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Monte Carlo Simulations to Democratize COVID-19 Policies*

Monte Carlo Simulations to Democratize COVID-19 Policies*

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COVID-19 Projections Collaboration**

Eugene Kolker, PhD
Eugene Kolker, PhD
Member, COVID-19 Projections Collaboration

We offer the scientific, government, business, and policy communities a simulation tool to predict and monitor the effects of the changing dynamics of coronavirus disease 2019 (COVID-19) on the overall fatality rate, one of the most significant determinants of pandemic policy. This tool enables them to predict and monitor weekly fatalities while optimizing public health and economic wellbeing.

Given the variations in model-derived predictions, and despite striving for a consensus, experts often disagree about likely pandemic evolution. It is therefore essential to develop new complementary approaches to pandemic modeling to ease tensions and find an evidence-based, rather than trial-and-error-based, pathway to recovery.

Background and guidelines for reopening

We propose democratizing the policy response through a policy-relevant, user-centric, SIR-driven (Susceptibility, Infection, and Recovery),1 and robust approach using Monte Carlo simulations2 of COVID-19 fatalities. We are a team of researchers, physicians, executives, managers, and students with medical, legal, technical, and academic backgrounds.

This article is our collaborative and volunteer-driven contribution to the ongoing efforts to protect lives and minimize fatalities while steps are taken to restart the American economy. With minor modifications, the proposed approach is applicable worldwide to any country, province, state, county, and city with a population of approximately 1 million or more.

So far, the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) pandemic has infected over 7 million people worldwide with COVID-19 and killed over 400,000 people.3–5 The United States has been hit especially hard, with over 100,000 deaths, a quarter of worldwide fatalities.3–5 [For a breakdown of U.S. statistics, please visit the COVID-19 Projections Collaboration website .6 There, a Supplementary Materials page includes the breakdown in Table S1. Demographics and COVID-19 Data.]

The pandemic has led to widespread quarantines, social distancing rules, and shutdowns at the federal, state, county, and city levels. These measures have been effective to slow the spread of SARS-CoV-2, minimize fatalities, and enable the healthcare system to keep up with immediate surges in demand and save lives.3–10

As infection rates and fatalities decline, experts and policymakers are introducing guidelines for gradual reopening.9 [For a summary of these guidelines, please visit the COVID-19 Projections Collaboration website, which includes a page titled Guidelines for Reopening.6] These efforts are paired with legitimate concerns of viral resurgence if proper measures are not taken. Many states are announcing plans to reopen in phases, with each phase gradually relaxing earlier restrictions. These plans are heavily influenced by epidemiological and public health models, for which we propose an enhancement.

Monte Carlo simulations

We propose a Monte Carlo simulation approach2 to estimate the ranges of key parameters relevant to the questions: “When will things return to a new ‘norm’?” and “What is the best way to monitor the rapidly changing situation at regional levels?” Monte Carlo simulations use predefined probability distributions of key input variables to calculate unknown outcomes, such as how many fatalities could occur.

Using computation power, these calculations undergo thousands of iterations, each generating a series of outputs, leading to a range of estimates with a higher confidence than a single point estimate.

The proposed Monte Carlo simulation approach is built with four customizable parameters: Basic Reproduction Number (a measure of rate of transmission, R0), Infection Fatality Rate (IFR), Weeks from Infection to Recovery/Fatality, and Weekly Fatality Threshold (WFT). [For additional technical details, please visit our COVID-19 website.6]

WFT is an average of flu-like-related U.S. fatalities over the previous decade, estimated per 100,000 people, based on data from the Centers for Disease Control and Prevention,11 and rescaled for different regions, states, counties, and cities.6 Our model enables robust fatality prediction and can be used to monitor weekly outcomes. To mitigate the effects of noise, we build our simulations on data from three distinct sources.3–5

Extrapolating the number of infections

We modeled data from the seven states constituting the Northeast Pact and the three states constituting the West Coast Pact. [For additional details, see Figures S1.A & S2.A, respectively, in the Supplementary Materials presented on our website.6] Additionally, we illustrate the relevance of our approach with two sample counties: King County in Washington State and Westchester County in New York State. Weekly raw data from three sources were downloaded during a period lasting about two months and ending May 18, 2020.

In the proposed Monte Carlo simulation approach, all four parameters require Low, Likely, and High values. These values for the first three parameters (R0, IFR, and Weeks from Infection to Recovery/Fatality) are obtained from the SIR disease spread models1 for New York, the hardest-hit state.3–6 In other words, the outputs from other SIR models are being used here as the inputs for our Monte Carlo model.

The fourth parameter, WFT, should be recalculated for the flu-like-related seasonality, weekly fatalities, and different population sizes.6,11 These four customizable parameters, along with recent weekly fatality data, enable relevant, case-specific, targeted, and timely projections. We performed the Monte Carlo simulations using the SIPmath Modeler Tools12 and Microsoft Excel. [For procedural details, please visit our website, which includes a page titled Excel Instructions.6]

Using weekly fatality data from the previous week(s), one can extrapolate the number of infections two weeks prior and then project out later weeks by multiplying the number of infections by R0. [For an example of such an extrapolation, please visit our website and download the Excel document provided.6] From the number of new infections, we forecast weekly fatalities by multiplying the fatality rate distribution by the number of infected people. This is done based on the number of fatalities during the previous weeks, depending on the simulation’s “time to no longer contagious” distribution. The simulation runs 1000 times and forecasts weekly fatality. Finally, it graphs the 95th, 50th, and 5th percentile values for each week.

Our model calculated WFT for Washington State (population of ~7.62 million) to be ~65.8 and forecasted that the state’s COVID-19 fatalities would drop below the state’s WFT threshold the week of May 25 (Figure 1A). Washington State’s King County (population of ~2.25 million) is also predicted to hit below its WTF the week of May 25. [For details, visit our website’s Supplementary Materials and review Figure S1.B.6]

TM Thought Leader figure 1A
Figure 1A. Weekly fatality forecast for Washington State. Range of fatality forecast from Monte Carlo simulations. Data derived from the COVID Tracking Project3 were visualized with Tableau.

 

New York State (population of ~19.45 million) has WFT of ~167.9. It is predicted that the state’s COVID-19 fatalities trend below the state’s WFT the week of June 15 (Figure 1B). New York State’s Westchester County (population of ~1 million) is predicted to drop below the county’s WFT the same week as the rest of New York State. [For details, visit our site’s Supplementary Materials and review Figure S2.B.6] These projections can inform decisions on when to reopen Washington State’s King County and New York State’s Westchester County, as well as when to reopen the rest of Washington State and New York State. [For more details, visit our website’s Supplementary Materials and review Table S2.6]

TM Thought Leader Figure 1B
Figure 1B. Weekly fatality forecast for New York State. Range of fatality forecast from Monte Carlo simulations. Data derived from the COVID Tracking Project3 were visualized with Tableau.

Accuracy, limitations, and future developments

The accuracy of the predictions that COVID-19 fatalities decrease below WFT (expected number of flu-like fatalities in an average year) for the same week was thoroughly evaluated. The forecasts vary ±1 week with ±50% variation of WFT with location. The approach always points to the most conservative estimates derived from different data sources.3–6

Like any model, ours is only as good as its assumptions. We hope that our approach can underscore the importance of obtaining more accurate values for key parameters. Better estimates of R0, numbers of infected people, and population density in various locations will yield better estimates of IFR.

R0 can vary with geography, weather, and public health measures. Additionally, IFR might change as medical interventions improve. Also, uncertainty remains regarding the number of fatalities directly caused by SARS-CoV-2. The model’s accuracy would improve with tighter ranges for the customizable parameters in various regions. Future models can include metrics on randomly sampled antibody rates around the United States for improved accuracy. [For technical details, please visit our website.6] If (any of) our assumptions fail, the proposed model can be readily rerun (on weekly or daily basis) with a revised distribution for R0.

Per George Box and Norman Draper,13 “Essentially, all models are wrong, but some are useful.” We did strive to develop a relatively simple, easy to use, and useful model. We hope our approach can underscore the importance of obtaining more accurate, diverse data for key parameters for more advanced and accurate models.

Summary

The proposed approach can be used to project and monitor fatalities at the country, province, state, county, and city levels provided that a given population size is large enough (over 1 million) to accurately estimate a range for R0. The model simulates key pandemic parameters, including IFR, Weeks from Infection to Recovery/Fatality, WFT, and a range of R0 values derived from existing epidemiological data for a given location. Our model can be especially useful for experts and policymakers who may lack access to reliable data regarding R0 for their districts and may, consequently, face tremendous uncertainty over possible changes in R0 as states begin to reopen.

This approach empowers policymakers and healthcare and business professionals to make better-informed, data-driven decisions on how to begin to reopen and how to proactively monitor afterward. The proposed Monte Carlo SIR-derived, robust approach is available not only to experts (policymakers, physicians, and healthcare managers), but also to the public at large via a downloadable Excel file. [For details, visit our website.6] We welcome actionable feedback from the users of our Monte Carlo model.

 

*This article is an edited and abbreviated version of a more formal academic paper that can be found at wp.nyu.edu/kolker/covid-19.

**Members of the COVID-19 Projections Collaboration are as follows: Eugene Kolker, PhD, Alexander Huber, Gurkirat Singh Sekhon, Sritham Thyagaraju, Minghao Fu, Isaac M. Krasnopolsky, Andrea Davidovich, Marita Acheson, MD, Dmitri Adler, Anthony M. Avellino, MD, MBA, Philip A. Bernstein, PhD, Paul E. Buehrens, MD, FAAFP, Patrick J. Boyd, JD, Drexel DeFord, MPA, MSHI, Yakov Grinberg, MD, Rose Guerrero, MD, Dawn Josephson, Raif Khassanov, Evelyne Kolker, Eugene Luskin, Aliona Rudys, MD, Irine Vaiman, MD, and Aleksandr Zhuk, PhD. Alexander Huber, Gurkirat Singh Sekhon, Sritham Thyagaraju, and Minghao Fu contributed equally to this article. Please address correspondence to Eugene Kolker (eugene@kolker.ai).

References
1. Anuj M, Christopher K, Viswanathan A, Carlos C. Studying complexity and risk through stochastic population dynamics: Persistence, resonance, and extinction in ecosystems. In: Rao ASRS, Rao CR, eds. Handbook of Statistics. Amsterdam: Elsevier; 2019; 40: 157–193.
2. Rubinstein RY, Kroese DP. Simulation and the Monte Carlo Method. 3rd edit. Hoboken, NJ: John Wiley & Sons; 2016.
3. Our Data. The COVID Tracking Project.
4. COVID-19 Projections. Institute for Health Metrics and Evaluation, University of Washington.
5. COVID-19 Coronavirus Pandemic. Worldometer.
6. Monte Carlo Simulations to Predict and Monitor Reopening after COVID-19 Outbreak. The COVID-19 Web Resource.
7. Chaney S, Morath E. April Unemployment Rate Rose to a Record 14.7%. Wall Street Journal. May 8, 2020.
8. Allen D, Block S, Cohen J, et al. Roadmap to Pandemic Resilience. Harvard University. April 20, 2020.
9. Rivers C, Martin E, Watson C, et al. Public Health Principles for a Phased Reopening During COVID-19: Guidance for Governors. John Hopkins Center for Health Security. April 17, 2020.
10. Kissler SM, Tedijanto C, Goldstein E, Grad YH, Lipsitch M. Projecting the transmission dynamics of SARS-CoV-2 through postpandemic period. Science 2020; 368: 860–868. DOI: 10.1126/science.abb5793.
11. a href=”https://www.cdc.gov/nchs/fastats/”>National Center for Health Statistics. Centers for Disease Control and Prevention.
12. Probability Management. https://www.probabilitymanagement.org.
13. Box GEP, Draper NR. Empirical Model-Building and Response Surfaces. p. 424. Hoboken, NJ: John Wiley & Sons; 1987.

Interview with Eugene Kolker, PhD

GEN: Gene, you are not an expert in epidemiology or virology. What prompted you to tackle a project involving COVID-19?

My research led to two huge questions: “When will things return to a new ‘norm’?” and “What is the best way to monitor the rapidly changing situation at regional levels?” I assembled and led a multidisciplinary team of researchers, physicians, managers, and students. Together, we developed a data science breakthrough approach to forecast reopening and conduct follow-up monitoring of COVID-19 fatalities on the federal, state, county, and city levels.

We felt it essential to develop complementary approaches to pandemic modeling to ease tensions (in an already intense election year) and find an evidence-based pathway to recovery that could obviate trial and error. Indeed, I did not specialize in epidemiology or virology. My doctorate is in bioinformatics and computational biology from the Weizmann Institute of Science, and I was the co-founding editor of Big Data and OMICS: A Journal of Integrative Biology, published by Mary Ann Liebert, Inc. It was vital to think out of the box, so we decided to utilize the outputs from other SIR models as the inputs for our Monte Carlo Simulations.

As a result, we wrapped our model into a downloadable Excel file, which can be used not only by the experts (policymakers, physicians, and healthcare managers), but also by the public at large.

GEN: Could you tell us about your background?

My dad was an engineer, and my mom was a pediatrician. As far back as I remember, I always wanted to help people, especially those in pain. After earning my PhD, I consulted on and off, worked at several enterprises, and co-founded three startups.

After the first startup was acquired, almost for a decade I worked at Seattle Children’s, helping our clients (patients and families). I subsequently moved to New York and joined IBM working with clients on a global scale. I also teach AI at NYU. My technical forte is robust models, data/AI/analytics solutions, and learning/teaching. My five key principles are: 1) respect people, humor, and data; 2) master your work; 3) question basic assumptions; 4) challenge conventional opinions; and 5) take calculated risks.

Last month, I joined DataArt, a global software engineering firm that takes a “human approach” to solving problems. I am super excited to help our clients in any way I can.

The post Monte Carlo Simulations to Democratize COVID-19 Policies* appeared first on GEN - Genetic Engineering and Biotechnology News.

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Are Voters Recoiling Against Disorder?

Are Voters Recoiling Against Disorder?

Authored by Michael Barone via The Epoch Times (emphasis ours),

The headlines coming out of the Super…

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Are Voters Recoiling Against Disorder?

Authored by Michael Barone via The Epoch Times (emphasis ours),

The headlines coming out of the Super Tuesday primaries have got it right. Barring cataclysmic changes, Donald Trump and Joe Biden will be the Republican and Democratic nominees for president in 2024.

(Left) President Joe Biden delivers remarks on canceling student debt at Culver City Julian Dixon Library in Culver City, Calif., on Feb. 21, 2024. (Right) Republican presidential candidate and former U.S. President Donald Trump stands on stage during a campaign event at Big League Dreams Las Vegas in Las Vegas, Nev., on Jan. 27, 2024. (Mario Tama/Getty Images; David Becker/Getty Images)

With Nikki Haley’s withdrawal, there will be no more significantly contested primaries or caucuses—the earliest both parties’ races have been over since something like the current primary-dominated system was put in place in 1972.

The primary results have spotlighted some of both nominees’ weaknesses.

Donald Trump lost high-income, high-educated constituencies, including the entire metro area—aka the Swamp. Many but by no means all Haley votes there were cast by Biden Democrats. Mr. Trump can’t afford to lose too many of the others in target states like Pennsylvania and Michigan.

Majorities and large minorities of voters in overwhelmingly Latino counties in Texas’s Rio Grande Valley and some in Houston voted against Joe Biden, and even more against Senate nominee Rep. Colin Allred (D-Texas).

Returns from Hispanic precincts in New Hampshire and Massachusetts show the same thing. Mr. Biden can’t afford to lose too many Latino votes in target states like Arizona and Georgia.

When Mr. Trump rode down that escalator in 2015, commentators assumed he’d repel Latinos. Instead, Latino voters nationally, and especially the closest eyewitnesses of Biden’s open-border policy, have been trending heavily Republican.

High-income liberal Democrats may sport lawn signs proclaiming, “In this house, we believe ... no human is illegal.” The logical consequence of that belief is an open border. But modest-income folks in border counties know that flows of illegal immigrants result in disorder, disease, and crime.

There is plenty of impatience with increased disorder in election returns below the presidential level. Consider Los Angeles County, America’s largest county, with nearly 10 million people, more people than 40 of the 50 states. It voted 71 percent for Mr. Biden in 2020.

Current returns show county District Attorney George Gascon winning only 21 percent of the vote in the nonpartisan primary. He’ll apparently face Republican Nathan Hochman, a critic of his liberal policies, in November.

Gascon, elected after the May 2020 death of counterfeit-passing suspect George Floyd in Minneapolis, is one of many county prosecutors supported by billionaire George Soros. His policies include not charging juveniles as adults, not seeking higher penalties for gang membership or use of firearms, and bringing fewer misdemeanor cases.

The predictable result has been increased car thefts, burglaries, and personal robberies. Some 120 assistant district attorneys have left the office, and there’s a backlog of 10,000 unprosecuted cases.

More than a dozen other Soros-backed and similarly liberal prosecutors have faced strong opposition or have left office.

St. Louis prosecutor Kim Gardner resigned last May amid lawsuits seeking her removal, Milwaukee’s John Chisholm retired in January, and Baltimore’s Marilyn Mosby was defeated in July 2022 and convicted of perjury in September 2023. Last November, Loudoun County, Virginia, voters (62 percent Biden) ousted liberal Buta Biberaj, who declined to prosecute a transgender student for assault, and in June 2022 voters in San Francisco (85 percent Biden) recalled famed radical Chesa Boudin.

Similarly, this Tuesday, voters in San Francisco passed ballot measures strengthening police powers and requiring treatment of drug-addicted welfare recipients.

In retrospect, it appears the Floyd video, appearing after three months of COVID-19 confinement, sparked a frenzied, even crazed reaction, especially among the highly educated and articulate. One fatal incident was seen as proof that America’s “systemic racism” was worse than ever and that police forces should be defunded and perhaps abolished.

2020 was “the year America went crazy,” I wrote in January 2021, a year in which police funding was actually cut by Democrats in New York, Los Angeles, San Francisco, Seattle, and Denver. A year in which young New York Times (NYT) staffers claimed they were endangered by the publication of Sen. Tom Cotton’s (R-Ark.) opinion article advocating calling in military forces if necessary to stop rioting, as had been done in Detroit in 1967 and Los Angeles in 1992. A craven NYT publisher even fired the editorial page editor for running the article.

Evidence of visible and tangible discontent with increasing violence and its consequences—barren and locked shelves in Manhattan chain drugstores, skyrocketing carjackings in Washington, D.C.—is as unmistakable in polls and election results as it is in daily life in large metropolitan areas. Maybe 2024 will turn out to be the year even liberal America stopped acting crazy.

Chaos and disorder work against incumbents, as they did in 1968 when Democrats saw their party’s popular vote fall from 61 percent to 43 percent.

Views expressed in this article are opinions of the author and do not necessarily reflect the views of The Epoch Times or ZeroHedge.

Tyler Durden Sat, 03/09/2024 - 23:20

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Veterans Affairs Kept COVID-19 Vaccine Mandate In Place Without Evidence

Veterans Affairs Kept COVID-19 Vaccine Mandate In Place Without Evidence

Authored by Zachary Stieber via The Epoch Times (emphasis ours),

The…

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Veterans Affairs Kept COVID-19 Vaccine Mandate In Place Without Evidence

Authored by Zachary Stieber via The Epoch Times (emphasis ours),

The U.S. Department of Veterans Affairs (VA) reviewed no data when deciding in 2023 to keep its COVID-19 vaccine mandate in place.

Doses of a COVID-19 vaccine in Washington in a file image. (Jacquelyn Martin/Pool/AFP via Getty Images)

VA Secretary Denis McDonough said on May 1, 2023, that the end of many other federal mandates “will not impact current policies at the Department of Veterans Affairs.”

He said the mandate was remaining for VA health care personnel “to ensure the safety of veterans and our colleagues.”

Mr. McDonough did not cite any studies or other data. A VA spokesperson declined to provide any data that was reviewed when deciding not to rescind the mandate. The Epoch Times submitted a Freedom of Information Act for “all documents outlining which data was relied upon when establishing the mandate when deciding to keep the mandate in place.”

The agency searched for such data and did not find any.

The VA does not even attempt to justify its policies with science, because it can’t,” Leslie Manookian, president and founder of the Health Freedom Defense Fund, told The Epoch Times.

“The VA just trusts that the process and cost of challenging its unfounded policies is so onerous, most people are dissuaded from even trying,” she added.

The VA’s mandate remains in place to this day.

The VA’s website claims that vaccines “help protect you from getting severe illness” and “offer good protection against most COVID-19 variants,” pointing in part to observational data from the U.S. Centers for Disease Control and Prevention (CDC) that estimate the vaccines provide poor protection against symptomatic infection and transient shielding against hospitalization.

There have also been increasing concerns among outside scientists about confirmed side effects like heart inflammation—the VA hid a safety signal it detected for the inflammation—and possible side effects such as tinnitus, which shift the benefit-risk calculus.

President Joe Biden imposed a slate of COVID-19 vaccine mandates in 2021. The VA was the first federal agency to implement a mandate.

President Biden rescinded the mandates in May 2023, citing a drop in COVID-19 cases and hospitalizations. His administration maintains the choice to require vaccines was the right one and saved lives.

“Our administration’s vaccination requirements helped ensure the safety of workers in critical workforces including those in the healthcare and education sectors, protecting themselves and the populations they serve, and strengthening their ability to provide services without disruptions to operations,” the White House said.

Some experts said requiring vaccination meant many younger people were forced to get a vaccine despite the risks potentially outweighing the benefits, leaving fewer doses for older adults.

By mandating the vaccines to younger people and those with natural immunity from having had COVID, older people in the U.S. and other countries did not have access to them, and many people might have died because of that,” Martin Kulldorff, a professor of medicine on leave from Harvard Medical School, told The Epoch Times previously.

The VA was one of just a handful of agencies to keep its mandate in place following the removal of many federal mandates.

“At this time, the vaccine requirement will remain in effect for VA health care personnel, including VA psychologists, pharmacists, social workers, nursing assistants, physical therapists, respiratory therapists, peer specialists, medical support assistants, engineers, housekeepers, and other clinical, administrative, and infrastructure support employees,” Mr. McDonough wrote to VA employees at the time.

This also includes VA volunteers and contractors. Effectively, this means that any Veterans Health Administration (VHA) employee, volunteer, or contractor who works in VHA facilities, visits VHA facilities, or provides direct care to those we serve will still be subject to the vaccine requirement at this time,” he said. “We continue to monitor and discuss this requirement, and we will provide more information about the vaccination requirements for VA health care employees soon. As always, we will process requests for vaccination exceptions in accordance with applicable laws, regulations, and policies.”

The version of the shots cleared in the fall of 2022, and available through the fall of 2023, did not have any clinical trial data supporting them.

A new version was approved in the fall of 2023 because there were indications that the shots not only offered temporary protection but also that the level of protection was lower than what was observed during earlier stages of the pandemic.

Ms. Manookian, whose group has challenged several of the federal mandates, said that the mandate “illustrates the dangers of the administrative state and how these federal agencies have become a law unto themselves.”

Tyler Durden Sat, 03/09/2024 - 22:10

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Low Iron Levels In Blood Could Trigger Long COVID: Study

Low Iron Levels In Blood Could Trigger Long COVID: Study

Authored by Amie Dahnke via The Epoch Times (emphasis ours),

People with inadequate…

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Low Iron Levels In Blood Could Trigger Long COVID: Study

Authored by Amie Dahnke via The Epoch Times (emphasis ours),

People with inadequate iron levels in their blood due to a COVID-19 infection could be at greater risk of long COVID.

(Shutterstock)

A new study indicates that problems with iron levels in the bloodstream likely trigger chronic inflammation and other conditions associated with the post-COVID phenomenon. The findings, published on March 1 in Nature Immunology, could offer new ways to treat or prevent the condition.

Long COVID Patients Have Low Iron Levels

Researchers at the University of Cambridge pinpointed low iron as a potential link to long-COVID symptoms thanks to a study they initiated shortly after the start of the pandemic. They recruited people who tested positive for the virus to provide blood samples for analysis over a year, which allowed the researchers to look for post-infection changes in the blood. The researchers looked at 214 samples and found that 45 percent of patients reported symptoms of long COVID that lasted between three and 10 months.

In analyzing the blood samples, the research team noticed that people experiencing long COVID had low iron levels, contributing to anemia and low red blood cell production, just two weeks after they were diagnosed with COVID-19. This was true for patients regardless of age, sex, or the initial severity of their infection.

According to one of the study co-authors, the removal of iron from the bloodstream is a natural process and defense mechanism of the body.

But it can jeopardize a person’s recovery.

When the body has an infection, it responds by removing iron from the bloodstream. This protects us from potentially lethal bacteria that capture the iron in the bloodstream and grow rapidly. It’s an evolutionary response that redistributes iron in the body, and the blood plasma becomes an iron desert,” University of Oxford professor Hal Drakesmith said in a press release. “However, if this goes on for a long time, there is less iron for red blood cells, so oxygen is transported less efficiently affecting metabolism and energy production, and for white blood cells, which need iron to work properly. The protective mechanism ends up becoming a problem.”

The research team believes that consistently low iron levels could explain why individuals with long COVID continue to experience fatigue and difficulty exercising. As such, the researchers suggested iron supplementation to help regulate and prevent the often debilitating symptoms associated with long COVID.

It isn’t necessarily the case that individuals don’t have enough iron in their body, it’s just that it’s trapped in the wrong place,” Aimee Hanson, a postdoctoral researcher at the University of Cambridge who worked on the study, said in the press release. “What we need is a way to remobilize the iron and pull it back into the bloodstream, where it becomes more useful to the red blood cells.”

The research team pointed out that iron supplementation isn’t always straightforward. Achieving the right level of iron varies from person to person. Too much iron can cause stomach issues, ranging from constipation, nausea, and abdominal pain to gastritis and gastric lesions.

1 in 5 Still Affected by Long COVID

COVID-19 has affected nearly 40 percent of Americans, with one in five of those still suffering from symptoms of long COVID, according to the U.S. Centers for Disease Control and Prevention (CDC). Long COVID is marked by health issues that continue at least four weeks after an individual was initially diagnosed with COVID-19. Symptoms can last for days, weeks, months, or years and may include fatigue, cough or chest pain, headache, brain fog, depression or anxiety, digestive issues, and joint or muscle pain.

Tyler Durden Sat, 03/09/2024 - 12:50

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