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


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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Apr 22, 2020

496K subscribers

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.
This conversation is part of the Artificial Intelligence podcast.
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OUTLINE: 0:00 – Introduction 2:33 – Viruses are terrifying and fascinating 6:02 – How hard is it to engineer a virus? 10:48 – What makes a virus contagious? 29:52 – Figuring out the function of a protein 53:27 – Functional regions of viral proteins 1:19:09 – Biology of a coronavirus treatment 1:34:46 – Is a virus alive? 1:37:05 – Epidemiological modeling 1:55:27 – Russia 2:02:31 – Science bobbleheads 2:06:31 – Meaning of life
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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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via Key Immune System Genes Identified to Explain High COVID Deaths and Spread in Northern Italy Versus Fewer Cases and Deaths in the South

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The Inequality and Health Disparity seen with the COVID-19 Pandemic Is Similar to Past Pandemics

Curator: Stephen J. Williams, PhD

2019-nCoV-CDC-23311

It has become very evident, at least in during this pandemic within the United States, that African Americans and poorer communities have been disproportionately affected by the SARS-CoV2 outbreak . However, there are many other diseases such as diabetes, heart disease, and cancer in which these specific health disparities are evident as well :

Diversity and Health Disparity Issues Need to be Addressed for GWAS and Precision Medicine Studies

Personalized Medicine, Omics, and Health Disparities in Cancer:  Can Personalized Medicine Help Reduce the Disparity Problem?

Disease like cancer have been shown to have wide disparities based on socioeconomic status, with higher incidence rates seen in poorer and less educated sub-populations, not just here but underdeveloped countries as well (see Opinion Articles from the Lancet: COVID-19 and Cancer Care in China and Africa) and graphics below)

 

 

 

 

 

 

 

 

 

 

In an article in Science by Lizzie Wade, these disparities separated on socioeconomic status, have occurred in many other pandemics throughout history, and is not unique to the current COVID19 outbreak.  The article, entitled “An Unequal Blow”, reveal how

in past pandemics, people on the margins suffered the most.

Source: https://science.sciencemag.org/content/368/6492/700.summary

Health Disparities during the Black Death Bubonic Plague Pandemic in the 14th Century (1347-1351)

During the mid 14th century, all of Europe was affected by a plague induced by the bacterium Yersinia pestis, and killed anywhere between 30 – 60% of the European population.  According to reports by the time the Black Death had reached London by January 1349 there had already been horrendous reports coming out of Florence Italy where the deadly disease ravished the population there in the summer of 1348 (more than half of the city’s population died). And by mid 1349 the Black Death had killed more than half of Londoners.  It appeared that no one was safe from the deadly pandemic, affecting the rich, the poor, the young, the old.

However, after careful and meticulous archaeological and historical analysis in England and other sites, revealed a distinct social and economic inequalities that predominated and most likely guided the pandemics course throughout Europe.   According to Dr. Gwen Robbins Schug, a bio-archaeologist at Appalachian State University,

Bio-archaeology and other social sciences have repeatedly demonstrated that these kinds of crises play out along the preexisting fault lines of each society.  The people at greatest risk were often those already marginalized- the poor and minorities who faced discrimination in ways that damaged their health or limited their access to medical care even in pandemic times.

At the start of the Black Death, Europe had already gone under a climactic change with erratic weather.  As a result, a Great Famine struck Europe between 1315-17.  Wages fell and more people fell into poverty while the wealthiest expanded their riches, leading to an increased gap in wealth and social disparity.  In fact according to recordkeeping most of Englanders were living below the poverty line.

Author Lizzie Wade also interviewed Dr. Sharon, DeWitte, a biological anthropologist at University of South Carolina, who looks at skeletal remains of Black Death victims to get evidence on their health status, like evidence of malnutrition, osteoporosis, etc.   And it appears that most of the victims may have had preexisting health conditions indicative of poorer status.  And other evidence show that wealthy landowners had a lower mortality rate than poorer inner city dwellers.

1918 Spanish Flu

Socioeconomic and demographic studies have shown that both Native American Indians and African Americans on the lower end of the socioeconomic status were disproportionately affected by the 1918 Spanish flu pandemic.  According to census records, the poorest had a 50% higher mortality rate than wealthy areas in the city of Oslo.  In the US, minors and factory workers died at the highest rates.  In the US African Americans had already had bouts with preexisting issues like tuberculosis and may have contributed to the higher mortality.  In addition Jim Crow laws in the South, responsible for widespread discrimination, also impacted the ability of African Americans to seek proper medical care.

From the Atlantic

Source: https://www.theatlantic.com/politics/archive/2016/05/americas-health-segregation-problem/483219/

America’s Health Segregation Problem

Has the country done enough to overcome its Jim Crow health care history?

VANN R. NEWKIRK II

MAY 18, 2016

Like other forms of segregation, health-care segregation was originally a function of explicitly racist black codes and Jim Crow laws. Many hospitals, clinics, and doctor’s offices were totally segregated by race, and many more maintained separate wings or staff that could never intermingle under threat of law. The deficit of trained black medical professionals (itself caused by a number of factors including education segregation) meant that no matter where black people received health-care services, they would find their care to be subpar compared to that of whites. While there were some deaths that were directly attributable to being denied emergency service, most of the damage was done in establishing the same cumulative health disparities that plague black people today as a societal fate. The descendants of enslaved people lived much more dangerous and unhealthy lives than white counterparts, on disease-ridden and degraded environments. Within the confines of a segregated health-care system, these factors became poor health outcomes that shaped black America as if they were its genetic material.

 

https://twitter.com/time4equity/status/1175080469425266688?s=20

 

R.A.HahnaB.I.TrumanbD.R.Williamsc.Civil rights as determinants of public health and racial and ethnic health equity: Health care, education, employment, and housing in the United States.

SSM – Population Health: Volume 4, April 2018, Pages 17-24

Highlights

  • Civil rights are characterized as social determinants of health.
  • Four domains in civil rights history since 1950 are explored in—health care, education, employment, and housing.
  • Health care, education, employment show substantial benefits when civil rights are enforced.
  • Housing shows an overall failure to enforce existing civil rights and persistent discrimination.
  • Civil rights and their enforcement may be considered a powerful arena for public health theorizing, research, policy, and action.

 

For more articles on COVID-19 Please go to our Coronovirus Portal

https://pharmaceuticalintelligence.com/coronavirus-portal/

 

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Study with important implications when considering widespread serological testing, Ab protection against re-infection with SARS-CoV-2 and the durability of vaccine protection

Reporter: Aviva Lev-Ari, PhD, RN

Serological Testing WordCloud

Longitudinal evaluation and decline of antibody responses in SARS-CoV-2 infection

Jeffrey SeowCarl GrahamBlair MerrickSam AcorsKathryn J.A. SteelOliver HemmingsAoife O’BryneNeophytos KouphouSuzanne PickeringRui GalaoGilberto BetancorHarry D WilsonAdrian W SignellHelena WinstoneClaire KerridgeNigel TempertonLuke SnellKaren BisnauthsingAmelia MooreAdrian GreenLauren MartinezBrielle StokesJohanna HoneyAlba Izquierdo-BarrasGill ArbaneAmita PatelLorcan OConnellGeraldine O HaraEithne MacMahonSam DouthwaiteGaia NebbiaRahul BatraRocio Martinez-NunezJonathan D. EdgeworthStuart J.D. NeilMichael H. MalimKatie Doores

Abstract

Antibody (Ab) responses to SARS-CoV-2 can be detected in most infected individuals 10-15 days following the onset of COVID-19 symptoms. However, due to the recent emergence of this virus in the human population it is not yet known how long these Ab responses will be maintained or whether they will provide protection from re-infection. Using sequential serum samples collected up to 94 days post onset of symptoms (POS) from 65 RT-qPCR confirmed SARS-CoV-2-infected individuals, we show seroconversion in >95% of cases and neutralizing antibody (nAb) responses when sampled beyond 8 days POS. We demonstrate that the magnitude of the nAb response is dependent upon the disease severity, but this does not affect the kinetics of the nAb response. Declining nAb titres were observed during the follow up period. Whilst some individuals with high peak ID50 (>10,000) maintained titres >1,000 at >60 days POS, some with lower peak ID50 had titres approaching baseline within the follow up period. A similar decline in nAb titres was also observed in a cohort of seropositive healthcare workers from Guy′s and St Thomas′ Hospitals. We suggest that this transient nAb response is a feature shared by both a SARS-CoV-2 infection that causes low disease severity and the circulating seasonal coronaviruses that are associated with common colds. This study has important implications when considering widespread serological testing, Ab protection against re-infection with SARS-CoV-2 and the durability of vaccine protection.

SOURCE

https://www.medrxiv.org/content/10.1101/2020.07.09.20148429v1

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