Table 6 Commonly used data sources and types of machine learning suitable for each use case based on studies from the in-depth review about past pandemics, SARS, and COVID-19.

From: Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

Key use case

Commonly used data sources

Suitable types of ML

Forecasting infectious disease dynamics and effects of interventions

Publicly available counts (e.g., Johns Hopkins COVID-19 map, Worldometer, World Health Organization), media reports, commercial publications, web searches (e.g., Google Trends), social media (e.g., Twitter), census data, population-level comorbidity statistics, data on outbreaks of similar pathogens

Augmenting traditional models: neural networks

Data-driven ML: recurrent neural networks

Surveillance and outbreak detection

Social media (e.g., Twitter), web searches, news reports, medical record data (structured and unstructured fields)

Text mining: natural language processing

Classification: support vector machines, transformer neural networks

Real-time monitoring of adherence to public health recommendations

Cameras in public spaces

Compliance with mandated quarantine: proprietary facial recognition

Adherence to mask wearing, social distancing, and sanitation: proprietary computer vision

Real-time detection of influenza-like illness

Cameras and sensors in public spaces

Interpretation of thermal imaging: neural networks, computer vision

Triage and timely diagnosis of infections

Exposure history, medical record data (structured and unstructured fields, laboratory results, chest imaging)

Interpretation of chest imaging: convolutional neural networks

Triage based on routinely collected medical record data: no standout ML

Interpretation of unstructured clinical notes: transformer neural networks

Prognosis of illness and response to treatment

Medical record data (structured and unstructured fields, laboratory results, chest imaging)

Based on chest imaging: combination of convolutional and recurrent neural networks

Based on routinely collected medical record data: no standout ML

Interpretation of unstructured clinical notes: natural language processing, neural networks

  1. ML machine learning, SARS severe acute respiratory syndrome.