Method of predicting severity of multiple sclerosis

摘要

A method of predicting severity of multiple sclerosis (MS) in an animal in a vivarium is described. Animal activity data is collected at multiple times during the night. Sequential time regions of the night are identified as high-activity, activity-drop, or low-activity regions. Embodiments are described to quantify a drop, during the night, of an animal's activity level. These quantified activity-drop scalars for consecutive nights are accumulated in an animal health dataset. Then, an MS severity index function is applied to this dataset that, in response to the level of activity change and the speed of activity change, predicts or measures severity of MS in the animal. One embodiment quantifies an activity-drop by fitting straight-line curves through the data in the three nightly regions. Another embodiment uses a Fourier transform on a circle and a linear combination. Another embodiment compares areas under data curves in the regions. Animals may be housed in cages with other animals.