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Public health surveillance has always depended on one fundamental ability of knowing what is happening in a population’s health and responding before a problem becomes a crisis.

For decades, public health professionals have collected and analyzed information from hospitals, laboratories, communities, health facilities, and other sources to detect diseases, monitor trends, and guide interventions. Today, the volume, speed, and complexity of health information are increasing rapidly.

Every day, public health systems generate vast amounts of information, from disease surveillance reports and laboratory results to hospital records, community surveys, environmental data, and population health indicators.

But data alone does not save lives, Its value comes from how quickly and effectively it can be transformed into  actionable intelligence, informed decisions, and measurable public health interventions.This is where AI in Public Health Decision-Making is creating new opportunities.

Algorithms’ ability to analyze and interpret large amounts of information in a relatively short time, too big and too complex for humans, may well be the blessing that public health agencies need to improve their response to outbreaks in the age of globalization.

Artificial intelligence (AI) is changing the way we conduct public health intelligence by quickly analysing vast and varied datasets, identifying patterns that are meaningful in an epidemiological sense, prioritising risks, and speeding and streamlining our responses.

Public health surveillance systems used to rely on reports from hospitals, laboratories and public health authorities. These systems are still important. Now we have a lot of digital information to look at and it is hard to analyze it all quickly