[ucodegen]

I still think a pump shotgun is a far superior close in weapon, especially in terms of how much damage it does. Put a 3″ magnum buck load into one of their grapes and its Jackson Pollock time.

How about an ‘semi-automatic’ shotgun (no pumping needed between shots). Some of the ones made are ‘inertial’ based. This means that they use the ‘kickback’ of the shell to load the next round as opposed to using gas bled from around the discharging round.

http://hunting.about.com/od/toppicks/tp/tp_autoshotguns.htm

Then there is this one for the really sick.

AA-12 Shotgun – Anti-Zombie Weapon of Choice

Even states that it is designed for zombie attacks!!

[ucodegen]

I still think a pump shotgun is a far superior close in weapon, especially in terms of how much damage it does. Put a 3″ magnum buck load into one of their grapes and its Jackson Pollock time.

How about an ‘semi-automatic’ shotgun (no pumping needed between shots). Some of the ones made are ‘inertial’ based. This means that they use the ‘kickback’ of the shell to load the next round as opposed to using gas bled from around the discharging round.

http://hunting.about.com/od/toppicks/tp/tp_autoshotguns.htm

Then there is this one for the really sick.

AA-12 Shotgun – Anti-Zombie Weapon of Choice

Even states that it is designed for zombie attacks!!

[ucodegen]

I still think a pump shotgun is a far superior close in weapon, especially in terms of how much damage it does. Put a 3″ magnum buck load into one of their grapes and its Jackson Pollock time.

How about an ‘semi-automatic’ shotgun (no pumping needed between shots). Some of the ones made are ‘inertial’ based. This means that they use the ‘kickback’ of the shell to load the next round as opposed to using gas bled from around the discharging round.

http://hunting.about.com/od/toppicks/tp/tp_autoshotguns.htm

Then there is this one for the really sick.

AA-12 Shotgun – Anti-Zombie Weapon of Choice

Even states that it is designed for zombie attacks!!

[ucodegen]

One of the main reasons for this is that they just didn’t test very many people.

True, but they are testing more people now, as a result of H1N1 and trying to track the disease, than they usually do. In the process, the ‘tracked’ count is not significantly greater than with other flu(s).

What is significant on this flu is that the characteristics of those succumbing are different than traditionally. Usually, the old and very young are the types of people that succumb. In this case, it was the category of people that usually handle the flu well.

So the extrapolation of the positive tests to the untested deaths from flu like sympoms is reasonable.

Not necessarily. More people admitted themselves to hospitals with symptoms than would have during a normal flu season because they were wary of what those symptoms might mean. This would change the % of reporting to non-reporting and therefore change the extrapolation. I would prefer that they present actuals before they show extrapolation.

[ucodegen]

One of the main reasons for this is that they just didn’t test very many people.

True, but they are testing more people now, as a result of H1N1 and trying to track the disease, than they usually do. In the process, the ‘tracked’ count is not significantly greater than with other flu(s).

What is significant on this flu is that the characteristics of those succumbing are different than traditionally. Usually, the old and very young are the types of people that succumb. In this case, it was the category of people that usually handle the flu well.

So the extrapolation of the positive tests to the untested deaths from flu like sympoms is reasonable.

Not necessarily. More people admitted themselves to hospitals with symptoms than would have during a normal flu season because they were wary of what those symptoms might mean. This would change the % of reporting to non-reporting and therefore change the extrapolation. I would prefer that they present actuals before they show extrapolation.

[ucodegen]

One of the main reasons for this is that they just didn’t test very many people.

True, but they are testing more people now, as a result of H1N1 and trying to track the disease, than they usually do. In the process, the ‘tracked’ count is not significantly greater than with other flu(s).

What is significant on this flu is that the characteristics of those succumbing are different than traditionally. Usually, the old and very young are the types of people that succumb. In this case, it was the category of people that usually handle the flu well.

So the extrapolation of the positive tests to the untested deaths from flu like sympoms is reasonable.

Not necessarily. More people admitted themselves to hospitals with symptoms than would have during a normal flu season because they were wary of what those symptoms might mean. This would change the % of reporting to non-reporting and therefore change the extrapolation. I would prefer that they present actuals before they show extrapolation.

[ucodegen]

One of the main reasons for this is that they just didn’t test very many people.

True, but they are testing more people now, as a result of H1N1 and trying to track the disease, than they usually do. In the process, the ‘tracked’ count is not significantly greater than with other flu(s).

What is significant on this flu is that the characteristics of those succumbing are different than traditionally. Usually, the old and very young are the types of people that succumb. In this case, it was the category of people that usually handle the flu well.

So the extrapolation of the positive tests to the untested deaths from flu like sympoms is reasonable.

Not necessarily. More people admitted themselves to hospitals with symptoms than would have during a normal flu season because they were wary of what those symptoms might mean. This would change the % of reporting to non-reporting and therefore change the extrapolation. I would prefer that they present actuals before they show extrapolation.

[ucodegen]

One of the main reasons for this is that they just didn’t test very many people.

True, but they are testing more people now, as a result of H1N1 and trying to track the disease, than they usually do. In the process, the ‘tracked’ count is not significantly greater than with other flu(s).

What is significant on this flu is that the characteristics of those succumbing are different than traditionally. Usually, the old and very young are the types of people that succumb. In this case, it was the category of people that usually handle the flu well.

So the extrapolation of the positive tests to the untested deaths from flu like sympoms is reasonable.

Not necessarily. More people admitted themselves to hospitals with symptoms than would have during a normal flu season because they were wary of what those symptoms might mean. This would change the % of reporting to non-reporting and therefore change the extrapolation. I would prefer that they present actuals before they show extrapolation.

[ucodegen]

A few more than 8. According to the CDC, more than 11,000 have died in the US, with as many as 55 million contracting the virus.

The 11,000 (11,160) is actually an estimate of the number (see heading; reports and extrapolations).
http://www.cdc.gov/h1n1flu/estimates/results_2009_h1n1.htm
I don’t know if the extrapolation on number of cases based upon previous cases seen at hospitals is a good methodology (ratio based on proportion of people with influenza that seek medical care). This ‘pandemic’ causes more people to visit the hospital for symptoms than would normally visit. Using previous ‘factors’ for percentage visiting hospital could cause the extrapolation to be invalid.

Using San Diego county counted numbers; 55 deaths attributed to H1N1, 829 hospitalizations, total population 3,001,072.
(link1, link2)
Taking these numbers and the ‘estimated’ 11,157 deaths, and fact-checking by using them to estimate total population (assumes similar infection/death rates in the rest of the population)
(11,157)/(55) * 3,001,072 = 608,781,096 which is about 2x the population of the United States.
http://www.census.gov/population/www/popclockus.html
I would have expected a ‘slight’ variance, not 2x.

Using population of California as a whole gives;
318 deaths, 6000+ hospitalized. Total population of California is 36,756,666.
(11,157)/(318) * 36,756,666 = 1,289,604,159 or 4x the population of the United States. Why is the difference between just San Diego and all of California important? The California sample includes San Diego and as you increase a statistical sample size, the result should close on the real number (reduction in variance with increased sample size). The increased sample size is actually moving away from the CDC numbers, not towards as would be expected.

This does give me pause to wonder.

Additional ref
http://ajph.aphapublications.org/cgi/content/full/98/5/939

Interesting notes:
Between 1999 and 2001, there was positive confirmation of influenza virus for fewer than 10% of deaths recorded as caused by influenza.
Official annual respiratory viral surveillance data for the seasons 1976–1977 through 1998–1999 have shown that a mean of only 12% of “influenza specimens” actually tested positive for influenza virus.

Buried deeper in the links:
http://www.cdc.gov/H1N1FLU/updates/us/index.htm

Looks like laboratory confirmed numbers is 1,857 for the period 8/30/2009 to 1/23/2010 (lower 2/3s of the page), and this is all influenza deaths, including H1N1 over the period.

[ucodegen]

A few more than 8. According to the CDC, more than 11,000 have died in the US, with as many as 55 million contracting the virus.

The 11,000 (11,160) is actually an estimate of the number (see heading; reports and extrapolations).
http://www.cdc.gov/h1n1flu/estimates/results_2009_h1n1.htm
I don’t know if the extrapolation on number of cases based upon previous cases seen at hospitals is a good methodology (ratio based on proportion of people with influenza that seek medical care). This ‘pandemic’ causes more people to visit the hospital for symptoms than would normally visit. Using previous ‘factors’ for percentage visiting hospital could cause the extrapolation to be invalid.

Using San Diego county counted numbers; 55 deaths attributed to H1N1, 829 hospitalizations, total population 3,001,072.
(link1, link2)
Taking these numbers and the ‘estimated’ 11,157 deaths, and fact-checking by using them to estimate total population (assumes similar infection/death rates in the rest of the population)
(11,157)/(55) * 3,001,072 = 608,781,096 which is about 2x the population of the United States.
http://www.census.gov/population/www/popclockus.html
I would have expected a ‘slight’ variance, not 2x.

Using population of California as a whole gives;
318 deaths, 6000+ hospitalized. Total population of California is 36,756,666.
(11,157)/(318) * 36,756,666 = 1,289,604,159 or 4x the population of the United States. Why is the difference between just San Diego and all of California important? The California sample includes San Diego and as you increase a statistical sample size, the result should close on the real number (reduction in variance with increased sample size). The increased sample size is actually moving away from the CDC numbers, not towards as would be expected.

This does give me pause to wonder.

Additional ref
http://ajph.aphapublications.org/cgi/content/full/98/5/939

Interesting notes:
Between 1999 and 2001, there was positive confirmation of influenza virus for fewer than 10% of deaths recorded as caused by influenza.
Official annual respiratory viral surveillance data for the seasons 1976–1977 through 1998–1999 have shown that a mean of only 12% of “influenza specimens” actually tested positive for influenza virus.

Buried deeper in the links:
http://www.cdc.gov/H1N1FLU/updates/us/index.htm

Looks like laboratory confirmed numbers is 1,857 for the period 8/30/2009 to 1/23/2010 (lower 2/3s of the page), and this is all influenza deaths, including H1N1 over the period.

[ucodegen]

A few more than 8. According to the CDC, more than 11,000 have died in the US, with as many as 55 million contracting the virus.

The 11,000 (11,160) is actually an estimate of the number (see heading; reports and extrapolations).
http://www.cdc.gov/h1n1flu/estimates/results_2009_h1n1.htm
I don’t know if the extrapolation on number of cases based upon previous cases seen at hospitals is a good methodology (ratio based on proportion of people with influenza that seek medical care). This ‘pandemic’ causes more people to visit the hospital for symptoms than would normally visit. Using previous ‘factors’ for percentage visiting hospital could cause the extrapolation to be invalid.

Using San Diego county counted numbers; 55 deaths attributed to H1N1, 829 hospitalizations, total population 3,001,072.
(link1, link2)
Taking these numbers and the ‘estimated’ 11,157 deaths, and fact-checking by using them to estimate total population (assumes similar infection/death rates in the rest of the population)
(11,157)/(55) * 3,001,072 = 608,781,096 which is about 2x the population of the United States.
http://www.census.gov/population/www/popclockus.html
I would have expected a ‘slight’ variance, not 2x.

Using population of California as a whole gives;
318 deaths, 6000+ hospitalized. Total population of California is 36,756,666.
(11,157)/(318) * 36,756,666 = 1,289,604,159 or 4x the population of the United States. Why is the difference between just San Diego and all of California important? The California sample includes San Diego and as you increase a statistical sample size, the result should close on the real number (reduction in variance with increased sample size). The increased sample size is actually moving away from the CDC numbers, not towards as would be expected.

This does give me pause to wonder.

Additional ref
http://ajph.aphapublications.org/cgi/content/full/98/5/939

Interesting notes:
Between 1999 and 2001, there was positive confirmation of influenza virus for fewer than 10% of deaths recorded as caused by influenza.
Official annual respiratory viral surveillance data for the seasons 1976–1977 through 1998–1999 have shown that a mean of only 12% of “influenza specimens” actually tested positive for influenza virus.

Buried deeper in the links:
http://www.cdc.gov/H1N1FLU/updates/us/index.htm

Looks like laboratory confirmed numbers is 1,857 for the period 8/30/2009 to 1/23/2010 (lower 2/3s of the page), and this is all influenza deaths, including H1N1 over the period.

[ucodegen]

A few more than 8. According to the CDC, more than 11,000 have died in the US, with as many as 55 million contracting the virus.

The 11,000 (11,160) is actually an estimate of the number (see heading; reports and extrapolations).
http://www.cdc.gov/h1n1flu/estimates/results_2009_h1n1.htm
I don’t know if the extrapolation on number of cases based upon previous cases seen at hospitals is a good methodology (ratio based on proportion of people with influenza that seek medical care). This ‘pandemic’ causes more people to visit the hospital for symptoms than would normally visit. Using previous ‘factors’ for percentage visiting hospital could cause the extrapolation to be invalid.

Using San Diego county counted numbers; 55 deaths attributed to H1N1, 829 hospitalizations, total population 3,001,072.
(link1, link2)
Taking these numbers and the ‘estimated’ 11,157 deaths, and fact-checking by using them to estimate total population (assumes similar infection/death rates in the rest of the population)
(11,157)/(55) * 3,001,072 = 608,781,096 which is about 2x the population of the United States.
http://www.census.gov/population/www/popclockus.html
I would have expected a ‘slight’ variance, not 2x.

Using population of California as a whole gives;
318 deaths, 6000+ hospitalized. Total population of California is 36,756,666.
(11,157)/(318) * 36,756,666 = 1,289,604,159 or 4x the population of the United States. Why is the difference between just San Diego and all of California important? The California sample includes San Diego and as you increase a statistical sample size, the result should close on the real number (reduction in variance with increased sample size). The increased sample size is actually moving away from the CDC numbers, not towards as would be expected.

This does give me pause to wonder.

Additional ref
http://ajph.aphapublications.org/cgi/content/full/98/5/939

Interesting notes:
Between 1999 and 2001, there was positive confirmation of influenza virus for fewer than 10% of deaths recorded as caused by influenza.
Official annual respiratory viral surveillance data for the seasons 1976–1977 through 1998–1999 have shown that a mean of only 12% of “influenza specimens” actually tested positive for influenza virus.

Buried deeper in the links:
http://www.cdc.gov/H1N1FLU/updates/us/index.htm

Looks like laboratory confirmed numbers is 1,857 for the period 8/30/2009 to 1/23/2010 (lower 2/3s of the page), and this is all influenza deaths, including H1N1 over the period.

[ucodegen]

A few more than 8. According to the CDC, more than 11,000 have died in the US, with as many as 55 million contracting the virus.

The 11,000 (11,160) is actually an estimate of the number (see heading; reports and extrapolations).
http://www.cdc.gov/h1n1flu/estimates/results_2009_h1n1.htm
I don’t know if the extrapolation on number of cases based upon previous cases seen at hospitals is a good methodology (ratio based on proportion of people with influenza that seek medical care). This ‘pandemic’ causes more people to visit the hospital for symptoms than would normally visit. Using previous ‘factors’ for percentage visiting hospital could cause the extrapolation to be invalid.

Using San Diego county counted numbers; 55 deaths attributed to H1N1, 829 hospitalizations, total population 3,001,072.
(link1, link2)
Taking these numbers and the ‘estimated’ 11,157 deaths, and fact-checking by using them to estimate total population (assumes similar infection/death rates in the rest of the population)
(11,157)/(55) * 3,001,072 = 608,781,096 which is about 2x the population of the United States.
http://www.census.gov/population/www/popclockus.html
I would have expected a ‘slight’ variance, not 2x.

Using population of California as a whole gives;
318 deaths, 6000+ hospitalized. Total population of California is 36,756,666.
(11,157)/(318) * 36,756,666 = 1,289,604,159 or 4x the population of the United States. Why is the difference between just San Diego and all of California important? The California sample includes San Diego and as you increase a statistical sample size, the result should close on the real number (reduction in variance with increased sample size). The increased sample size is actually moving away from the CDC numbers, not towards as would be expected.

This does give me pause to wonder.

Additional ref
http://ajph.aphapublications.org/cgi/content/full/98/5/939

Interesting notes:
Between 1999 and 2001, there was positive confirmation of influenza virus for fewer than 10% of deaths recorded as caused by influenza.
Official annual respiratory viral surveillance data for the seasons 1976–1977 through 1998–1999 have shown that a mean of only 12% of “influenza specimens” actually tested positive for influenza virus.

Buried deeper in the links:
http://www.cdc.gov/H1N1FLU/updates/us/index.htm

Looks like laboratory confirmed numbers is 1,857 for the period 8/30/2009 to 1/23/2010 (lower 2/3s of the page), and this is all influenza deaths, including H1N1 over the period.

[ucodegen]

3) I think the 2nd gets wiped out at trustee sale. Fed tax liens must be settled, I’m pretty sure. So you should avoid properties that have these kind of red flags.

It is correct that the second gets wiped out at trustee sale. It is a subordinate loan to the first. Tax liens do have to be settled unless the property has been taken for ‘taxes’ as opposed to foreclosed upon. Also watch out for other liens as well (HOA, mechanics). I wouldn’t stay away from this type, but it is important to find all liens/mortgages associated with a property and determine what their disposition will be at sale, whether tax sale or foreclosure.

[ucodegen]

3) I think the 2nd gets wiped out at trustee sale. Fed tax liens must be settled, I’m pretty sure. So you should avoid properties that have these kind of red flags.

It is correct that the second gets wiped out at trustee sale. It is a subordinate loan to the first. Tax liens do have to be settled unless the property has been taken for ‘taxes’ as opposed to foreclosed upon. Also watch out for other liens as well (HOA, mechanics). I wouldn’t stay away from this type, but it is important to find all liens/mortgages associated with a property and determine what their disposition will be at sale, whether tax sale or foreclosure.

[Author]

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