Much is made of the varying models presented to the Federal Government and those used by the State Governments to determine the course of action when it comes to Covid and reducing the impact that the virus has on the overall health of the country be that in its residents or in its economy.  And what we have seen is a massive overall failure of both. This we can say was due to the CDC and WHO failing to secure clear messaging, information and processing testing to tracking and tracing the outbreak. China's unwillingness or its own political decision making clearly contributed to that but by the time this virus spread to Europe it was again largely ignored with regards to the affects that it would have globally as it literally jumped aboard planes and flew across oceans, states and countries while the band played on the the waters also landing in shores with more than good vibes to further spread chaos and disease.

As this went the modeling factors came into high gear often contradicting each other in some cases making the worst case scenario of impending doom that did little to actually assist Governors and others to make wise decisions as they had little actual guidance from any who had experienced this type of scenario, had no federal guidance and in turn political gamesmanship and alliances also led to further divisions and confusion as the virus spread. Did such individuals exist? Did the Government at one point have such a playback and in turn the ability to function as a clear leader and informational processor in this case? Yes and no. Under the Trump Administration and the GOP Leadership the funding of public health, the proverbial bogeyman of the deep state and the endless turnover and chaos of the Trump Administration and their appointing ill qualified and trained individuals into positions over their heads and skill sets lent another level of incompetence to a very already incompetent leader.

Strong medical Epidemiologists and other medical professionals have decried much of the decision making that contributed to massive collapse of the global economy from the shutting of doors, to literally closing off cities and states from one another as if they are enemy's at war.  That divisive nature set up what we see as States that re-opened versus those that are still in process and the fear factors used  to somehow defend, if not hope, that they spike with high levels of Covid to prove "they" were right all along seems to be into play at this point, and what good that is again seems to be political if not cultural in making.

The fight wars are also among the varying institutes who created the models, from the University of Washington and its model to the one the CDC uses, created by John Hopkins.  In turn the model of the Imperial College in Great Britain adds another layer of even further doom and gloom if its predictions were in fact correct but have since been exposed as well not. 

We are seeing medical journals retract studies and admit that they were wrong as they were publishing studies without sufficient peer review and the media was already off and running sure they had the next big story be it cure or curse on the Covid virus.

If in fact anyone noticed between protests and riots we already passed a landmark of June 1 where at one point there was the belief that we would hit 200K dead which in fact is now 111K but again these numbers are well just wrong.  Anything at this point with regards to Covid is truly debatable over positive cases, those dead and in fact those possessing antibodies as they are that unreliable.

What the models are to do is three fold. This from the NEJM:

• First, we remain uncertain about the extent of protective immunity.4 If SARS-CoV-2 infection produces strong, long-lasting immunity, then the risk of recurrent, annual outbreaks is lower. If there is waning, only partially protective, or no immunity, then epidemics may recur frequently or seasonally, as the Kissler model explores. Most models (such as the Ferguson, Aleta, and Hellewell models) assume that immunity completely protects against infection for at least a year or two — often the duration of the simulation. Until we have better data on antibody kinetics and protection against reinfection, models will be useful for exploring possibilities rather than making strong predictions about longer-term disease dynamics.
• Second, the extent of transmission and immunity among people with no or minimal symptoms (including children) plays an important role in predictions: if there is very little asymptomatic infection, we are probably still far from the epidemic peak. If there is a lot of asymptomatic transmission, there are many unobserved cases, but we may be further along the epidemic curve than we thought — assuming some protective immunity. Carefully designed serologic surveys will clarify this issue, but meanwhile models vary in their assumptions, primarily affecting estimates about the peak’s timing and the epidemic’s duration.
• Third, it remains extremely challenging to measure and model contact rates between susceptible and infectious people, not only under physical distancing policies but also in various reopening scenarios. Models must make assumptions about how people interact with others, and they often do so on the basis of diary studies conducted in different countries at different times.5 Contact rates will be hard to predict during such a rapidly changing crisis and are therefore a key source of model uncertainty.
• In all mechanistic models, epidemics can die away in two ways: either the disease runs out of fuel because there are no longer enough susceptible people to infect, or something changes to slow or halt transmission — for example, the number of contacts is reduced by dramatic physical distancing interventions. Since this latter mechanism slows the spread of disease without changing the number of people at risk, Covid-19 models agree that almost all populations are at risk of disease resurgence when societies reopen. Recent serosurveys indicate that even where this pandemic has been most severe, we remain far from starving it of susceptible hosts and must continue to control spread with contact-reduction measures.
• Unlike other scientific efforts, in which researchers continuously refine methods and collectively attempt to approach a truth about the world, epidemiologic models are often designed to help us systematically examine the implications of various assumptions about a highly nonlinear process that is hard to predict using only intuition. Models are constrained by what we know and what we assume, but used appropriately and with an understanding of these limitations, they can and should help guide us through this pandemic.

In other words this is a game of guessing and using whatever data they can find and that is based on the States getting that to the CDC to process, those two factors right there can make one go: REALLY?  Certainly they are already off to a rocky start and at this point it's a little late to fix.

This from The Atlantic:

The Centers for Disease Control and Prevention is conflating the results of two different types of coronavirus tests, distorting several important metrics and providing the country with an inaccurate picture of the state of the pandemic. We’ve learned that the CDC is making, at best, a debilitating mistake: combining test results that diagnose current coronavirus infections with test results that measure whether someone has ever had the virus. The upshot is that the government’s disease-fighting agency is overstating the country’s ability to test people who are sick with COVID-19. The agency confirmed to The Atlantic on Wednesday that it is mixing the results of viral and antibody tests, even though the two tests reveal different information and are used for different reasons.
This is not merely a technical error. States have set quantitative guidelines for reopening their economies based on these flawed data points.

So again, as the Knucklehead Governor of New Jersey likes to say, "We are reopening the State based on Science, Data and Facts."  Good plan if there was one and those three variables were accurate but nope.

There is more as this from STAT explains:

A widely followed model for projecting Covid-19 deaths in the U.S. is producing results that have been bouncing up and down like an unpredictable fever, and now epidemiologists are criticizing it as flawed and misleading for both the public and policy makers. In particular, they warn against relying on it as the basis for government decision-making, including on “re-opening America.” 

“It’s not a model that most of us in the infectious disease epidemiology field think is well suited” to projecting Covid-19 deaths, epidemiologist Marc Lipsitch of the Harvard T.H. Chan School of Public Health told reporters this week, referring to projections by the Institute for Health Metrics and Evaluation at the University of Washington. 

Others experts, including some colleagues of the model-makers, are even harsher. “That the IHME model keeps changing is evidence of its lack of reliability as a predictive tool,” said epidemiologist Ruth Etzioni of the Fred Hutchinson Cancer Center, who has served on a search committee for IHME. “That it is being used for policy decisions and its results interpreted wrongly is a travesty unfolding before our eyes.” 

While other epidemiologists disagree on whether IHME’s deaths projections are too high or too low, there is consensus that their volatility has confused policy makers and the public.

They explain in detail the flaws with this and for the record the Covid task force has not met now for over two weeks I guess they re-branded and are the Floyd Force now dealing with Police Brutality.


In addition this also may have contributed to the misdiagnosis and ultimate mistreatment medically of many patients, including the histrionic overuse of intubation as a form of treatment, which ultimately became the death panel we once feared.  This from Medical Life Sciences. 

The researchers, whose paper has been published in The British Medical Journal (BMJ), found that the data and methods used in these studies were potentially at high risk of bias, while some of the studies included recommendations that were questionable if put into practice. 

The researchers warn that the potentially flawed models may result in doctors making inappropriate decisions about whether patients have the virus, need a ventilator or should remain in hospital. 

Since the outbreak in December, health care systems across the world have been under severe strain. 

More than one million people have been diagnosed with the virus worldwide and the death toll has surpassed 51,000. 

Doctors face significant pressure to detect and diagnose patients who are infected with the virus and to give a prognosis for each confirmed case. 

The researchers reviewed 27 studies - 25 used data from China, one used data from Italy, while another used international data. The data was collected between 8 December 2019 and 15 March 2020. 

They found that all the studies had a high risk of bias. Some of the studies had a non-representative selection of patients, while others excluded patients who were still ill at the end of the studies. Others had poor statistical analysis. 

The researchers acknowledge that clinical data from Covid-19 patients is scarce and that the studies were done under severe time constraints so that they could help medical decision-making as quickly as possible. 

However, given the identified flaws, the researchers said it was a concern that some of the proposed models were already being used to support medical decisions.

But let's keep the fear going and right now we have civil unrest happening.  In a way Covid helped bring attention to an issue that has been going on for over a century, not decades, not years.. A CENTURY.  Good luck with that shit doesn't change in America unless you are afraid and well we are it just went in different direction, shame the models couldn't predict that.


Science
Why this Nobel laureate predicts a quicker coronavirus recovery: ‘We’re going to be fine’
Los Angeles Times
By Joe Mozingo Staff Writer
March 23, 2020


Michael Levitt, a Nobel laureate and Stanford biophysicist, began analyzing the number of COVID-19 cases worldwide in January and correctly calculated that China would get through the worst of its coronavirus outbreak long before many health experts had predicted.

Now he foresees a similar outcome in the United States and the rest of the world.

While many epidemiologists are warning of months, or even years, of massive social disruption and millions of deaths, Levitt says the data simply don’t support such a dire scenario — especially in areas where reasonable social distancing measures are in place.

“What we need is to control the panic,” he said. In the grand scheme, “we’re going to be fine.”

Here’s what Levitt noticed in China: On Jan. 31, the country had 46 new deaths due to the novel coronavirus, compared with 42 new deaths the day before.

Although the number of daily deaths had increased, the rate of that increase had begun to ease off. In his view, the fact that new cases were being identified at a slower rate was more telling than the number of new cases itself. It was an early sign that the trajectory of the outbreak had shifted.

Think of the outbreak as a car racing down an open highway, he said. Although the car is still gaining speed, it’s not accelerating as rapidly as before.

“This suggests that the rate of increase in the number of deaths will slow down even more over the next week,” Levitt wrote in a report he sent to friends Feb. 1 that was widely shared on Chinese social media. And soon, he predicted, the number of deaths would be decreasing every day.

Three weeks later, Levitt told the China Daily News that the virus’ rate of growth had peaked. He predicted that the total number of confirmed COVID-19 cases in China would end up around 80,000, with about 3,250 deaths.

This forecast turned out to be remarkably accurate: As of March 16, China had counted a total of 80,298 cases and 3,245 deaths — in a nation of nearly 1.4 billion people where roughly 10 million die every year. The number of newly diagnosed patients has dropped to around 25 a day, with no cases of community spread reported since Wednesday.

Now Levitt, who received the 2013 Nobel Prize in chemistry for developing complex models of chemical systems, is seeing similar turning points in other nations, even those that did not instill the draconian isolation measures that China did.

He analyzed data from 78 countries that reported more than 50 newcases of COVID-19 every day and sees “signs of recovery” in many of them. He’s not focusing on the total number ofcases in a country, but on the number of new cases identified every day — and, especially, on the change in that number from one day to the next.

“Numbers are still noisy, but there are clear signs of slowed growth.”

In South Korea, for example, newly confirmed cases are being added to the country’s total each day, but the daily tally has dropped in recent weeks, remaining below 200. That suggests the outbreak there may be winding down.

In Iran, the number of newly confirmed COVID-19 cases per day remained relatively flat last week, going from 1,053 last Monday to 1,028 on Sunday. Although that’s still a lot of new cases, Levitt said, the pattern suggests the outbreak there “is past the halfway mark.”

Italy, on the other hand, looks like it’s still on the upswing. In that country, the number of newly confirmed cases increased on most days this past week.

In places that have managed to recover from an initial outbreak, officials must still contend with the fact that the coronavirus may return. China is now fighting to stop new waves of infection coming in from places where the virus is spreading out of control. Other countries are bound to face the same problem.

Levitt acknowledges that his figures are messy and that the official case counts in many areas are too low because testing is spotty. But even with incomplete data, “a consistent decline means there’s some factor at work that is not just noise in the numbers,” he said.

In other words, as long as the reasons for the inaccurate case counts remain the same, it’s still useful to compare them from one day to the next.

The trajectory of deaths backs up his findings, he said, since it follows the same basic trends as the new confirmed cases. So do data from outbreaks in confined environments, such as the one on the Diamond Princess cruise ship. Out of 3,711 people on board, 712 were infected, and eight died.

This unintended experiment in coronavirus spread will help researchers estimate the number of fatalities that would occur in a fully infected population, Levitt said. For instance, the Diamond Princess data allowed him to estimate that being exposed to the new coronavirus doubles a person’s risk of dying in the next two months. Most people have an extremely low risk of death in a two-month period, so that risk remains extremely low even when doubled.

Nicholas Reich, a biostatistician at the University of Massachusetts Amherst, said the analysis was thought-provoking, if nothing else.

“Time will tell if Levitt’s predictions are correct,” Reich said. “I do think that having a wide diversity of experts bringing their perspectives to the table will help decision-makers navigate the very tricky decisions they will be facing in the upcoming weeks and months.”

Levitt said he’s in sync with those calling for strong measures to fight the outbreak. The social-distancing mandates are critical — particularly the ban on large gatherings — because the virus is so new that the population has no immunity to it, and a vaccine is still many months away. “This is not the time to go out drinking with your buddies,” he said.

Getting vaccinated against the flu is important, too, because a coronavirus outbreak that strikes in the middle of a flu epidemic is much more likely to overwhelm hospitals and increases the odds that the coronavirus goes undetected. This was probably a factor in Italy, a country with a strong anti-vaccine movement, he said.

But he also blames the media for causing unnecessary panic by focusing on the relentless increase in the cumulative number of cases and spotlighting celebrities who contract the virus. By contrast, the flu has sickened 36 million Americans since September and killed an estimated 22,000, according to the CDC, but those deaths are largely unreported.

Levitt fears the public health measures that have shut down large swaths of the economy could cause their own health catastrophe, as lost jobs lead to poverty and hopelessness. Time and again, researchers have seen that suicide rates go up when the economy spirals down.

The virus can grow exponentially only when it is undetected and no one is acting to control it, Levitt said. That’s what happened in South Korea last month, when it ripped through a closed-off cult that refused to report the illness.

“People need to be considered heroes for announcing they have this virus,” he said.

The goal needs to be better early detection — not just through testing but perhaps with body-temperature surveillance, which China is implementing — and immediate social isolation.

While the COVID-19 fatality rate appears to be significantly higher than that of the flu, Levitt says it is, quite simply put, “not the end of the world.”

“The real situation is not as nearly as terrible as they make it out to be,” he said.

Dr. Loren Miller, a physician and infectious diseases researcher at the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, said it’s premature to draw any conclusions — either rosy or bleak — about the course the pandemic will take.

“There’s a lot of uncertainty right now,” he said. “In China they nipped it in the bud in the nick of time. In the U.S. we might have, or we might not have. We just don’t know.”