Symptom-based data preprocessing for the detection of disease outbreak

Early warning systems for outbreak detection is a challenge topic for researchers in the epidemiology and biomedical informatics fields. We are proposing a new method for detecting disease epidemics using a symptom-based approach. The data was collected from developed mobile applications which include users' demographic information and a list of chief complaint symptoms. Deliberated outbreaks are differentiated from seasonal outbreak by specific symptoms that represent a sign of infection. These symptoms were grouped, classified, and then converted to a time-series digital signal using the consensus scoring approach. Through the syndromic grouping method, the system digitized each data package into a single independent variable that is ready for further one-dimensional signal processing to predict disease outbreaks in the future.