"[1] Public health surveillance may be used to track emerging health-related issues at an early stage and find active solutions in a timely manner.
A passive surveillance system consists of the regular, ongoing reporting of diseases and conditions by all health facilities in a given territory.
Many large institutions, such as the WHO and the Centers for Disease Control and Prevention (CDC), have created databases and modern computer systems (public health informatics) that can track and monitor emerging outbreaks of illnesses such as influenza, SARS, HIV, and even bioterrorism, such as the 2001 anthrax attacks in the United States.
[4] Other illnesses such as one-time events like stroke and chronic conditions such as diabetes, as well as social problems such as domestic violence, are increasingly being integrated into epidemiologic databases called disease registries.
[5] These systems intersect with the field of medical informatics, and are rapidly becoming adopted by hospitals and endorsed by institutions that oversee healthcare providers (such as JCAHO in the United States).
[9] An early awareness and response to a bioterrorist attack could save many lives and potentially stop or slow the spread of the outbreak.
[13] However, it has been shown that the original approach behind Google Flu Trends had various modelling deficiencies leading to significant errors in its estimates.
[14] More recently, a series of more advanced linear and nonlinear approaches to influenza modeling from Google search queries have been proposed.
The most important ones being the use of search-based trends on sites like Google and Wikipedia, social media posts on platforms like Facebook and Twitter, and participatory surveillance websites such as Flu Near You and Influenzanet.
[citation needed] Examples of social media public health surveillance include HealthTweets, which gathers data from Twitter.
[18] During the COVID-19 pandemic, Facebook used aggregated, anonymized data collected from its platforms to provide human movement information to disease models.
Unlike most syndromic surveillance systems, in which each record is assumed to be independent of the others, laboratory data in chronic conditions can be theoretically linked together at the individual patient level.
[citation needed] Laboratory registries allow for the analysis of the incidence and prevalence of the target condition as well as trends in the level of control.
[24] The data included measures of blood sugar control (glycated hemoglobin A1c), cholesterol, and kidney function (serum creatinine and urine protein), and were used to monitor the quality of care at the patient, practice, and population levels.
The system also generated reminders and alerts with guideline-based advice for the practice as well as a periodic roster of each provider's patients and a report card summarizing the health status of the population.