Detection lead time has been measured on eight farms in the Netherlands.Not on a US broiler farm.
PoultraIQ is recruiting research and grower partners for structured field validation of PoultraIQ Daily.
On a commercial broiler farm, using only the data a grower already records by hand, how many hours of advance warning can a simple statistical anomaly model provide before a condition becomes operationally obvious?
This is a narrow question and that is intentional. It is answerable inside a single growout cycle, it does not require new hardware on the farm, it is compatible with biosecurity restrictions because it requires no vendor site presence, and the answer is useful whether it is large or small.
It is also nearly unstudied. The published work on detection lead time from grower-recorded daily data amounts to a small number of retrospective avian influenza analyses in Europe. There is no prospective validation, nothing on endemic production disease, and nothing on a US broiler farm. We set out what is actually published, including the study that contradicted our own earlier assumption, in our note on detection lead time.
Three kinds of partner.
University extension and poultry science programs
Land-grant institutions with grower relationships and research flock access. We are prepared to serve as the industry partner on a grant application and to provide the software at no cost for the study period.
Grower cooperators
Independent and multi-house operators willing to run a parallel log for one to two cycles. Free access, no hardware, no site visits required, and your operating data is not shared with your integrator.
Equipment and sensor partners
Controller and sensor manufacturers interested in whether an anomaly layer sitting on top of existing installed hardware increases the value of that hardware.
What a pilot involves.
- Define the baseline.
One cycle of parallel logging establishes normal for each house.
- Run the flags silently.
Anomaly detection runs without alerting, so it cannot influence operator behavior during the measurement window.
- Reconcile against events.
Flags are compared retrospectively against recorded events — mortality spikes, equipment failures, water line issues, ventilation problems — to measure lead time and false-positive rate.
- Publish the result.
With the partner’s consent, and whether or not the result is favorable.
Non-dilutive pathways we are pursuing.
PoultraIQ is structuring its validation work around established public research funding, so that the first growers to run it are not the ones paying for the measurement.
USDA SBIR
Automated poultry-house monitoring and early disease detection through behavioral analysis are named eligible topics under the livestock health area.
What we have written down.
The evidence base
What is actually known about broiler house technology, grower economics, and the mortality trend — with sample sizes, dates, and the places where the data does not exist.
The landscape
Who else is building software for poultry houses, what happened to the ones that failed, and where a manual-entry product does and does not compete.
Notes
Short technical pieces on detection lead time, water intake as a leading indicator, and the limits of a rolling baseline.
Propose a pilot or a joint application.
Tell us what you have access to — houses, growers, a research flock, or a grant cycle with a deadline.