandemic has swept the globe, resulting in an enormous worldwide burden of infection and mortality. However, the additional toll resulting from long-term consequences of the pandemic has yet to be tallied. Heterogeneous disease manifestations and syndromes are now recognized among some persons after their initial recovery from SARS-CoV-2 infection, representing in the broadest sense a failure to return to a baseline state of health after acute SARS-CoV-2 infection. On 3 to 4 December 2020, the National Institute of Allergy and Infectious Diseases, in collaboration with other Institutes and Centers of the National Institutes of Health, convened a virtual workshop to summarize existing knowledge on postacute COVID-19 and to identify key knowledge gaps regarding this condition.

Over the past year, the SARS-CoV-2 pandemic has swept the globe, resulting in more than 113 million persons infected and 2.5 million deaths (1). However, the additional toll resulting from long-term consequences of the pandemic has yet to be tallied. Heterogeneous disease manifestations and syndromes are now recognized among some persons after their initial recovery from SARS-CoV-2 infection. Although a standardized case definition does not yet exist for these manifestations, in the broadest sense they represent a failure to return to a baseline state of health after acute SARS-CoV-2 infection. The various terms used to describe this condition have included postacute (or late) sequelae of COVID-19, post-COVID condition or syndrome, long COVID, and long-haul COVID. In this article, we use the general umbrella term of “postacute COVID-19” to refer to multiple disease processes that may have varying degrees of overlap (including but not limited to sequelae of critical illness and hospitalization in persons with COVID-19) and the entity of long COVID, which refers to prolonged health abnormalities in persons previously infected with SARS-CoV-2 who may or may not have required hospitalization. Of note, there is not yet a consensus on terminology, which will likely evolve with a better understanding of this condition.

Reported symptoms are wide-ranging and may involve nearly all organ systems, with fatigue, dyspnea, cognitive dysfunction, anxiety, and depression often described (2–5). Although abnormalities in imaging studies and functional testing have been reported, the long-term clinical significance of some of these findings is not yet clear (367). Postacute manifestations of COVID-19 have been seen in persons of all demographic groups and include reports of multisystem inflammatory syndrome in children (89). Although the epidemiology of the diverse manifestations of postacute COVID-19 is not yet known, the expansive global burden of SARS-CoV-2 infection suggests that the potential public health effects of postacute COVID-19 are significant if even a small proportion of persons with SARS-CoV-2 infection have prolonged recovery or do not return to their baseline health.

On 3 to 4 December 2020, the National Institute of Allergy and Infectious Diseases, in collaboration with other Institutes and Centers of the National Institutes of Health, convened a virtual workshop (available via videocast at https://videocast.nih.gov/watch=38878 and https://videocast.nih.gov/watch=38879) to summarize existing knowledge on postacute COVID-19 and to identify key knowledge gaps. The speakers and participants included epidemiologists, clinicians, clinical and basic scientists, and members of the affected community. The videocast was open to the general public and had more than 1200 registered participants.

SOURCE

UPDATED on 4/7/2021

‘Beyond a Reasonable Doubt’: COVID-19 Brain Health Fallout Is Real, Severe

Sarah Edmonds

April 07, 2021

Editor’s note: Find the latest COVID-19 news and guidance in Medscape’s Coronavirus Resource Center.

START QUOTE

COVID-19 survivors face a sharply elevated risk of developing psychiatric or neurologic disorders in the six months after they contract the virus — a danger that mounts with symptom severity, new research shows.

In what is purported to be the largest study of its kind to-date, results showed that among 236,379 COVID-19 patients, one third were diagnosed with at least one of 14 psychiatric or neurologic disorders within a 6-month span.

The rate of illnesses, which ranged from depression to stroke, rose sharply among those with COVID-19 symptoms acute enough to require hospitalization.  

“If we look at patients who were hospitalized, that rate increased to 39%, and then increased to about just under 1 in 2 patients who needed ICU admission at the time of the COVID-19 diagnosis,” Maxime Taquet, PhD, University of Oxford Department of Psychiatry, Oxford, United Kingdom, told a media briefing.

Incidence jumps to almost two thirds in patients with encephalopathy at the time of COVID-19 diagnosis, he added.

The study, which examined the brain health of 236,379 survivors of COVID-19 via a US database of 81 million electronic health records, was published online April 6 in The Lancet Psychiatry.

High Rate of Neurologic, Psychiatric Disorders

The research team looked at the first-time diagnosis or recurrence of 14 neurologic and psychiatric outcomes in patients with confirmed SARS-CoV-2 infections. They also compared the brain health of this cohort with a control group of those with influenza or with non-COVID respiratory infections over the same period. 

SOURCE

The Effects of Loneliness and Our Brain function: poorer physical and mental health

One review of the science of loneliness found that people with stronger social relationships have a 50 per cent increased likelihood of survival over a set period of time compared with those with weaker social connections. Other studies have linked loneliness to cardiovascular disease, inflammation, and depression.

For loneliness researchers the pandemic has provided an unprecedented natural experiment in the impact that social isolation might have on our brains. As millions of people across the world emerge from months of reduced social contact, a new neuroscience of loneliness is starting to figure out why social relationships are so crucial to our health.

Neural basis of Emotion

Desire for Social Interaction

Are there neurological differences between people who experience short-term isolation and those who have been isolated for long stretches of time? What kinds of social interactions satisfy our social cravings? Is a video call enough to quell our need for social contact, or do some people require an in-person connection to really feel satiated?

START QUOTE

Julianne Holt-Lunstad, a psychology professor at Brigham Young University in the US and the author of two major studies on social isolation and health. “We have a lot of data that very robustly shows that both isolation and loneliness put us at increased risk for premature mortality—and conversely, that being socially connected is protective and reduces our risk,” she says.

START QUOTE

“Trying to investigate isolation or loneliness is not as straightforward in humans. In humans, being lonely is not necessarily correlated with how many people are around you,” says Tomova. She is particularly interested in the impact that the pandemic might have had on young people whose cognitive and social skills are still developing. “I think we will see potentially some differences in how their social behavior developed or things like that,” she says. But as is always the case in the uncertain world of loneliness research, the opposite could be true. “It could also be that most people are fine, because maybe social media does fulfill our social needs really well.”

SOURCE

https://www.wired.co.uk/article/lockdown-loneliness-neuroscience

The Weird Science of Loneliness and Our Brains – Social isolation as been linked to poorer physical and mental health, but scientists are finally starting to understand its neurological impact

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COVID-related financial losses at Mass General Brigham

Reporter: Aviva Lev-Ari, PhD, RN

Based on

Mass General Brigham reports COVID-related financial losses not as bad as expected

By Priyanka Dayal McCluskey Globe Staff,Updated December 11, 2020, 3:02 p.m.

START QUOTE

The state’s largest hospital system on Friday reported the worst financial loss in its history while fighting the COVID-19 pandemic — but still ended the fiscal year in better shape than expected.

Mass General Brigham, formerly known as Partners HealthCare, lost $351 million on operations in the fiscal year that ended Sept. 30. In 2019, the system recorded a gain of $382 million.

The loss, however, is not as great as projected, thanks in part to an infusion of federal aid and patients returning to hospitals in large numbers after the first COVID surge receded.

“2020 is like no other year,” said Peter Markell, chief financial officer at Mass General Brigham, which includes Massachusetts General Hospital, Brigham and Women’s Hospital, and several community hospitals. “At the end of the day, we came out of this better than we thought we might.”

Total revenue for the year remained relatively stable at about $14 billion.

When the pandemic first hit Massachusetts in March, hospitals across the state suddenly experienced sharp drops in revenue because they canceled so much non-COVID care to respond to the crisis at hand. They also faced new costs related to COVID, including the personal protective equipment needed to keep health care workers safe from infection.

Federal aid helped to make up much of the losses, including $546 million in grant money that went to Mass General Brigham. The nonprofit health system also slashed capital expenses in half, by about $550 million, and temporarily froze employee wages and cut their retirement benefits.

Among the unusual new costs for Mass General Brigham this year was the expense of building a field hospital, Boston Hope, at the Boston Convention and Exhibition Center. The project cost $15 million to $20 million, Markell said, and Mass General Brigham is working to recoup those costs from government agencies.

The second surge of COVID, now underway, could hit hospitals’ bottom lines again, though Markell expects a smaller impact this time. One reason is because hospitals are trying to treat most of the patients who need care for conditions other than COVID even while treating growing numbers of COVID patients. In the spring, hospitals canceled vastly more appointments and procedures in anticipation of the first wave of COVID.

Mass General Brigham hospitals were treating more than 300 COVID patients on Friday, among the more than 1,600 hospitalized across the state.

Steve Walsh, president of the Massachusetts Health & Hospital Association, said hospitals across the state will need more federal aid as they continue battling COVID into the new year.

“The financial toll of COVID-19 has been felt by every hospital and health care organization in the Commonwealth,” he said. “Those challenges will continue during 2021.”


Priyanka Dayal McCluskey can be reached at priyanka.mccluskey@globe.com. Follow her on Twitter @priyanka_dayal.

END QUOTE

SOURCE

https://www.bostonglobe.com/2020/12/11/business/mass-general-brigham-reports-covid-related-financial-losses-better-than-expected/?p1=Article_Inline_Related_Box

Integration of Mass General Hospital and Brigham Women’s Hospital was accelerated by the COVID-19 pandemic

Reporter: Aviva Lev-Ari, PhD, RN

BASED on

At Mass General Brigham, a sweeping effort to unify hospitals and shed old rivalries

Executives say greater cooperation is necessary to stay relevant in a dynamic and competitive health care industry. But the aggressive push to integrate is stirring tensions and sowing discontent among doctors and hospital leaders.

By Priyanka Dayal McCluskey and Larry Edelman Globe Staff and Globe Columnist,Updated March 27, 2021, 6:15 p.m.125

https://www.bostonglobe.com/2021/03/27/business/mass-general-brigham-sweeping-effort-unify-hospitals-shed-old-rivalries/?s_campaign=breakingnews:newsletter

START QUOTE

The work of integration was accelerated by the COVID-19 pandemic. As patients flooded hospitals last spring, Mass General Brigham — not each of its individual hospitals — set pandemic policies, from what kind of personal protective equipment health care providers should wear, to which visitors were allowed inside hospitals, to how employees would be paid if they were out sick with the virus.

During the winter surge of COVID, Mass General Brigham officials closely tracked beds across their system and transferred patients daily from one hospital to another to ensure that no one facility became overwhelmed.

And, in the early months of the pandemic, the company dropped the name Partners, which meant little to patients, and unveiled a new brand to reflect the strength of its greatest assets, MGH and the Brigham.

Officials at the nonprofit health system have instructeddepartment heads across their hospitals to coordinate better, so, for example, if a patient needs surgery at the Brigham but is facing a long wait, they can refer that patient to another site within Mass General Brigham.

Some executives want patients, eventually, to be able to go online and book appointments at any Mass General Brigham facility, as easily as they make reservations for dinner or a hotel.

Walls described it like this: “How do we put things together that make things better and easier for patients, and leave alone things that are better where they are?

“We’re not going to push things together that don’t fit together,” he said.

And yet the aggressive pursuit of “systemness,” as executives call it, is taking a toll. Physicians and hospital leaders are struggling with the loss of control over their institutions and worried that the new era of top-down management threatens to homogenize a group of hospitals with different cultures and identities.

Veteran physicians and leaders have been surprised and upset by the power shift that is stripping them of the ability to make key decisions and unhappy with abrupt changes they feel are occurring with little discussion. Most are uncomfortable sharing their concerns publicly.

“If you’re not on the train, you’re getting run over by the train,” said one former Mass General Brigham executive who requested anonymity in orderto speak openly. “It’s not an environment to invite debate.”

Amid the restructuring, senior executives are departing in droves. They include the CEO of the MGH physicians group, Dr. Timothy Ferris; Brigham and Women’s president Dr. Elizabeth Nabel; chief financial officer of the system, Peter Markell; Cooley Dickinson Hospital president Joanne Marqusee; and president of Spaulding Rehabilitation Network, David Storto.

Some also fear the internal discord could hinder Mass General Brigham’s ability to attract talented leaders.

Top executives acknowledge there is angst — “Change is hard,” Klibanski said — but are pushing ahead.

MORE

https://www.bostonglobe.com/2021/03/27/business/mass-general-brigham-sweeping-effort-unify-hospitals-shed-old-rivalries/?s_campaign=breakingnews:newsletter

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Rise of a trio of mutated viruses hints at an increase in transmissibility, speeding the virus’ leaps from one host to the next

Reporter: Aviva Lev-Ari, PhD, RN

“We have uncontrolled viral spread in much of the world,” says Adam Lauring, an infectious disease physician and virologist at the University of Michigan. “So the virus has a lot of opportunity to evolve.”

“The variants may be more transmissible, but physics has not changed,” says Müge Çevik, an infectious disease physician at the University of St. Andrews in Scotland.

Many changes don’t affect the virus’ function, and some even harm SARS-CoV-2’s ability to multiply, but they keep happening. “Viruses mutate; that’s what they do,” says Akiko Iwasaki, an immunologist at Yale School of Medicine in Connecticut.

U.K., Brazil, and South Africa. In the United Kingdom, variant B.1.1.7 likely drove the region’s record-setting spike of COVID-19 cases in January. The variant is now circulating in more than 60 countries, including the United States—and projections suggest it will become the most common virus variety in the U.S. by mid-March.

An independently arising lineage called P.1 might also be driving a wave of cases in Manaus, Brazil, where it accounted for nearly half of new COVID-19 infections in December. On January 26, Minnesotan officials reported the first U.S. case of P.1 in a resident who previously traveled to Brazil. And a third lineage raising alarms, known as B.1.351, was first spotted amid a December wave of infections in South Africa. On January 28, the first known U.S. cases of the variant were reported in South Carolina.

One specific mutation, known as N501Y, popped up independently in all three variants, suggesting it could provide an advantage to the virus. “That’s a sign that there is natural selection going on,” Lauring says. The N501Y mutation affects the virus’ spike protein, which is the key it uses to unlock entry into its host’s cells.

Another possibility is that new variants cause people who are infected to harbor more copies of the virus. This results in greater viral “shedding” in airborne droplets spewed when people talk, sing, cough, and breath.

mutations in 501Y.V2 could diminish the effectiveness of antibodies in the blood of people previously infected with the virus. But understanding whether that could lead to more re-infections, or if it could affect vaccine efficacy.

Dramatically scale up production of high-filtration masks for the general public.

Based on:

Why some coronavirus variants are more contagious‹and how we can stop them

https://www.nationalgeographic.com/science/2021/01/why-some-coronavirus-variants-are-more-contagious/?cmpid=org=ngp::mc=crm-email::src=ngp::cmp=editorial::add=SpecialEdition_20210129

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Why Do Some COVID-19 Patients Infect Many Others, Whereas Most Don’t Spread the Virus At All?

Guest Reporter: Jason S Zielonka, MD

One of the key parameters in COVID-19 pandemic epidemiology has been to define the spread metrics, basically identifying how a host spreads the virus to uninfected individuals. The pattern of spread can impact how and which preventative measures such as social distancing and hand washing can impact spread patterns. In particular, two metrics, the average number of new patients infected by each host (the reproduction number, R) and a factor representing the tendency to cluster (the dispersion factor, k) can be used to describe and model the spread of a virus quite well. Higher values of R mean more people are infected by a single host, i.e, the disease is more contagious; lower values of k mean that a host infects a larger number of new patients, i.e., the disease is more clustered.

The reproduction number, R, for SARS-CoV-2, without social distancing, is about 3. But this is an average, taken over an aggregate of patients. For most individuals, R is zero, i.e., most patients do not transmit the virus to others. For comparison, SARS and MERS, both coronaviruses, had R > 3 and the 1918 influenza pandemic had R >> 3. So what determines viral spread and how can we use that information to treat and eradicate SARS-CoV-2?

In 2005, by modeling the Chinese SARS outbreak and comparing the model to the real-world data, Lloyd-Smith and co-authors were able to determine that SARS had a k of about 0.16. MERS, in 2012, was estimated to have k around 0.25; the 1918 pandemic, by contrast, had a k of 1, meaning it had very little cluster effect. The current modeling indicates that k for SARS-CoV-2 is not conclusive, but it appears higher than k for either SARS or MERS.

This work has provided insights into some of the factors influencing cluster spread, which can be controlled in a more specific way than quarantining an entire population. There will be individual variance, but we know that people are particularly infectious over a certain time period; that certain activities are more conducive to droplet formation and wider spread, and that being outdoors rather than in confined and noisy indoor locations leads to less spread. This can all lead to better, faster and more tolerable approaches to either future pandemics or to a recurrence of SARS-CoV-2.

SOURCE

https://www.sciencemag.org/news/2020/05/why-do-some-covid-19-patients-infect-many-others-whereas-most-don-t-spread-virus-all

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From AAAS Science News on COVID19: New CRISPR based diagnostic may shorten testing time to 5 minutes

Reporter: Stephen J. Williams, Ph.D.

 

 

 

 

 

 

 

 

 

A new CRISPR-based diagnostic could shorten wait times for coronavirus tests.

 

 

New test detects coronavirus in just 5 minutes

By Robert F. ServiceOct. 8, 2020 , 3:45 PM

Science’s COVID-19 reporting is supported by the Pulitzer Center and the Heising-Simons Foundation.

 

Researchers have used CRISPR gene-editing technology to come up with a test that detects the pandemic coronavirus in just 5 minutes. The diagnostic doesn’t require expensive lab equipment to run and could potentially be deployed at doctor’s offices, schools, and office buildings.

“It looks like they have a really rock-solid test,” says Max Wilson, a molecular biologist at the University of California (UC), Santa Barbara. “It’s really quite elegant.”

CRISPR diagnostics are just one way researchers are trying to speed coronavirus testing. The new test is the fastest CRISPR-based diagnostic yet. In May, for example, two teams reported creating CRISPR-based coronavirus tests that could detect the virus in about an hour, much faster than the 24 hours needed for conventional coronavirus diagnostic tests.CRISPR tests work by identifying a sequence of RNA—about 20 RNA bases long—that is unique to SARS-CoV-2. They do so by creating a “guide” RNA that is complementary to the target RNA sequence and, thus, will bind to it in solution. When the guide binds to its target, the CRISPR tool’s Cas13 “scissors” enzyme turns on and cuts apart any nearby single-stranded RNA. These cuts release a separately introduced fluorescent particle in the test solution. When the sample is then hit with a burst of laser light, the released fluorescent particles light up, signaling the presence of the virus. These initial CRISPR tests, however, required researchers to first amplify any potential viral RNA before running it through the diagnostic to increase their odds of spotting a signal. That added complexity, cost, and time, and put a strain on scarce chemical reagents. Now, researchers led by Jennifer Doudna, who won a share of this year’s Nobel Prize in Chemistry yesterday for her co-discovery of CRISPR, report creating a novel CRISPR diagnostic that doesn’t amplify coronavirus RNA. Instead, Doudna and her colleagues spent months testing hundreds of guide RNAs to find multiple guides that work in tandem to increase the sensitivity of the test.

In a new preprint, the researchers report that with a single guide RNA, they could detect as few as 100,000 viruses per microliter of solution. And if they add a second guide RNA, they can detect as few as 100 viruses per microliter.

That’s still not as good as the conventional coronavirus diagnostic setup, which uses expensive lab-based machines to track the virus down to one virus per microliter, says Melanie Ott, a virologist at UC San Francisco who helped lead the project with Doudna. However, she says, the new setup was able to accurately identify a batch of five positive clinical samples with perfect accuracy in just 5 minutes per test, whereas the standard test can take 1 day or more to return results.

The new test has another key advantage, Wilson says: quantifying a sample’s amount of virus. When standard coronavirus tests amplify the virus’ genetic material in order to detect it, this changes the amount of genetic material present—and thus wipes out any chance of precisely quantifying just how much virus is in the sample.

By contrast, Ott’s and Doudna’s team found that the strength of the fluorescent signal was proportional to the amount of virus in their sample. That revealed not just whether a sample was positive, but also how much virus a patient had. That information can help doctors tailor treatment decisions to each patient’s condition, Wilson says.

Doudna and Ott say they and their colleagues are now working to validate their test setup and are looking into how to commercialize it.

Posted in:

doi:10.1126/science.abf1752

Robert F. Service

Bob is a news reporter for Science in Portland, Oregon, covering chemistry, materials science, and energy stories.

 

Source: https://www.sciencemag.org/news/2020/10/new-test-detects-coronavirus-just-5-minutes

Other articles on CRISPR and COVID19 can be found on our Coronavirus Portal and the following articles:

The Nobel Prize in Chemistry 2020: Emmanuelle Charpentier & Jennifer A. Doudna
The University of California has a proud legacy of winning Nobel Prizes, 68 faculty and staff have been awarded 69 Nobel Prizes.
Toaster Sized Machine Detects COVID-19
Study with important implications when considering widespread serological testing, Ab protection against re-infection with SARS-CoV-2 and the durability of vaccine protection

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Sex Differences in Immune Responses that underlie COVID-19 Disease Outcomes

Reporter: Aviva Lev-Ari, PhD, RN and Irina Robu, PhD COVID-19 is a non-discriminatory virus, it can infect anyone from young to old, but it seems that older men are twice more susceptible to it and most likely to become severely sick and die in comparison to women of the same age. Researchers from Yale university, published an article suggesting that men, particularly those over the age of 60 may need to depend more on vaccines to protect themselves from infection. According to their research published in Nature in August 2020, known sex differences between men and women pose challenges to the immune system. Women mount faster and stronger immune responses, possibly because their bodies are equipped to fight pathogens that threaten unborn or newborn children. Over time, an immune system in a constant state of high alert can be harmful. The findings underline the necessity for companies developing coronavirus vaccines to analyze their data by sex and may influence decisions about dosing. Dr. Iwasaki’s team from Yale  analyzed immune responses in 17 men and 22 women who were admitted to the hospital soon after they were infected with the coronavirus. The investigators collected blood, nasopharyngeal swabs, saliva, urine and stool from the patients every three to seven days. The researchers also analyzed data from an additional 59 men and women who did not meet those criteria. Over all, the scientists found, the women’s bodies produced more T cells, which can kill and stop the infection from spreading. Men on the other hand presented  a much weaker activation of T cells and that delay was linked to how sick the men became. The older the men, the weaker their T cell responses. Even though the study provided some more information about why men become sicker when diagnosed with coronavirus than women,  it did not offer a clear reason for the differences between men and women. SOURCE https://www.nature.com/articles/s41586-020-2700-3
Article

This is an unedited manuscript that has been accepted for publication. Nature Research are providing this early version of the manuscript as a service to our authors and readers. The manuscript will undergo copyediting, typesetting and a proof review before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers apply.

Sex differences in immune responses that underlie COVID-19 disease outcomes

Abstract

A growing body of evidence indicates sex differences in the clinical outcomes of coronavirus disease 2019 (COVID-19)1–5. However, whether immune responses against SARS-CoV-2 differ between sexes, and whether such differences explain male susceptibility to COVID-19, is currently unknown. In this study, we examined sex differences in
  • viral loads,
  • SARS-CoV-2-specific antibody titers,
  • plasma cytokines, as well as
  • blood cell phenotyping in COVID-19 patients.
By focusing our analysis on patients with moderate disease who had not received immunomodulatory medications, our results revealed that
  • male patients had higher plasma levels of innate immune cytokines such as IL-8 and IL-18 along with more robust induction of non-classical monocytes. In contrast,
  • female patients mounted significantly more robust T cell activation than male patients during SARS-CoV-2 infection, which was sustained in old age.
  • Importantly, we found that a poor T cell response negatively correlated with patients’ age and was associated with worse disease outcome in male patients, but not in female patients.
  • Conversely, higher innate immune cytokines in female patients associated with worse disease progression, but not in male patients.
  • These findings reveal a possible explanation underlying observed sex biases in COVID-19, and provide an important basis for the development of
  • a sex-based approach to the treatment and care of men and women with COVID-19.

Author information

Affiliations

Consortia

Corresponding author

Correspondence to Akiko Iwasaki.

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Clustering of Country-Based Data in COVID-19 Infections by Coronavirus outbreak features – First wave, Data up to date 28/5/2020

Authors: Akad Doha, Markman Ofer and Lefkort Jared

 

This study investigated connections between the infection cycles of countries around the world. Utilizing factors such as the Day of Maximum Infections, the Total Infections and the Day of Maximum Infections, and Deaths and Recoveries per Million. In addition, countries that have completed the infection cycle were compared to understand similarities and differences amongst the aforementioned factors and others.

Note: All variables are reportedly up to date 28/5.

The variables:

Country

State / status – The state of the outbreak

Daily_peak – Maximum number of new daily infections

Total_at_daily_peak – The number of infections from the beginning of the outbreak to the maximum day of the new infections.

Death_per_m – The deaths per million people

Recovered_per_m – The recovery cases per million people

Continent – Continent

Time_to_peak- Time from day to the maximum day of new infections.

Break_time – Time in days from the maximum day for new infections until fading (only in countries that have significantly decreased the number of infections, which means that they can be considered in the end)

Total_time- Time from the day of first outbreak to the end.

 

 

Clustering:

Figure 1. Classification 1, Clustering Based on the variables – the number of new daily infections , the number of infections from the beginning of the outbreak to the maximum day of infections , the deaths per million people , the recoveries per million people , the time to the maximum day for new infections.

Cluster 1 – red – characterized by:

  • The number of new daily maximum infections below average
  • The number of infections from the beginning of the outbreak to the maximum daily infections below average
  • Deaths per million persons below average
  • Recoveries per million less than the number of deaths and below average.

Cluster 2 – blue – characterized by:

  • The number of new daily infections usually above average Deaths per million people above average
  • Recoveries per million above average yet less than deaths
  • Time to the maximum day for new infections less than average.

 

Figure 2. Classification 2, Clustering Based on the variables – the number of new daily infections, the number of infections from the beginning of the outbreak to the maximum day of infections, the deaths per million people , the recovery cases per million people.

Cluster 1 (red): The number of new daily infections is less than average, the number of infections from the beginning of the outbreak to the maximum day of the new infections is almost average, deaths to one million people on average, recovery cases per million people above average

Cluster 2 ( green): the number of new daily maximum infections above average, the number of infections from the beginning of the outbreak to the maximum daily infections most often above average, yet less than the maximum daily new infections, the deaths per million above average, the recoveries per million above average, but less than deaths.

Cluster 3 (blue): maximum number of new daily infections smaller than average and smaller than cluster 1 , the number of infections since the beginning of the outbreak to the maximum new infections below the average, deaths per million people below average, recoveries per million people under the average and lower than deaths.

 

Figure 3. Classification 3, Cluster (clustering) Based on all variables for countries that have already completed the outbreak cycle.

Cluster 1 (red): maximum number of daily new infections above average, number of infections from the initial outbreak to the maximum day of new infections above average, recoveries per million people below average, the fading time below average, and total time to completion of outbreak circle below average.

Cluster 2 ( blue ): maximum number of daily new infections below average, number of infections from the initial outbreak to the maximum day of new infections less than average, fading time usually above average and not necessarily over cluster 1, and the total time to the end of the outbreak cycle above average.

This classification is done based on a small number of countries since there are a lack of countries who have completed the outbreak circle, so we will use it only to understand what kinds of classifications we receive if there is a fading time and total time.

Figure 4. World map by classification 1:

The map shows that the countries of Asia, Northeastern Europe, Africa, Central America and South America, and some of North America are classified by Cluster 1, which means that they have Cluster 1 characteristics.

Western Europe, Eastern South America, part of North America belongs to Cluster 2. (Please refer to Cluster properties in explanation of Figure 3)

 

Figure 5. World Map by Classification 2:

Northern North America, South America, the Middle East, parts of Europe, and North Asia are classified as Cluster 3.

Western Europe, Southeastern America, and some of North America are classified as Cluster 2.

East Asia, Africa, parts of Northern Europe, parts of South America and Central America are classified into Cluster 3. (Please refer to Cluster properties in explanation of Figure 2).

 

Figure 6. Summary Classification – Combining the two classifications 1 and 2:

Cluster 1 (red) is characterized by a maximum number of new infections larger than average (highest number of maximum daily infections), the number of infections since the beginning of the outbreak to the day of maximum new daily infections more than or equal to the average, deaths above average and above cluster 4, recoveries per million people over the average, yet less than deaths.

Cluster 2 (green) is characterized by the maximum number of daily new infections close to average and tends to be above average in most cases, the number of infections since the beginning of the outbreak to the day of maximum new daily infections almost average, deaths mostly at or above average, but below cluster 1, recoveries per million above average and greater than the deaths.

Cluster 3 (blue) is characterized by a maximum number of new infections below average, the number of infections since the beginning of the outbreak to the day of maximum new daily infections less than or equal to the average, deaths below average (lowest deaths) , recoveries per million people below average and less than deaths.

Cluster 4 (Purple) is characterized by a maximum number of new infections below average, the number of infections since the beginning of the outbreak to the day of maximum new daily infections below average, deaths above average and above clusters 2 and 3, recoveries per million above average and above deaths (greatest amount of recoveries)

 

Figure 7. Distribution of time until the maximum day of New infections by the summary classification.

Cluster 3 has the highest average time up to the maximum day for new infections, followed by Cluster 1, then Cluster 2 and Cluster 4 with the lowest average.

 

Figure 8. The world map is classified according to the summery classification:

Southern South America, parts of North America, and Western Europe are classified as Cluster 1.

Table 1. countries in first cluster:

Status Country
Ongoing USA
Subsiding Belgium
Subsiding UK
Subsiding Italy
Ongoing Brazil
Subsiding France
Subsiding Spain

 

Western South America, parts of North America, the Middle East, North Asia and some parts of Europe are classified as Cluster 2.

Table 2. countries in second cluster:

status country status country
ongoing Panama ongoing Russia
completed Norway subsiding Turkey
subsiding Germany reemerged Iran
ongoing Peru ongoing Canada
subsiding Netherlands ongoing Saudi Arabia
ongoing Sweden ongoing Chile
completed Israel subsiding Portugal
completed Austria subsiding Ecuador
    subsiding Denmark

 

Parts of America, Africa, East Asia and parts of Europe are classified into Cluster 3.

Table 3. countries in second cluster:

status country status country
ongoing South Africa ongoing Poland
ongoing Philippines ongoing Mexico
ongoing Dominican Republic ongoing India
ongoing Egypt ongoing Pakistan
completed South Korea ongoing Bangladesh
subsiding Czechia ongoing Ukraine
ongoing Argentina ongoing Indonesia
ongoing Algeria subsiding Romania
subsiding Finland completed Japan
subsiding Hungary ongoing Colombia
    completed China

 

Small parts of Western Europe are classified into Cluster 4. (Please refer to Cluster properties in explanation of Figures 6 and 7)

Table 4. countries in second cluster:

status country
completed Switzerland
completed Ireland

 

Interesting discovery:

While searching the variables that contribute to a clearer picture of the world situation, some countries were found to have a day that repeats every week, characterized by the minimum number of deceased from coronavirus. These countries include: The United States, Brazil, the Netherlands, Sweden, and Israel.

In addition, India had a day characterized by a maximum number of new infections that repeats every week.

Peru had a devoted day that repeats every week characterized by a minimum number of new infections.

Statistical insights appendix:

 

Figure 9. The quantum of the quantitative variables

We can see that:

  1. The maximum number of new daily infections in most countries is less than 10000 people. In individual cases over 10000.
  2. The number of deaths from the virus in most countries is less than 200 people per million.
  3. The number of people who have recovered from the virus in most countries are under 2000 people per million.
  4. The maximum time to date for new infections varies by country and there is no common reservation for a number of days, but from the chart it can be assumed that most countries are below 80 days for maximum full outbreak.
  5. The number of infections from the beginning of the outbreak to the maximum day for new infections in most countries does not exceed 250000 infections.

 

Relationships and adjustments between variables:

 

Figure 10. Correlation between the different variables

The most prominent correlations between the variables are:

  1. The number of new daily infections in the maximum day for new infections and the number of infections from the beginning of the outbreak to the maximum day for new infections. Indicates a strong positive correlation.
  2. Between the number of deaths and the number of recoveries a moderate positive correlation exists.
  3. Between the number recoveries per million and the time to maximum day of new infections a moderate negative correlation exists.

 

Figure 11. Correlation of all variables Countries that completed the outbreak cycle:

The most prominent correlations between the variables are:

  1. The number of new daily infections in the maximum day for new infections and the number of infections from the beginning of the outbreak to the maximum day for new infections. Indicates a very strong positive correlation.
  2. Between the number of deaths and the number of recoveries correlates strong positive.
  3. The number of infections that have healed, the maximum number of new daily cases and the number of infections from the beginning of the outbreak to the maximum day of new infections has a negative medium correlation.
  4. Between the time of the outbreak fading and the time of the complete outbreak cycle there is a very strong positive correlation.
  5. The maximum number of daily new infections and outbreak fading time and all the time of outbreak cycle has a strong negative correlation.
  6. Between the number of infections from the onset of the outbreak to the maximum day for new infections, the time of outbreak fading and the whole time of the complete outbreak cycle has a very strong negative correlation.

* consider that the correlations are based on a small number of countries, so there may be biases in the correctness of adjustment with the true situation. If there were more countries that have completed the outbreak cycle would have been more precise – recommends future research.

 

Figure 12. Diagram of the correlation between variables by PCA analysis (For all countries)

 

The diagram shows the relationships between all variables, they can be interpreted as follows:

  • As the total number of infections from the onset of the outbreak to the maximum day for new infections increases, the number of maximum new daily infections increases.
  • As the number of deaths increases, the number of recovered patients also increases.
  • As the time to the maximum day for new infections decreases, the number of recovered patients increases.
  • The variables depicted in red represent those that are significant to understanding the world data, and conversely, the variables in blue are less significant, but are also necessary in understanding the data. Therefore, subsequently, one analysis was performed including the maximum day for new infections variable, and one was performed without it.

 

Figure 13. Diagram of the correlation between variables by PCA analysis (Countries that have completed the outbreak cycle)

Chart is prepared to show the connections of the variables with two variables that were found only in countries that have completed the outbreak cycle, 1. Fading time 2. The total time to completion.

  • As the time between the reduction of infection rates and the day of maximum infections increases, so does the total length of the infection cycle. And it seems that a negative relationship exists between this relationship and time to the maximum day of new infections.
  • As the fading time and time to end decreases, the total number of infections in the maximum day of new infections and new daily infections number increases (very interesting).

Reference:

The data was collected from:

https://ourworldindata.org/covid-deaths

 

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Online Event: Vaccine matters: Can we cure coronavirus? An AAAS Webinar on COVID19: 8/12/2020

Reporter: Stephen J. Williams. PhD

Source: Online Event

Top on the world’s want list right now is a coronavirus vaccine. There is plenty of speculation about how and when this might become a reality, but clear answers are scarce.Science/AAAS, the world’s leading scientific organization and publisher of the Science family of journals, brings together experts in the field of coronavirus vaccine research to answer the public’s most pressing questions: What vaccines are being developed? When are we likely to get them? Are they safe? And most importantly, will they work?

link: https://view6.workcast.net/AuditoriumAuthenticator.aspx?cpak=1836435787247718&pak=8073702641735492

Presenters

Presenter
Speaker: Sarah Gilbert, Ph.D.

University of Oxford
Oxford, UK
View Bio

Presenter
Speaker: Kizzmekia Corbett, Ph.D.

National Institute of Allergy and Infectious Diseases, NIH
Bethesda, MD
View Bio

Presenter
Speaker: Kathryn M. Edwards, M.D.

Vanderbilt Vaccine Research Program
Nashville, TN
View Bio

Presenter
Speaker: Jon Cohen

Science/AAAS
San Diego, CA
View Bio

Presenter
Moderator: Sean Sanders, Ph.D.

Science/AAAS
Washington, DC
View Moderator Bio

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Dmitry Korkin is a professor of bioinformatics and computational biology at Worcester Polytechnic Institute, where he specializes in bioinformatics of complex disease, computational genomics, systems biology, and biomedical data analytics. I came across Dmitry’s work when in February his group used the viral genome of the COVID-19 to reconstruct the 3D structure of its major viral proteins and their interactions with human proteins, in effect creating a structural genomics map of the coronavirus and making this data open and available to researchers everywhere. We talked about the biology of COVID-19, SARS, and viruses in general, and how computational methods can help us understand their structure and function in order to develop antiviral drugs and vaccines.
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Recent Grim COVID-19 Statistics in U.S. and Explanation from Dr. John Campbell: Why We Need to be More Proactive

Reporter: Stephen J. Williams, Ph.D.

In case you have not been following the excellent daily YouTube sessions on COVID-19 by Dr. John Campbell I am posting his latest video on how grim the statistics have become and the importance of using proactive measures (like consistent use of facial masks, proper social distancing) instead of relying on reactive measures (e.g. lockdowns after infection spikes).  In addition, below the video are some notes from his presentation and some links to sites discussed within the video.

 

Notes from the video:

  • approaching 5 million confirmed cases in US however is probably an underestimation
  • 160,00 deaths as of 8/08/2020

From the University of Washington Institute for Health Metrics and Evaluation in Seattle WA

  • 295,000 US COVID-19 related deaths estimated by December 1, 2020
  • however if 95% of people in US consistently and properly wear masks could save 66,000 lives
  • however this will mean a remaining 228,271 deaths which is a depressing statistic
  • Dr. John Campbell agrees with Dr. Christopher Murray, director of the Institute for Health Metrics that “people’s inconsistent use of these measures (face masks, social distancing) is a serious problem”
  • States with increasing transmission like Colorado, Idaho, Kansas, Kentucky, Mississippi, Missouri, Ohio, Oklahoma, Oregon, and Virginia are suggested to have a lockdown when death rate reaches 8 deaths per million population however it seems we should be also focusing on population densities rather than geographic states
  • Dr. Campbell and Dr. Murray stress more proactive measures than reactive ones like lockdowns
  • if mask usage were to increase to 95% usage reimposition to shutdown could be delayed 6 to 8 weeks

 

New IHME COVID-19 Forecasts See Nearly 300,000 Deaths by December 1

SEATTLE (August 6, 2020) – America’s COVID-19 death toll is expected to reach nearly 300,000 by December 1; however, consistent mask-wearing beginning today could save about 70,000 lives, according to new data from the Institute for Health Metrics and Evaluation (IHME) at the University of Washington’s School of Medicine.The US forecast totals 295,011 deaths by December. As of today, when, thus far, 158,000 have died, IHME is projecting approximately 137,000 more deaths. However, starting today, if 95% of the people in the US were to wear masks when leaving their homes, that total number would decrease to 228,271 deaths, a drop of 49%. And more than 66,000 lives would be saved.Masks and other protective measures against transmission of the virus are essential to staying COVID-free, but people’s inconsistent use of those measures is a serious problem, said IHME Director Dr. Christopher Murray.

“We’re seeing a rollercoaster in the United States,” Murray said. “It appears that people are wearing masks and socially distancing more frequently as infections increase, then after a while as infections drop, people let their guard down and stop taking these measures to protect themselves and others – which, of course, leads to more infections. And the potentially deadly cycle starts over again.”

Murray noted that there appear to be fewer transmissions of the virus in Arizona, California, Florida, and Texas, but deaths are rising and will continue to rise for the next week or two. The drop in infections appears to be driven by the combination of local mandates for mask use, bar and restaurant closures, and more responsible behavior by the public.

“The public’s behavior had a direct correlation to the transmission of the virus and, in turn, the numbers of deaths,” Murray said. “Such efforts to act more cautiously and responsibly will be an important aspect of COVID-19 forecasting and the up-and-down patterns in individual states throughout the coming months and into next year.”

Murray said that based on cases, hospitalizations, and deaths, several states are seeing increases in the transmission of COVID-19, including Colorado, Idaho, Kansas, Kentucky, Mississippi, Missouri, Ohio, Oklahoma, Oregon, and Virginia.

“These states may experience increasing cases for several weeks and then may see a response toward more responsible behavior,” Murray said.

In addition, since July 15, several states have added mask mandates. IHME’s statistical analysis suggests that mandates with no penalties increase mask wearing by 8 percentage points. But mandates with penalties increase mask wearing by 15 percentage points.

“These efforts, along with media coverage and public information efforts by state and local health agencies and others, have led to an increase in the US rate of mask wearing by about 5 percentage points since mid-July,” Murray said. Mask-wearing increases have been larger in states with larger epidemics, he said.

IHME’s model assumes that states will reimpose a series of mandates, including non-essential business closures and stay-at-home orders, when the daily death rate reaches 8 per million. This threshold is based on data regarding when states and/or communities imposed mandates in March and April, and implies that many states will have to reimpose mandates.

As a result, the model suggests which states will need to reimpose mandates and when:

  • August – Arizona, Florida, Mississippi, and South Carolina
  • September – Georgia and Texas
  • October – Colorado, Kansas, Louisiana, Missouri, Nevada, North Carolina, and Oregon.
  • November – Alabama, Arkansas, California, Iowa, New Mexico, Oklahoma, Utah, Washington, and Wisconsin.

However, if mask use is increased to 95%, the re-imposition of stricter mandates could be delayed 6 to 8 weeks on average.

Source: http://www.healthdata.org/news-release/new-ihme-covid-19-forecasts-see-nearly-300000-deaths-december-1

 

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