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Aren't these values sensitive to the population size of the sampled
population? Since the draw can hardly be random, I would imagine
sampling effort would need to be larger in large populations.

 

________________________________

From: Global Ranavirus Consortium [mailto:[log in to unmask]] On
Behalf Of Seigel, Richard
Sent: 18 June 2012 19:00
To: [log in to unmask]
Subject: Re: question from the netherlands

 

Jesse et al:

 

There are many different ways of calculating sample size. A simple (and
interactive way) of determining the required sample size for a study is
to model this as a Binomial Distribution, since animals can only be
positive or negative. By using one of the many web-based Binomial
calculators available (I recommend
http://vassarstats.net/binomialX.html), you can easily model the
probability of getting zero positive results in a sample size of N
animals, assuming a specific level of prevalence.

 

Here is how this works for the calculator noted above:

 

You need to specify three parameters: N (number of individuals you want
to use for the total sample), K (the number of positive animals, which
you set as zero), and P (the "true" prevalence rate, from 0-1.0).

 

Using the calculator noted above, if you fill in these values for
Jesse's example (30 amphibians sampled, prevalence = 0.1, zero found
with infections), you will get the output shown on the attached page.
The critical part of this is the part listed under "hypothesis testing"
towards the bottom the output:

 

P: 0 or fewer out of 30

One-Tail Two-Tail

Method 1. exact binomial calculation 0.0423

 

 

-this tells you that the probability of sampling 30 animals at random
where the actual prevalence is 0.1 and finding zero "hits" is 0.0423.
The inverse of this is the confidence level (1-0.0423 = 95.7%). If the
actual prevalence rate was 5%, you would need a sample of 60 animals. 

 

The nice thing about this approach is that it is (a) free and (b) can be
used for any combination of values of estimated sample size, levels of
prevalence, and desired confidence levels. However, keep in mind that,
like any inference-based statistic, you need to be sure that you are
sampling at random and that there are no false negatives. Based on
Jesse's last email, I wonder how confident we can be of that?

 

Rich Seigel

 

 

 

 

Richard A. Seigel

Professor

Department of Biological Sciences

Towson University

Towson, MD 21252

 

410-704-3123

 

http://towson.academia.edu/RichardSeigel
<http://towson.academia.edu/RichardSeigel> 

 

 

From: Global Ranavirus Consortium [mailto:[log in to unmask]] On
Behalf Of Jesse Brunner
Sent: Monday, June 18, 2012 1:12 PM
To: [log in to unmask]
Subject: Re: question from the netherlands

 

Dear All, 

 

I was just looking through the OIE manual again, based on Greg's
suggestion, and was struck by the lack of validated PCR, especially qPCR
assays in it. From my experience (and I believe that of others, though I
won't speak for them), quantitative real time PCR is much more
(analytically) sensitive than standard PCR (e.g., with the MCP4/5
primers in Mao et al. 1997) and more sensitive even than virus isolation
in cell culture. We are consistently detecting the equivalent of <1
plaque forming unit in asymptomatic animals from experiments and from
the wild. Jaramillo et al. have a validated assay for ranavirus in fish
that seems to support the assertion that qPCR is more sensitive: 

"The assay had 100-fold greater analytical sensitivity compared to virus
isolation in Bluegill fry (BF-2) cells, detected all viruses in a panel
of 20 ranaviruses from Australia, America, Europe and the United
Kingdom, did not detect two disputed members of the genus Ranavirus
(doctorfish virus and guppy virus 6) and did not detect a
megalocytivirus or two cyprinid herpesviruses."  (Note: I do understand
that analytic sensitivity is not the same as diagnostic sensitivity, but
if virus isolation is our gold standard then qPCR cannot, by definition,
have greater diagnostic sensitivity!) 

 

So I guess my question is, if someone is interested in ensuring that
their animals are ranavirus free, shouldn't we be pointing them to these
more sensitive assays? What are your thoughts? Would it be worth
"stressing out" some subset of the animals to see if animals carrying
inapparent infections might recrudesce? The question of whether they can
develop full blown, transmissible infections seems very relevant in
these cases.

 

I would also like to point out that the protocols in the OIE manual
state, in section 6: "Statistically valid sampling practices need to be
used but these cannot presently be defined for amphibians." I am not
sure why amphibians would present a special case in epidemiology, but I
was not at these meetings, so perhaps someone could clear up my
confusion. Barring that, I might suggest using the standard formulas for
calculating the sample sizes required to be, say, 95% confidence that
the prevalence of infection is at or below some level (e.g., 10%) given
a population of N (e.g., 10^6)... here n = 30.  I'm sure there are other
references for the formula, but this is the one I have:

Cannon, R.M., and R.T. Roe. 1982. Livestock Disease Surveys: A Field
Manual For Veterinarieans. Canberra: Australian Bureau of Animal Health

I've attached a spreadsheet I created a while back to help me see the
relationships between confidence-level, population sizes, the number of
disease animals, and the sample size. It is rather busy, but some
playing with the numbers in different colors should clear up which graph
is associated with which equation. Or I'm happy to answer questions
about it.  

 

In any case, if there is a better way of establishing statistically
valid sample sizes, I would love to hear them. It would be great for the
members of this list to be on the same page, more or less, about how
best to sample.

 

Best wishes, 

 

Jesse

 

Jesse Brunner

School of Biological Sciences

Washington State University

PO Box 644236

Pullman, WA 99164 USA

[log in to unmask]

509-335-3702 

 

 

 

http://www.oie.int/international-standard-setting/aquatic-manual/access-
online/

 

Jaramillo D, Tweedie A, Becker JA, Hyatt A, Crameri S et al. (2012) A
validated quantitative polymerase chain reaction assay for the detection
of ranaviruses (Family Iridoviridae) in fish tissue and cell cultures,
using EHNV as a model. Aquaculture 

 

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