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for Mosquito Surveillance
Click here to download Windows Excel 2000/2003 v4 ZIP . (1.43MB)
Click here to download Windows Excel 2007 v4 ZIP file. (1.53MB)
arbovirus surveillance data are useful in tracking virus activity.
The most basic form of mosquito-based surveillance data presentation
- and that currently used by CDC's ArboNET system - is the number
of positive mosquito pools found in collections of a particular
mosquito species over a defined time period and area.
encourages surveillance programs to routinely incorporate a more
informative index of relative virus activity, the virus infection
rate (IR), into their mosquito-based evaluation of local virus
activity patterns. At the county level or below, weekly tracking
of mosquito IR can provide important predictive indicators of
transmission activity levels associated with elevated human risk.
of the IR are usually presented as the number of infected mosquitoes
per 1,000 tested. The simplest estimate, the minimum infection
rate (MIR), is calculated: ([number of positive pools / total
specimens tested] x 1000), with the data representing a single
species or species group collected over a time period and geographic
area relevant to the goals of the surveillance program. The MIR
uses the assumption that a positive pool contains only one infected
mosquito, an assumption that may be invalid when infection rates
are high, as has been observed during West Nile virus epidemics.
Brad Biggerstaff, Mathematical Statistician at CDC/DVBID, developed
an easy-to-use program for calculating IR estimates from mosquito
pool data using methods that do not require the assumption used
in the MIR calculation. This program also includes calculation
of confidence intervals which reflect, in part, the sample sizes
used in the calculations. The confidence intervals (or any other
uncertainty measure) are essential for interpreting the precision
of the IR estimate.
program and instructions are contained in the downloadable zip
file below. Written for Excel 2000, this Excel add-in computes
point and confidence interval estimates of IRs (i.e., infection
prevalence) using data from pooled samples, where pool sizes may
differ. Bias-corrected likelihood methods are used to estimate
infection rate, and a skew-corrected score confidence interval
is computed by default. Traditional methods using the MIR are
available for comparison.
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