ML algorithms are employed to analyze complex and large-scale epidemiological data. They can identify risk factors, predict disease outbreaks, and model the spread of infectious diseases. Some common applications include:
1. Disease Prediction and Outbreak Detection: ML models can predict the likelihood of disease outbreaks by analyzing historical data, environmental factors, and socio-economic indicators. 2. Risk Factor Analysis: Algorithms can identify and quantify the impact of various risk factors on health outcomes, aiding in the development of targeted intervention strategies. 3. Surveillance and Monitoring: ML can enhance the accuracy and timeliness of disease surveillance systems by automatically processing large volumes of health data from multiple sources.
