Farrington Algorithm



Start Year

Insert the start year of the data

Start Month

Insert the data start date

Start Day

Insert the data start date


If the time unit is weekly, select the number 52 and if the time unit is monthly, select the number 12

Range of Data(min)

Insert the beginning of range under review

Range of Data(max)

Insert the end of the range under review


How many years back in time to include when forming the base counts

w )windows size(

Windows size, i.e. number of weeks to include before and after the current week


An approximate (two-sided) (1 − α) prediction interval is calculated

Choose xlsx File

In this section, upload the data file. The data structure should be according to the desired sample.

Farrington Algorithm

Due to significant variability in surveillance data, Farrington et al. (1996) introduced a quasi- Poisson regression model for the early detection of outbreaks based on reports received at the Communicable Disease Surveillance Center. Let yi be the baseline count of a disease under the surveillance system corresponding to the baseline week ti , independently distributed with mean μi and variance ∅μi . Considering a linear time trend in reporting the frequency of the disease, the regression model is defined as

where ti measures time on a weekly scale and all estimates are obtained by the quasi-likelihood method. Trends are included in the regression model by fitting a linear time variable. This adjusted log-linear regression is very sensitive to overdispersion, detecting small increases in reporting of diseases with low incidence, as well as large increases in reporting of diseases with high incidence. For more details about Farrington and other outbreak detection algorithms based on generalized linear model, please refer to the following references.


Farrington C, Andrews NJ, Beale A, Catchpole M. A statistical algorithm for the early detection of outbreaks of infectious disease. Journal of the Royal Statistical Society: Series A (Statistics in Society). 1996;159(3):547-63.

– Designing a set of evaluation tools for outbreak detection algorithms in the timely discovery of single-source and progressive epidemics. doctorate thesis Hamedan University of Medical Sciences.