Hand, foot, and mouth disease is a highly infectious disease common among early childhood populations caused by human enteroviruses (Enterovirus genus).1 The enteroviruses responsible for HFMD generally cause mild illness among children in the United States with symptoms of fever and rash/blisters, but have also been linked to small outbreaks of severe neurological disease such as meningitis, encephalitis, and acute flaccid myelitis.2 Enteroviruses circulate year-round but increase in the summer-fall months across much of the United States.3 The drivers of this seasonality are not fully und

Syndromic surveillance data is typically used for the monitoring of symptom combinations in patient chief complaints (i.e. syndromes) or health indicators within a population to inform public health actions. The Tennessee Department of Health collects emergency department (ED) data from more than 80 hospitals across Tennessee to support statewide situational awareness. Most hospitals in Tennessee provide data within 48 hours of the patient being seen in the emergency department.

The public health problem identified by Alabama Department of Public Health Syndromic Surveillance (AlaSyS) was that the data reflected in the user application of ESSENCE (Electronic Surveillance System for the Early Notification of Community-based Epidemics) was underestimating occurrences of syndromic alerts preventing Alabama Department of Public Health (ADPH) from timely recognition of potential public health threats.

Infectious diseases present with multifarious factors requiring several efforts to detect, prevent, and break the chain of transmission. Recently, machine learning has shown to be promising for automated surveillance leading to rapid and early interventions, and extraction of phenotypic features of human faces. In addition, mobile devices have become a promising tool to provide on-the-ground surveillance, especially in remote areas and geolocation mapping.