Dr. Michael L. Larsen – Research Overview

Accounting for Natural Variability in Airborne Pathogen Risk Estimation

 

Basic Background

 

Much has been made of the anthrax attacks of 2001.  There’s just something sensationalistic about an airborne “nasty thing” that could kill a large number of people that the media just loves to scare people with (exhibit two – the SARS hoopla).

My cynicism aside, there is a non-negligible risk that somewhere down the line, something nasty will be put into the atmosphere – either intentionally or accidentally.  It has happened before on a fairly large scale (e.g. the accidental anthrax leak in Sverdlovsk in 1979), and to think that it will never happen again is just naïve.  Too many unstable people have access to too many nasty things.

 

In addition to preparing and informing the general populus regarding what should be done in the event of an airborne threat, as scientists we are in the position to make another contribution – estimating the severity of an airborne pathogen release to get realistic infection estimates into the hands of the “deciders”.

 

 

 

So what’s the Problem?

 

Let us say you are put into a room with 100 particles of some nasty airborne thing “X”.  What are your chances of getting infected?

 

Well, that depends on a number of things including (but probably not limited to):

 

1.      What is your personal tolerance to nasty thing X?

2.      How long are you in the room?

3.      How quickly are you breathing?

4.      How deeply are you breathing?

5.      Are you moving around?  (How does that affect how many of the 100 particles stay in the air and how often you rebreathe the “same air” you already inhaled?)

6.      What’s the ventilation in the room like?

7.      Will nasty thing X hurt you if it gets caught in your throat, or does it have to make its way to your lungs to be a threat?

 

To determine your personal risk, we’ve got to ballpark all of these things.  Some of these are easier than others; some are nearly impossible to guess accurately, but we have to try anyway.

 

The problem gets even uglier when you remove the borders of the room and include complicated geometry (e.g. buildings), flow patterns near the surface of the earth (e.g. winds), and a bunch of other people with a bunch of different personal tolerances.

 

 

 

 

What is Dr. Larsen doing to try and Solve the Problem?

 

There are “standard ways” of estimating each of the 7 items in the list above.  Some of the ways are reasonable, some are obviously extreme oversimplifications made because there doesn’t seem to be a reasonable way to handle the problem more realistically.  There are some cases, however, where improvements could be made in a way that isn’t too impossible.

 

For example, items 2,3,4, and 5 combine to help determine the “expected dose” of an individual in the room.  If you are in the room for T seconds, you take B breaths per second, your average breath contains a volume V of new air, the volume of the room is S, and by moving around you don’t change too many things, then your “expected dose” would be equal to:

 

D = 100*T*B*V/S

 

(The 100 because we said there were 100 particles of X in the room).  However, if you are in the room for 2 seconds, it is highly doubtful you received EXACTLY 200*B*V/S particles.  D is just the MEAN expected dose.  There is a distribution around this value.

 

Some people actually take into account that the actual received dose is not exactly D.  HOWEVER, in every published case that I have found, when the actual received dose is not assumed to always be equal to D, they assume that the particles – at the very least – are distributed perfectly randomly.  (If you are reading these research pages in order, you’re probably getting sick of that link.  Maybe you can see the recurring theme in the research).

 

Even if you ignore all of the other work I and other scientists have done on the clustering of airborne particulates, it hardly seems reasonable to assume that the particles associated with an intentional release of an airborne pathogen would be equally distributed through a volume.  You’d expect that all the particles were likely released from a point and not necessarily mixed perfectly throughout the volume.  Consequently, the implications of that false assumption of perfect randomness should be fleshed out to come up with a more reasonable estimate of infection risk.

 

Unfortunately, this is not easy because issue number 1 in the above section is rather difficult to handle in an accurate way.  There are some cases (e.g. Q-fever, Tuberculosis, or roto-virus) where relationship 1 may be very simple – a dose of 1 particle means infection.  These pathogens may be the best way to start examining the problem due to the simplicity of the so-called “dose-response relationship”.

 

Currently, Dr. Larsen is attempting to use the current assumptions regarding number 1 above for individuals and populations, combined with more realistic methods of examining the true dose variability likely experienced by a population to come up with more reliable risk estimates.

 

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