Machine Learning and Prediction of Infectious Diseases: A Systematic Review

Omar Enzo Santangelo, Vito Gentile, Stefano Pizzo, Domiziana Giordano, Fabrizio Cedrone

Machine Learning and Knowledge Extraction 2023 review cited by 124

Source Open-access PDF More in Medicine & Health

importance 7/10 partially solved Data-Driven Disease Surveillance epidemiology stage: research

Problem

Predicting infectious disease outbreaks early enough to act is difficult; this systematic review assessed whether machine learning can do it.

Why it matters

Early outbreak warnings could save lives and reduce epidemic response costs.

What the AI did

A systematic review of 75 studies found that combining machine learning techniques can predict incidence and trends of some infectious diseases.

Results

75 records reviewed; prediction shown possible for some diseases, no accuracy metrics stated

Impact

Reviewed evidence supports ML-based early prediction for some infectious diseases.

Record

Venue	Machine Learning and Knowledge Extraction
Published	2023
Work type	review
Authors	Omar Enzo Santangelo, Vito Gentile, Stefano Pizzo, Domiziana Giordano, Fabrizio Cedrone
Citations	124 (OpenAlex, at import time)
References	95
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.3390/make5010013
Open access	gold
Keywords	machine learning, infectious diseases, outbreak prediction, systematic review, epidemiology

Importance rating

7 / 10

significant — thousands to millions affected

Early prediction of infectious disease outbreaks could affect epidemic response at population scale; review of 75 studies shows prediction is possible for some diseases, but no disease burden or accuracy figures stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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