As you can see in the figure below, different illnesses and pathogens stimulate unique responses in the human immune system - and this is observable in Full Blood Count (FBC) data.
We have developed sophisticated machine learning models which are trained using all the historical FBC data from a population. These models can then be used to detect anomalies in current FBC tests from the same population - a rise in the number of anomalous samples from unrelated people could be a warning sign that an outbreak of infectious disease has occurred.
You can think of the BloodCounts! solution as a tsunami-like warning system for infectious disease outbreaks which uses the human immune response measured by the FBC test.
A major advantage is that our method requires zero knowledge of the causative pathogen to work!