The use of social media as a syndromic sentinel for diseases is an emerging field of growing relevance as the public begins to share more online, particularly in the area of influenza. Several applications have been developed to predict or monitor influenza activity using publicly posted or self-reported online data; however, few have prioritized accuracy at the local level. In 2016, the Cook County Department of Public Health (CCDPH) collected localized Twitter information to evaluate its utility as a potential influenza sentinel data source.

Early Notification Detection Systems have taken a critical role in providing early notice of disease outbreaks. To improve the detection methods for disease outbreaks, many detection methods have been created and implemented. However, there is limited information on the effectively of syndromic surveillance in Thailand. Knowing the performance, strengths and weakness of these surveillance systems in providing early warning for outbreaks will increase disease outbreak detection capacity in Thailand.

Objective:

Ministries of Health in Low and Middle Income Countries (LMIC) are making or trying to make public health decisions for infectious disease conditions like HIV using data garnered from sentinel events and disease tracking in the community. The process of gathering and aggregating data for these case-based reports for is, in all too often a cumbersome or paper-based process. The Center for Disease Control (CDC) was interested in prototyping and piloting approaches that could improve the efficiency and reliability of case reports in resource-constrained environments.