***NON-POLITICAL COVID-19 Discussion Thread***

[JakeF]

A couple of reminders...

Originally Posted by Bwana

Once again, don't come in this thread with some kind of political agenda, or you will be shown the door. If you want to go that route, there is a thread about this in DC.

Originally Posted by Dartgod

People, there is a lot of good information in this thread, let's try to keep the petty bickering to a minimum.

We all have varying opinions about the impact of this, the numbers, etc. We will all never agree with each other. But we can all keep it civil.

Thanks!

Click here for the original OP:

Spoiler!

Apparently the CoronaVirus can survive on a inanimate objects, such as door knobs, for 9 days.

California coronavirus case could be first spread within U.S. community, CDC says
By SOUMYA KARLAMANGLA, JACLYN COSGROVE
FEB. 26, 2020 8:04 PM

The Centers for Disease Control and Prevention is investigating what could be the first case of novel coronavirus in the United States involving a patient in California who neither recently traveled out of the country nor was in contact with someone who did.

“At this time, the patient’s exposure is unknown. It’s possible this could be an instance of community spread of COVID-19, which would be the first time this has happened in the United States,” the CDC said in a statement. “Community spread means spread of an illness for which the source of infection is unknown. It’s also possible, however, that the patient may have been exposed to a returned traveler who was infected.”

The individual is a resident of Solano County and is receiving medical care in Sacramento County, according to the state Department of Public Health.

The CDC said the “case was detected through the U.S. public health system — picked up by astute clinicians.”

Officials at UC Davis Medical Center expanded on what the federal agency might have meant by that in an email sent Wednesday, as reported by the Davis Enterprise newspaper.

The patient arrived at UC Davis Medical Center from another hospital Feb. 19 and “had already been intubated, was on a ventilator, and given droplet protection orders because of an undiagnosed and suspected viral condition,” according to an email sent by UC Davis officials that was obtained by the Davis Enterprise.

The staff at UC Davis requested COVID-19 testing by the CDC, but because the patient didn’t fit the CDC’s existing criteria for the virus, a test wasn’t immediately administered, according to the email. The CDC then ordered the test Sunday, and results were announced Wednesday. Hospital administrators reportedly said in the email that despite these issues, there has been minimal exposure at the hospital because of safety protocols they have in place.

A UC Davis Health spokesperson declined Wednesday evening to share the email with The Times.

Since Feb. 2, more than 8,400 returning travelers from China have entered California, according to the state health department. They have been advised to self-quarantine for 14 days and limit interactions with others as much as possible, officials said.

“This is a new virus, and while we are still learning about it, there is a lot we already know,” Dr. Sonia Angell, director of the California Department of Public Health, said in a statement. “We have been anticipating the potential for such a case in the U.S., and given our close familial, social and business relationships with China, it is not unexpected that the first case in the U.S. would be in California.”

It is not clear how the person became infected, but public health workers could not identify any contacts with people who had traveled to China or other areas where the virus is widespread. That raises concern that the virus is spreading in the United States, creating a challenge for public health officials, experts say.

“It’s the first signal that we could be having silent transmission in the community,” said Lawrence Gostin, director of the World Health Organization Collaborating Center on National and Global Health Law. “It probably means there are many more cases out there, and it probably means this individual has infected others, and now it’s a race to try to find out who that person has infected.”

On Tuesday, the CDC offered its most serious warning to date that the United States should expect and prepare for the coronavirus to become a more widespread health issue.

“Ultimately, we expect we will see coronavirus spread in this country,” said Nancy Messonnier, director of the CDC’s National Center for Immunization and Respiratory Diseases. “It’s not so much a question of if, but a question of when.”

According to the CDC’s latest count Wednesday morning, 59 U.S. residents have tested positive for the new strain of coronavirus — 42 of whom are repatriated citizens from a Diamond Princess cruise. That number has grown by two since Messonnier’s last count Tuesday, although the CDC was not immediately available to offer details on the additional cases.

More than 82,000 cases of coronavirus have been reported globally, and more than 2,700 people have died, with the majority in mainland China, the epicenter of the outbreak.

But public health leaders have repeatedly reminded residents that the health risk from the novel coronavirus to the general public remains low.

“While COVID-19 has a high transmission rate, it has a low mortality rate,” the state Department of Public Health said in a statement Wednesday. “From the international data we have, of those who have tested positive for COVID-19, approximately 80% do not exhibit symptoms that would require hospitalization. There have been no confirmed deaths related to COVID-19 in the United States to date.”

CDC officials have also warned that although the virus is likely to spread in U.S. communities, the flu still poses a greater risk.

Gostin said the news of potential silent transmission does not eliminate the possibility of containing the virus in the U.S. and preventing an outbreak.

“There are few enough cases that we should at least try,” he said. “Most of us are not optimistic that that will be successful, but we’re still in the position to try.”

[BigCatDaddy]

Originally Posted by 'Hamas' Jenkins

Had a lockdown been instituted in New York two weeks earlier the death rate would have been 80-90% lower, yet people are bitching about there not being enough hospitalizations and deaths in their locales.

I don't even know how this is an argument.

It was the basis for the entire shutdown. We have had about 2 hospitalizations in over month. We aren't over running shit yet if a few cases pop up, people panic and scream at the sky to "SHUT IT DOWN".

Now given its mostly idiots posting on our local news FB pages, but still.

[DaFace]

Originally Posted by KCChiefsFan88

"Hope" is not a realistic strategy for large scale events, restaurants, gyms, haircut establishments and other businesses that some want to remain closed or at reduced capacity until there is a vaccine.

There may not be a scalable/effective vaccine for several years or ever.

Herd immunity is a more realistic strategy.

It's not that you're wrong, it's just that the implications of that are dire. If that's our only hope, we're looking at 250k deaths at an absolute minimum, and likely far higher than that.

[dirk digler]

Thanks to that CDC site you can look at the different models besides the IHME one.

This one you can look at the R0 estimate and actual for every state.

https://covid19-projections.com/infections-tracker/

[Bugeater]

Originally Posted by notorious

We all know this is going to pass through just about everyone, eventually. People will die, and it sucks, but we can't hide from this forever.

By isolating ourselves we've given the hospitals a chance to take in the people that need care at a somewhat controlled rate. By some miracle a vaccine will be developed (not holding my breath) that will protect everyone.

It is possible.......sit down for this........that both sides of the argument are correct. Distancing and herd mentality work.

Yes sir. I've said over and over that there's a balance in there somewhere and this post outlines so simply and perfectly it should be placed in the OP.

Originally Posted by 'Hamas' Jenkins

You'll only get a huge spike if you completely open things up and disregard distancing and hygiene recommendations.

You don't need enough people to get it to develop herd immunity (which lasts 1-2 years with every other known coronavirus in humans), you need to establish practices that reduce the effective R to the point where the virus doesn't spread easily.

If the R0 is 3 without any measures in place, you'd need 2/3 of the population to get the virus. If you have a system in place of mask wearing and hand washing and you can reduce the R to less than one, the virus dies out. If you can only reduce it to 1.5, then you only need 1/3 of the population to get the virus. That's a difference of 110 million infections even with an Re of 1.5. With an IFR of only 0.2 (and it's likely higher than that by a fair number), that's 220,000 fewer deaths.

But once again...everyone needs to play along and the key will be how to deal with those who don't.

[limested]

Originally Posted by KCChiefsFan88

"Hope" is not a realistic strategy for large scale events, restaurants, gyms, haircut establishments and other businesses that some want to remain closed or at reduced capacity until there is a vaccine.

There may not be a scalable/effective vaccine for several years or ever.

Herd immunity is a more realistic strategy.

With a needed rate of 70% immunity to achieve herd immunity a working vaccine is basically a necessity to achieve it.

[Discuss Thrower]

Originally Posted by dirk digler

Thanks to that CDC site you can look at the different models besides the IHME one.

This one you can look at the R0 estimate and actual for every state.

https://covid19-projections.com/infections-tracker/

"One interesting thing to note is that the 5 states with the lowest initial R0 estimates also happen to be the 5 least densely-populated states in the US: Montana, North Dakota, South Dakota, Alaska, and Wyoming. Our model has zero knowledge of population density, yet it was able to indirectly deduce that from the R0."

[wazu]

Originally Posted by Monticore

Inflammatory damage to the body is not always seen instantly. Who knows if covid damage done in a young person today doesn’t come back to haunt then down the road .

Sorry, but "who knows?" isn't enough cause for isolating people, especially young people that from all evidence are at very, very low risk.

[Discuss Thrower]

Originally Posted by wazu

Sorry, but "who knows?" isn't enough cause for isolating people, especially young people that from all evidence are at very, very low risk.

Implication being the risk of the unforeseen is so great we must shelter in place indefinitely.

[Bugeater]

Originally Posted by Discuss Thrower

Implication being the risk of the unforeseen is so great we must shelter in place indefinitely.

Or move to Montana, North Dakota, South Dakota, Alaska, or Wyoming.

[Discuss Thrower]

Originally Posted by Bugeater

Or move to Montana, North Dakota, South Dakota, Alaska, or Wyoming.

Hell, if we're all going to be out of jobs and shit to do then might as well live in a pretty part of the country.

[Donger]

Originally Posted by dirk digler

Thanks to that CDC site you can look at the different models besides the IHME one.

This one you can look at the R0 estimate and actual for every state.

https://covid19-projections.com/infections-tracker/

That's claiming that the present R0 in every state is sub-1?

[Discuss Thrower]

Originally Posted by Donger

That's claiming that the present R0 in every state is sub-1?

Spoiler!

Model details
SEIS Model
Our COVID-19 prediction model has an underlying simulator based on an elaboration of the classic SIR model used in epidemiology: the SEIS (susceptible-exposed-infectious-susceptible) model. We added an exposed (E) period due to the reported incubation period of COVID-19 during which individuals are not yet infectious. We also modified the last step from recovered (R) to susceptible (S) to account for the possibility of re-infection, a phenomnenon that has already been reported in several regions and by WHO. With that said, we assume that a recovered individual is less likely to be infected again.

To quickly summarize how an SEIS model works, at each time period, an individual in a population is in one of three states: susceptible (S), exposed (E), and infectious (I). If an individual is in the susceptible state, we can assume they are healthy. If they are in the exposed state, they have been infected with the virus but are not infectious. If they are infectious, they can actively transmit the disease. An individual who is infected ultimately either recovers or dies. We assume that a recovered individual’s chances of re-infection is lower, but not zero. We can model the movement of individuals through these various states at each time period. The model’s exact specifications depend on its parameters, which we describe in the next section.

For our implementation, we use a discrete time series where each data point is a day in the simulation. For each day, we have a probability distribution for which an infected individual will transmit the virus, and another probability distribution for which an infected individual will succumb to the disease. These distributions are then convolved with the total existing cases to determine the number of new infections and new deaths per day. For new infections, we multiple the convolution by R0, while for deaths, we multiple the convolution by the mortality rate.

Assumptions
Please see the About page to read about the assumptions in our model.

Data
The sole data source we use is the daily reported deaths from Johns Hopkins CSSE. In addition, we use population data for each state/country to calculate the total susceptible population.

Because the raw data may be noisy, we must first run a smoothing algorithm to smooth the data. For example, if a state reports 0 death on one day and 300 deaths the next day, we smooth the data to show 150 deaths on each day.

Parameters
For our SEIS model, there are basic inputs/parameters that must be set to begin simulation. covid19-scenarios.org developed by the University of Basel provides a good visualization of sample inputs/parameters into a simulation. We chose a set of parameters that we think are important for the accuracy of the simulation. We divide the parameters into two categories:

Category 1: Fixed parameters
Fixed parameters are those that are fixed for this particular COVID-19 epidemic and likely do not fluctuate significantly across countries/states/regions. These include the following:

Latency period
Infectious period
Time between illness onset to hospitalization
Time between illness onset to death
Hospital stay time
Time to recovery
We use various reports and publications to determine the best values for these fixed parameters. Most of these sources reference studies from Wuhan, China, where the epidemic first began in December 2019. While it is possible that the exact values may fluctuate slightly from region to region, we believe that the impacts these fluctuations have are minimal compared to the variable parameters.

Category 2: Variable parameters
Variable parameters are those that may change depending on the country/state/region. Some examples are the following:

R0 - the basic reproduction number
Mortality rate
Imports of positive cases per day
Mitigation effects (i.e. post-mitigation R)
Lifting of shelter-at-home orders (i.e. post-reopening R)
Population
Hospital beds per 1000
Note that variable parameters such as population and hospital beds per 1000 are easily computable from a simple lookup. However, the other parameters are not easily retrievable. We will allocate the majority of our resources towards estimating the most sensible values for these parameters for each region.

To model the effects of shelter-at-home/lockdown orders, we assign an R value for post-lockdown and a separate R value for post-reopening. These R values are unknown ahead of time, and will be learned by the model. We assume that post-mitigation R is less than 1 to account for the decrease in cases, and that the post-reopening R is on average less than the initial R0.

Parameter Search using Machine Learning
As described in the previous section, determining the best values for variable parameters such as R0, the mortality rate, post-mitigation R, and post-reopening R will help us determine how COVID-19 will progress in a region. If we know what the “true” values of those parameters are, we can accurately simulate what is happening in the real world using our SEIS model. To determine these values, we use hyperparameter optimization.

Hyperparameter Optimization
We found that a brute-force search method that iterates through the entire parameter space is the most effective in finding an optimal set of parameters. We use grid search to iterate through the parameter space. So if we have 10 values for R0 and 10 values for the mortality rate, then there are 100 different parameter combinations for those two parameters. To optimize computation time, we prune unrealistic parameters (e.g. R0 > 20).

To measure the error of a parameter set, we use a loss function that minimizes the error between our projected daily deaths and the actual daily deaths. We find that an ensemble loss function that minimizes both absolute daily deaths and total daily deaths works well. For parameters where we do not have the data to estimate (e.g. we do not know the post-reopening R for regions that have not reopened), we consider all values equally, resulting in a wider confidence interval.

While we do not have much out-of-sample data to work with, we try our best to take advantage of the data from countries such as China, Italy, and Iran, whose progression is much further along than regions such as the US.

It is also important to limit the number of free variables. For example, it would be very difficult to try to determine 5 free variables when you only have 20 days of data. Therefore, for some of the variable parameters, we try varying one parameter at a time while holding all other parameters constant. This allows us to improve the signal-to-noise ratio when doing parameter search.

Validation Techniques
For any machine learning model, it is important to minimize overfitting. This model is no exception. In fact, due to the limited amount of data (e.g. if a region reported its first death 10 days ago, we only have 10 data points), it is very easy to overfit if we are not careful. That’s why we have developed a robust validation system that allows us to test various changes in a controlled environment such that overfitting is minimized. We can set our model to run on the first 20 days of data and compare the performance on the next 10 days. We can then repeat this process by running it on the first 21 days and comparing the performance on the next 9 days, and so forth. This allows us to perform cross-validation while preserving the maximum amount of data in our training set.

We use our validation techniques to test out various model-specific parameters and functions. For example, we can try various loss functions (e.g. mean square error, mean absolute error, ratio error) and pick the one with the lowest validation error. We also use validation to determine parameters such as the alpha for exponential decay of data points. We continuously perform validation to ensure that the parameters we select are truly meaningful and predictive, rather than simply being a product of overfitting.

Putting it All Together
For each region, we run our optimizer on the data to score each set of parameters. After some additional validation techniques described above, we aggregate the forecasts generated by each set of parameter, giving higher weights to parameters that have a lower error. This approach was inspired by the Monte Carlo method, allowing us to simulate the future while incorporating the inherent uncertainty. We now have our projections.

[TLO]

NOW JOHN HOPKINS MOVED IT BACK TO 343. WHAT ARE THEY TRYING TO DO TO ME

[Monticore]

Originally Posted by wazu

Sorry, but "who knows?" isn't enough cause for isolating people, especially young people that from all evidence are at very, very low risk.

I am not saying that is the answer just saying because you get it and recover doesn't mean no damage was done.

[Pablo]

Originally Posted by Discuss Thrower

Kansas City's broader metro area is roughly 80% of the population of St. Louis's.

Shouldn't their case numbers be a lot closer?

Nah, everyone knows KC>>>STL in every single fashion, so I'm not surprised.