The CDC reports 12 cases this week, 1 in Hawaii, 10 in Ohio, and one in Indiana. Seventeen more cases were reported since about one year ago. Most of the cases are found in individuals who had direct contact with swine, but some cases appear to be person to person transmission.

A large number of the recent cases seem to have been in individuals who had contact with swine at a fair. This is fair season across much of the US, and apparently petting the pigs is a cool thing for people, mainly kids, to do.

No one has died of this flu, and few seem to have become gravely ill. Three people with high risk factors were hospitalized in all.

The take-home message, the message the CDC is trying to get out, is to wash hour hands very carefully after you pet the pig. But health authorities are also saying to not eat or drink in the presence of swine at these state fairs. Personally, I plan to stay away from the pigs entirely. That will be hard to do because one of our favorite things to do at the State Fair is to visit the Big Pig. They have one really big pig at the fair every year. It is the pig that won the prize for being really really big. Nobody pets it, though. But a lot of people stand around looking at it while they eat their Food on a Stick. This year, I may glance at the pig from a distance, but I will not be eating my corn dog at that particular time.

You can’t get this flu from eating a swine who is infected. So, I can eat the corn dog, just not while petting an influenza infected swine.

Normally, even though swine do get the flu pretty routinely, it does not transmit to humans. While it is possible that there is just a lot more swine flu among the swine and we are seeing unlikely events happening, it is thought that this flu is a variant that is more transmittable to humans than is usual. In prior years, an average of about one person per year in the US gets swine flu from swine. Over the last few years, this number has gone up and the present situation is seemingly unprecedented. However, there have also been significant changes in surveillance and reporting which almost certainly account for some of this apparent increase. The CDC is not sure if there is a real increase in swine flu occurrence, transmission, or mainly reporting. They say:

The increased detection and reporting of these cases could be occurring for a number of reasons, including one or more of the following factors: First, pandemic preparedness efforts have improved state level surveillance and laboratory capacity to detect novel viruses in the United States. Second, in 2007, novel influenza virus infections were made domestically and internationally reportable. And three, it’s also possible that there is a true increase in the number of these cases, possibly occurring from exposure to infected swine or through subsequent, limited human-to-human transmission.

Just don’t pet the pig.

The CDC report is here and additional information is here and in links therein.

The reason advice is needed is two-fold. First, although I’ve raised a child before, I’ve not had charge of an infant during a pandemic. Second, few other people have either, and the nature of advice, common knowledge and practice about things in general and health related issues in particular is not usually rational, in my opinion.

We’ve already decided to skip the usual Huge Thanksgiving Get Together for the simple reason that there will be thirty or forty people there, and it simply does not make sense to wander into a crowded house full of people with a new baby during a flu pandemic.

An alternative has been suggested. This is a smaller, more limited Thanksgiving dinner with just a few family members, and that’s all. But there are three problems with this emerging, at least in my mind: 1) At least two people who will be there have rather proudly declared (and I love you guys, but you are way off on this one …) that they don’t do vaccines. They are not denialists, they just think the vaccines will make them sick. And of the other people who might visit I’m not sure who or who else might have the vaccine; 2) There will be at least one, possibly two, other vulnerable individuals who will also be at the get-together, so this may not be a good idea for them either. (Though they are older than what seems to be a high-risk cutoff for Novel A/H1N1 Swine Flu, and by that time will have been vaccinated.); and 3) I’m not sure that spending five hours in a house with one person who is sending out flu infectoids is much different than spending five hours in a larger house, more densely crowded, with, maybe, a dozen people sending out the infectoids. In fact, the way social events like this work, it is quite possible that the expected number of people woud grow rather than shrink. Will it be eight people? Ten? Fourteen? All these numbers are less than 30 (the best guess for the main Thanksgiving event) but I’m not sure if I care about that difference. Yes, I understand that exposure for longer periods and to more infectious sources should be more highly correlated with actual infection across many instances, statistically, so the probabilities are different. But …

We are talking about my baby. If I had a hundred babies, I might not mind so much if two or three of them died of the flu. But with just one …. I’m not so sure that aggregate values and probabilities are of any great interest to me. My baby does not get to be several thousand dots on a graph only a few of which will get sick. He is allowed to be only one dot.

My current plan is to monitor the situation, and consider a visit that falls short of staying around for hours and having dinner, but would allow the people to see the child and visa versa. Although the unvaccinated will be wearing those scary masks and everyone will be washing their hands every few minutes.

As I suggested above, there is likely to be a certain amount of irrationality in a decision making process like this one. I’m not sure what will emerge in this particular case, but for the more general circumstance of family meeting baby vs. limiting exposure to baby, here’s a few items that come to mind:

First, if you read the “how to not screw up your baby” literature, you’ll see a common question addressed: When is it “safe” or advisable to “take your baby out” after it is born? The answer in this literature strongly implies that new parents are often too shy about going out with baby. It is perfectly OK to cart around a properly swaddled new-born. Just don’t let sick people touch it, and be smart about what you are doing. In fact, you will see comments in this expert advice literature such as “It is probably healthier to get your baby … and yourself! … out of the house early and reasonably often! Don’t be a hermit.” and so on.

And of course, that is all true. But, this advice addresses a question other than: “When is it safe to bring my baby into contact with other people who may have the Pandemic Flu?”

And, the advice does not specifically mention my baby. Here, we are talking about my baby. If I had a hundred babies, I might not mind so much if two or three of them died of the flu because I took them out because some FAQ on taking care of new borns told me to. But with just one …. I’m not so sure….

A second feature of this sort of discussion is the necessity and importance of the family visit, of the “coming out” of the new born for all to meet and stuff. Obviously, this is a very important thing to do, but it is possible that the importance of everyone getting to meet and greet the new baby is not quite as great as the baby surviving his first several weeks of life, despite one’s desire to avoid social awkwardness of any kind.

Let me be quite stark about this: Cousin Jeeter may feel great about meeting the new baby now, but how is Jeeter going to feel if the next day he suddenly comes down with the flu, and two weeks later finds out that he probably gave the flu to his infant cousin, who has died, and no, Jeeter is not invited to the funeral. I understand that dead baby comments are in bad taste and there will be people mad at me for making the stark link between this decision and that outcome.

Tough. We are talking about my baby. If I had a hundred babies, I might not mind so much if two or three of them died of the flu. But with just one …. I’m not so sure that someone’s sensitivity to facing the stark reality that this flu … this pandemic flu we are having now … appears to be potentially deadly to anyone under 18 years of age is of any great interest to me. Yes, the chances that an infant will die from the flu are low. A week or two in the NICU should take care of him, and organ damage caused by such a major infection early in life won’t matter for … decades, if at all. But we are talking about my baby, so I might be a little picky about this.

A third fallacy that is of great importance is that if everyone washes their hands, there won’t be any problem. While it is good to wash hands a lot to reduce flu transmission, this only reduces transmission to some extent. The flu is transmitted very nicely by flying through the air. Even if an infected person coughs into his or her sleeve, the air that comes out of the person’s mouth has a zillion tiny saliva spaceships each occupied by thousands of eager flu viruses, which blow around the sleeve and into the air. Those tiny, microscopic droplets float around in the air for many minutes, possibly hours. They are then breathed in by other people in the room. They can also land on surfaces such as … candy in a candy dish, the rims of drinking glasses, the nipple of a baby’s bottle, the cat, or on someone’s hair.

Yes, a really good way to get the flu is if an infected person coughs slimy stuff into his hand, and shakes your hand thus putting the slimy stuff on your palm, then you wipe your nose with the palm of your hand or maybe you lick your palm or something. But for the most part, the way flu actually gets from one person to another is when there are two people in a room, one is infected and the other not, and they both breath for a while, with the infected person coughing or sneezing now and then.

Am I exaggerating the air borne infectious nature of this flu? Maybe, maybe not. Conditions vary, the flu varies, it is all a game of complex interconnected probabilities, so there is certainly a calculable probability of infection via direct hand to hand (to mouth) contact vs. airborne only.

But we are talking about my baby. If I had a hundred babies, I might not mind so much if two or three of them died of the flu transmitted via the air. But with just one …. I’m not so sure that aggregate values and probabilities are of any great interest to me.

I feel very lucky that all the close and more distant family members in our case will be totally understanding and supportive of whatever decisions we make (though everyone really should get vaccinated). The problem is, what exactly should that decision be?

So, what are you doing for Thanksgiving? What do you think we should we do?

Amanda, who is 8.3 months pregnant, started getting symptoms of the flu two days ago. As a high school teacher in a school being affected in a state being affected (as most are) she is at high risk for this. She was one of the first people around here to get the vaccine, just a couple of days ago, but it takes about 10 days to take full effect, so it was recommended that she go on Tamiflu for a while.

Tamiflu seems to not work very well against the current (or should I say expected) seasonal flu, but it appears that the Pandemic Swine Flu has virtually no resistance to it. And it normally works fast. Within 24 hours Amanda’s symptoms disappeared. There are three possible explanations for that:

1. Utter chance;
2. Tamiflu did it’s thing; or
3. The Tamiflu pill was actually a sugar pill with an especially strong Placebo effect.

Today, Amanda and many many other teachers from across the country are meeting at the national Science Teachers Association. So any mixing up and spreading of the flu that the students have not yet accomplished will be compensated for by the teachers exchanging the virus today and over the weekend. But Amanda has her Tamiflu and the vaccine, so she should be fine. I may ask her to take some extra placebo tonight with dinner.

In the next iteration of a pandemic, we should be providing vaccine for free at conferences and conventions. (Maybe we’re doing that now…. anybody know?)

There are three things you should read on the internet this morning about the flu, vaccines, and related issues:

1) Swine flu: How bad was the first wave?

This is by Revere, and it covers a paper just published in expedited form, OpenAccess, so you can read it yourself. I’ll have a few comments to make about this paper below, but the best summary of its results is Revere’s post at Effect Measure.

Then there are these two items by Orac and and James Hrynyshyn, respectively, on related issues: 2) The anti-vaccine movement strikes back using misogyny and 3) The link between the climate denial and anti-vaccine crowds

OK, now, about this flu paper. My comments are restricted to two aspects of the method used in this paper, and all I really want to do is add a little to your comfort level in relation to these methods. These are methods commonly used in my own fields of research (including archaeology) and that I’ve thought a bit about and taught in various classes, and I’ve found that people, once they start to learn about them, get all freaked out and refuse to believe that they are of any use. The methods are, to adopt terminology for this post that may not be reflected perfectly in the paper at hand, extrapolation and resampling.

Resampling first. Bootstrapping is also known, depending on its implementation, as Monte Carlo Simulation, Resampling, or just Simulation. There are other terms as well. It is probably best to consider them all under the heading “Resampling.”

To really understand the value of resampling, it is best to start with a concept of the inadequacy of normal parametric statistics. What the heck does that mean? At the risk of oversimplifying…. Let’s say you have calculated two averages and you want to find out if it is statistically OK for you to say that they are different … that they are averages of different populations, instead of two numbers that look different but only for random reasons. So you take the averages, the difference between them, and some kind of estimate of the variation in the population(s) you think you are sampling, and the number of samples you took to get the average.

If the two numbers are farther apart, you can have more confidence that they are different. If the amount of variation in the actual populations you are sampling is low, then you can have more confidence that they are different. If the number of samples you’ve taken is greater, you can have greater confidence that they are different.

Standard statistical methods evaluate this information … difference between means, variation in the population, and size of your sample(s), to give you a couple of numbers you can use to determine if it is statistically valid to say that the numbers are different.

But, there is a problem with this. In order for out of the box statistical methods to be used to do this, there has to be a number of assumptions made about the underlying distributions of the population(s) you are looking at. For instance, it is common to assume that these populations are “normally distributed” (like a bell curve) or that they follow some other standard, well studied distribution. So, you plug the numbers you have … the means or the difference between them, the info on variance, and the sample size … and those parameters are evaluated by magic statistical formulas built into computer programs in relation to some pre-existing model using distributions and statistics derived from earlier study with those distributions.

Often that works well because the previously studied distributions, and the relationships between the numbers and the distributions and stuff tends to be the same time after time. If you are studying the behavior of a roulette wheel, the frequency over time of raindrops falling into a bucket, people getting the flu, Russian soldiers getting killed by their horses, the distribution of stars in the sky, and so on, you may be able to use research on the distributions and statistical measures (and their interactions) carefully carried out on one or two of these phenomena to develop shortcuts to apply in the other situations.

And that is the crux of what I want to say: Standard statistical tests (the z-test, the t-test, the F-test, chi-square statistics, etc. etc.) whether they be “parametric” or “non-parametric” are all shortcuts.

The reason these shortcuts exist is because it is impossible to take thousands or tens of thousands of data points, analyze them to determine the nature of the distributions they represent, then use those discovered empirically based situationally dependent distributions to calculate test statistics and confidence intervals and stuff.

Unless, of course, we had a machine to do this! If only we had a machine into which we could put all the data, and then this machine would do calculations on the data!

Yes, folks, with modern computers it is quite straight forward to replace the old fashioned shortcuts with a brute force, direct analysis of actual data which produces (using proper methods and theory) much much better statistics than before.

I want to re-explain this two more ways keeping in mind that I’m still oversimplifying.

1) Here is the actual sequence of events one would like to do in statistical analysis.

a) Formulate a hypothesis about some numbers.

b) Fully analyze the distributional context of those numbers … are the populations they come from uniformly distributed? skewed? unary (only one possible number can be obtained no matter how often you sample it)? distributed like a bell curve?

c) Calculate the parameters of the actual distribution linked to the actual numbers you are using.

d) Calculate the actual probability related to your hypothesis, such as “the probability that these two numbers I say are different are actually drawn form the same population and only look different because of the nature of the distributions I analyzed in step ‘b’ is …”

Here’s what really happens in traditional statistical analysis:

a) Some guy, like two hundred years ago, gets interested in numbers and creates idealized distributions of things and figures out that there are some interesting relationships between and among them.

b) Some other guys, over the next couple of centuries, do the same thing with a bunch of other phenomena and come up with a handful of additional relationship types. Having no computers for any of this, that was hard.

c) Meanwhile, people figure out how to take this handful of distribution sets and use then to estimate what may or may not be going on with a particular data set. But each time one must worry about the degree to which one’s own data matches the original distribution on which a certain test statistic is based. Over time, people forget what the original distributions even were, and begin to fetishize them. For instance, the degree to which one’s data behave just like Russian Army horses’ tendency to kick soldiers to death becomes a matter of great angst and consternation, especially in graduate school.

d) Individual researchers learn which other researchers to emulate, and then they just do what they do and hope nothing goes wrong. The important thing is the p-value anyway.

Here is how resampling works:

a) All of the above is compressed into a single analysis of your actual data.

The distributional behavior of your data is determined by taking repeated random samples of the data (with replacement). Perhaps you will do this at several sample sizes. The result tells you how badly wrong your hypothesis can be … and if the answer is “not to bad” then your good. (This is all done with numbers, of course.)

2) For my second parable, imagine that you are in a situation that has nothing to do with statistics but requires you to make a decision. It is complex. The situation is unique although is falls into a known category of situations. So, you go to an experienced expert in this kind of situatoi and you describe only the basic outline, leaving out all details, and ask the expert what she would normally do in this situation.

The expert replies “Well, I don’t know the details, but generally, in this situation, I’d punt (or whatever).”

Alternatively, you are facing the same situation. So you get the expert (from above) and bring them to wherever it is you are working on this. The expert gets to see the exact situation you are in, and how your situation differs from the typical situation. Based on all the information, she draws a very different conclusion than above because there are particulars that matter.

“Don’t punt (or whatever).”

Which would you prefer? The first scenario is your data in a t-test. The second scenario is your data bootstrapped.

The second analytical techniques talked about in the paper covered by Revere is extrapolation. Obviously, extrapolation is dangerous and scary. Which would you feel more comfortable with:

1) Estimate the percentage of people who are sick in the hospital with a possible flu who require IV fluids in a particular hosptical in United States. You are given given data on number of people who walk into a hospital with flu-like symptoms, and the number of these people who get IV’s, for five one week periods distributed evenly across the flu season in ten randomly chosen hospitals plus the one you are charged to calculate this number for. In other words, you are having a statistician’s wet dream.

2) Estimate the number of people who have the flu in the United states for a given flu season based on the number of IV’s doled out to patients in ten randomly chosen hospitals. You are now having a statistician’s nightmare.

Or, consider this somewhat cleaner comparison:

You must dig a hole into which will be placed the the concrete base for a gate you hope to have in a fence you are installing in your yard.

1) All of the fence posts are in place, and you are told to put the gate post half way between two of the posts.

2) None of the fence posts are in place, and you are told to measure a line that is 47.5 feet from the NW corner of your house at bearing 312 degrees. You are not quite sure what is meant by “corner” of your house because your foundation has a vertical jog in it, and the original measurement may have been from the siding and not the foundation. Your compass sucks. You are not sure if this is 312 degrees off magnetic north or true north. You don’t have a tape measure that long. And so on.

Taking numbers that are fairly good numbers and dividing them up, looking within their ranges, breaking them into bits, is interpolation, and that can be done fairly accurately. Extending numbers outward long ‘distances’ (sometimes real distances, sometimes time, sometimes frequencies, etc.) involves a lot more uncertainty. That is what you see in the flu paper. The authors use appropriate techniques, and you will see that the range of numbers they conclude in answer to the question proposed in the title of the paper is quite large … that is because it is extrapolation that they are using, but these numbers are well confirmed by a kind of resampling.

How well all this works depends, as usual, on the question you are asking. One time I needed to find out if a particular house was made of brick vs. timber. The remote farm house had been torn down and most of the debris seemed to be dumped in the cellar hole. There were a lot of bricks, but there would have been one or two chimneys in a frame house. Also, a frame house could be “nogged” which is where clunkers and seconds (low quality bricks) are used to fill in between the timbers. Or, it oculd have been a brick house.

So, I did two things. Using the foundation size and what was known for houses at the time, I estimated how many bricks would be used for the following:

1. A two story brick house
2. A one story brick house
3. A two story nogged house
4. A one story nogged house
5. A two story house with a brick chimney
6. A one story house with a brick chimney

Separately, I weighed all the bricks we dug up in several holes, and extrapolated that number to estimate how many bricks would likely be found if we dug up the whole property. I came up with a number closest to choice 5: One chimney on one story frame house.

I did not need to know the actual number of bricks. What I needed to know was which of the plausible alternatives the estimate of brick quantity matched most closely. For the flu, it may be enough at this time to know if the Swine Flu is like the seasonal flu, not nearly as bad, much worse, etc.

Confidence can be increased in extrapolation with confirming evidence. In the case of the farm house, I counted the number of brick faces that were heavily charred (from being inside the chimney) and found that this number relative to uncharred faces was a very high. This suggests a fireplace. I noted that the bricks were mostly in one area of the foundation like maybe there was a chimney there. That suggests the chimney idea is more likely than the other ideas. And, I noted that most houses built in Saugerties NY in the 1870s were one story unnogged timber with brick chimneys. Had I started with that last observation and drew conclusions I might be guilty of confirmation bias. But instead, I ended with it, and got reasonable confirmation.

The first estimate was truly unworthy …. I could have been way far off with the brick count for a lot of reasons, and I had to make a lot of assumptions (we had not dug very many holes!). But the ratio of burned surfaces was an independent confirmation, and the conclusion was not unexpected. So, I was able to argue against confirmation bias (finding what we expected) and put this house down in the data base as yet another timber framed farm house.

Extrapolation is dangerous. Ask any Marine artillery forward observer you may happen to know, because it is what they do, but they do it with bombs and a misplaced bomb may fall right on him or herself, or a nearby baby food factory, or some other thing you don’t want to drop a bomb on. But with strong empirical background, experience, good theory, and independent confirmation it works. Or at least, it is often the best we can do and our best is good enough.

Reed, C, Angulo, F., Swerdow, D, Lipsitch, M, Meltzer, M, Jeernigan, F., & Harvard School of Public Health (2009). Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April-July 2009
Emerging Infectiou Diseases, 15 (11)

Probably the latter, but health officials seem interested in the developing data.

From CTV:

… in a report released Friday, health officials detailed the cases of 10 Michigan patients who were very sick from swine flu in late May and early June and ended up at a specialized hospital in Ann Arbor. Three of them died.

Nine of the 10 were either obese or extremely obese. Only three of the 10 had other health problems. Two of the three that died had no other health conditions.

This hardly settles the question of whether obesity is its own risk factor for swine flu. It’s possible the patients had undiagnosed heart problems or other unidentified conditions.

The report is called Intensive-Care Patients With Severe Novel Influenza A (H1N1) Virus Infection — Michigan, June 2009 and is published in MMWR, the CDC’s rapid turnaround publication for disease. The report warns:

This report describes the clinical findings of a limited series of patients with novel influenza A (H1N1) virus infection and refractory ARDS …. This patient group represents the most severely ill subset of persons with novel influenza A (H1N1) virus infection and is notable for the predominance of males, the high prevalence of obesity (especially extreme obesity), and the frequency of clinically significant pulmonary emboli and MODS. All required advanced mechanical ventilator support, reflecting severe pulmonary damage. The pulmonary compromise described in this report suggests that severe pulmonary damage occurred as a result of primary viral pneumonia. Although data are not available, this damage also might be attributable to secondary host immune responses (e.g., through cytokine dysregulation triggered by high viral replication). However, bacterial coinfection in the lung not identified by blood culture or bronchoalveolar lavage cannot be excluded.

…

The high prevalence of obesity in this case series is striking. Whether obesity is an independent risk factor for severe complications of novel influenza A (H1N1) virus infection is unknown. Obesity has not been identified previously as a risk factor for severe complications of seasonal influenza. ….

Further characterization of severe cases of novel influenza A (H1N1) virus infection in the United States and worldwide is needed to determine the frequency of the findings from this limited case-series.

You can read the report here.

Clearly this is very preliminary and I suspect that this will not develop. In other words, I suspect that existing poor health related to the cardiopulmonary system is a serious risk factor with any flu. But we shall see.

The new Swine Flu has now been verified in nineteen US states, with 141 cases. Technically there is 1 death, but since the young girl who died actually caught the flu in Mexico (and came to Texas for treatment) it is hard to say how that should be counted.

WHO characterizes the global spread of the flu as a “rapidly evolving” situation. As of an early morning update from WHO, the swine flu has been confirmed in Mexico (156 confirmed cases) as well as Austria, Canada, Germany, Israel, Netherlands, New Zealand, Spain, Switzerland and the UK. Each of these countries has between 1 and about a dozen cases, except Canada which has 34.

My comments:

1) I’m annoyed at the playing around with the name of the flu. The Israeli Government was wrong to insist on not calling this Swine because of the kosher-pork issue. Nothing about that makes sense. The Minnesota Government led by Republican “I’m not very smart but I won’t raise your taxes” Pawlenty is also wrong in insisting that it be called the H1N1 flu. Pawlenty has stated that we should not call it the “swine flu” because that is disparaging of the pork industry (which is reasonably important here in Minnesota).

That is utterly stupid because you can eat pork chops, ham,and bacon all day, from swine who have the flu, and never get it. There are times when it is appropriate to adjust the name of a disease for various social or political reasons, but it is wrong to do so in response to utter ignorance. Instead, deal with the ignorance. Pawlenty will not deal with the ignorance directly because he is a Republican and, quite honestly, Republicans prefer to foster, and when convenient use, ignorance for their own monetary and political gain.

2) There is no validity whatsoever to the idea that the swine flu is more deadly in Mexico than elsewhere, or that Mexicans are somehow more likely to die of it. Well, the latter may ultimately be true because of health care differences. It may turn out that people in Mexico and Panama will be more likely to die of this flu than people in Sweden and France, owing to disparities in health care across countries. It may also be the case that people in Louisiana will be more likely to die of this flu than people in Minnesota or Massachusetts for the same reason. However, it is also true that the data that are currently available are not sufficient to say anything other than this, and only in a very preliminary way: The current swine flu outbreak seems to have a mortality rate that is roughly similar to the seasonal flu, at present. (But see below.)

3) It s not true, as is reported again and again by reporters who should really stuff a sock in their mouth on waking and keep it there all day, that the swine flu is “mild” in the United States. This misconception and mis statement comes from an illogical extension of number 2 above. This is a serious flu. You get sick as a dog for a week or so, and it can kill you, just like the seasonal flu.

4) Regarding the spread of this flu: More and more people who know what they are talking about are saying that the spread of this flu is similar to that of seasonal flu. In the same way that we can characterize the mortality rate as similar to seasonal flu, we can probably say the same regarding the rate of spread. However, both characterizations are subject to change as data become more available.

There are two separate issues here. On one hand, we have the accumulation and verification of case by case data, and on the other hand, we have the spread of the flu. Over time, the quality of the data will become good enough to make longer term projections and to make assumptions about the missing or lower quality data areas of the world. So, right now, as we see reports of more states in the US or more countries across the world reporting cases for the first time, are we watching the actual spread of the flu, or the improvement of data? Answer: Both, and we can’t separate the two right now with any degree of reliability.

5) Regarding the flu’s virulence and mortality: Good news and bad news. I have one piece of good news and two pieces of bad news.

First the good news: It does seem (see above) that this flu is not extra deadly.

Now the first bit of bad news: The seasonal flu is deadly. This new flu … the swine flu … is like the seasonal flu. It is also deadly. It might be that the currently spreading strain of Swine H1N1 ends up being less deadly, it might end up being more deadly, but if it turns out to be about the same as the seasonal flu and goes though a similar cycle, you can expect several tens of thousands of Americans to die of it.

Maybe thats a good thing. Maybe killing 38,000 Americans twice in one year instead of once will result in a change in attitude towards both the flu and towards vaccination in general. Maybe I’m too cynical. Maybe I’m not.

Now the second piece of bad news, and this is the scary bit. This flu may be worse than a regular flu in that more people will get it … there is not vaccine available now, so it’s initial spread will be unchecked compared to a normal seasonal flu for which vaccines may have been distributed. There is probably not as much of an immunity to this flu as for the seasonal flu (this has yet to be determined but is likely true) so it may be that more people will end up getting this flu.

Fine. But that’s not the real bad news.

The real bad news is that since this is a new flu part of which (flu has different parts that may have different histories) only recently entered the human environment, there might be a slightly higher than we would like to have chance that this flu, while it swaggers around the human population making people sick, will recombine with one or more other flu viruses that are already out there with very nasty results.

This flu could spread around the world as a regular flu, making lots of people sick but not being overly deadly. Then, some time during its spread, or even after it has largely abated, it could mutate through recombination (of some of its parts) and come back as a much more severe flu, causing a truly deadly world wide pandemic.

How possible is this? We have no way of knowing, but that scenario has happened before. From a paper by Taubenberger and Morens:

Historical records since the 16th century suggest that new influenza pandemics may appear at any time of year, not necessarily in the familiar annual winter patterns of interpandemic years…. Thereafter, confronted by the selection pressures of population immunity, these pandemic viruses begin to drift genetically and eventually settle into a pattern of annual epidemic recurrences caused by the drifted virus variants.

In the 1918-1919 pandemic, a first or spring wave began in March 1918 and spread unevenly through the United States, Europe, and possibly Asia over the next 6 months … Illness rates were high, but death rates in most locales were not appreciably above normal. A second or fall wave spread globally from September to November 1918 and was highly fatal. In many nations, a third wave occurred in early 1919 … Clinical similarities led contemporary observers to conclude initially that they were observing the same disease in the successive waves. The milder forms of illness in all 3 waves were identical and typical of influenza seen in the 1889 pandemic and in prior interpandemic years. In retrospect, even the rapid progressions from uncomplicated influenza infections to fatal pneumonia, a hallmark of the 1918-1919 fall and winter waves, had been noted in the relatively few severe spring wave cases. The differences between the waves thus seemed to be primarily in the much higher frequency of complicated, severe, and fatal cases in the last 2 waves.

That paper characterized the pattern of the 1918 flu well, but was written before some key findings in the nature of flu evolution, so I won’t pass on the speculations those authors provide for why this pattern developed.

Taubenberger, Jeffery, & Morens, David (2006). 1918 Influenza: the Mother of All Pandemics Emerging Infectious Diseases, 12 (1)

The swine flu maneno in the 1970s was actually a key moment in the history of epidemiology politics. It also relates to the history of anti-vaccine activism in important and interesting ways. I should probably write a whole post about it. For now, suffice it to say that the government reaction to the sudden appearance of swine flu on the scene was somewhat bungled, it is probably true that the wrong people got screwed, and the swine flu itself turned out to be a false start. But please also note that the epidemiology of the present swine flu is very different from what we had then. And, we have a Democrat in the White House so the government won’t screw it up as badly.

Less traditional and less reliable sources have suggested that there are a large number of cases of type A flu (not necessarily the swine flue) in Queens New York, perhaps something like 100, and two cases in Kansas. If these are real, they will probably be reported by WHO and CDC over the next 24 hours.

In Mexico, there are three separate situations being reported. According to WHO:

In the Federal District of Mexico, surveillance began picking up cases of ILI starting 18 March. The number of cases has risen steadily through April and as of 23 April there are now more than 854 cases of pneumonia from the capital. Of those, 59 have died. In San Luis Potosi, in central Mexico, 24 cases of ILI, with three deaths, have been reported. And from Mexicali, near the border with the United States, four cases of ILI, with no deaths, have been reported.

Of the Mexican cases, 18 have been laboratory confirmed in Canada as Swine Influenza A/H1N1, while 12 of those are genetically identical to the Swine Influenza A/H1N1 viruses from California.

Swine flu us common, not surprisingly, in pigs and passes to humans fairly often. There is a low level background of swine farmers getting the swine flu … and getting sick enough to notice it (many more may get infected but don’t become ill or mistake it for a cold). Rarely, swine flu shows up in a form that can pass between humans.

Flu evolves (if that is the correct term) by exchanging large sections of genome back and forth during its life (if that is the correct term) cycle. A flue can be constructed of bits of avian, bits of swine, and bits of human flu, for instance. A combination of swine and human flu may have the property of transmission between humans and some added virulence (because the swine part is new to the immune system). This particular flu seems to be a combination of swine and human as well as avian components.

One alarming feature of this flu, if confirmed, is that it seems to be worse in healthy prime age individuals. Now, don’t get too worried about this yet, but this could be a problem. This was the case with the 1918 flu, which infected half of the human population and killed millions. The so called “Spanish Flu” was a combo of human and avian genetic components. It killed by causing an overreaction in the immune system. The healthier you are, the better your immune system. The better your immune system, the more likely that flu would kill you.

So you can see why health officials are worried about this flu. You don’t have to worry yet, though. This could still fizzle out. Just keep an eye on things.

Or, panic.

The World Health Organization (WHO) has a threat level system. The lowest threat level is 1, the highest is at 6. The good news is that we are currently only at level 3, which means you should not be worried. The bad news is that some experts think we should be at level 4 already, and as far as I can tell they are correct. Which may make us worry about WHO more than it does about the flu…

Level 3 means that there is no or limited human to human transmission of a flu virus. Since the vast majority of flu viruses are non-human, and the most dangerious ones to humans are those that primarily originate from non humans, or have genetic components that originated from non-humans and are able to spread form humans to humans, as the amount of human to human spread increases the threat level goes up.

So Level 4 is for “increased” human to human spread, and Level 5 is “significant” human to human transmission. We are probably at level 4 because there are apparently dozens of human cases of fatal results out of hundreds (a few hundred, perhaps) cases of a novel flu virus.

It is also notable that this flu affects those who are most likely to be immune or resistant to the more run of the mill flu viruses. This is the hallmark of a new genetic combination that humans are are highly susceptible to.

But even at level 4, you should probably not worry too much. Rather, you should be concerned about the possibility that you will have to start worrying soon, maybe. This could fizzle out over the next couple of weeks, or it could expand. If we go to Level 5, start to worry. If we get to Level 6, put on the mask.

I recommend subscribing to the RSS feeds of a couple of sites:

Effect Measure

H5N1