  Clinicians conduct routine diagnosis by scrutinizing signs and symptoms of
patients in treating epidemics. This skill evolves through trial-and-error and
improves with time. The success of the therapeutic regimen relies largely on
the accuracy of interpretation of such sign-symptoms, based on which the
clinician ranks the potent causes of the epidemic and analyzes their
interdependence to devise sustainable containment strategies. This study
proposed an alternative medical front, a VIRtual DOCtor (VIRDOC), that can
self-consistently rank key contributors of an epidemic and also correctly
identify the infection stage, using the language of statistical modelling and
Machine Learning. VIRDOC analyzes medical data and then translates these into a
vector comprising Multiple Linear Regression (MLR) coefficients to
probabilistically predict scores that compare with clinical experience-based
assessment. The VIRDOC algorithm, risk managed through ANOVA, has been tested
on dengue epidemic data (N=100 with 11 weighted sign-symptoms). Results highly
encouraging with ca 75% accurate fatality prediction, compared to 71.4% from
traditional diagnosis. The algorithm can be generically extended to analyze
other epidemic forms.
