Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review
Problem
Arbovirus outbreaks are hard to anticipate, and public health decision-makers need forecasting models of disease and vector dynamics.
Why it matters
Arboviruses are neglected tropical diseases posing serious public health challenges worldwide.
What the AI did
A systematic review of 139 studies (2015-2020) identified machine learning prediction models for arboviruses and their vector dynamics, mapping challenges in the field.
Results
139 studies included after filtering from 429 articles returned.
Impact
The review catalogues existing forecasting approaches and their challenges; it does not itself solve forecasting.
Record
| Venue | Frontiers in Public Health |
|---|---|
| Published | 2022 |
| Work type | review |
| Authors | Clarisse Lins de Lima, Ana Clara Gomes da Silva, Giselle Machado Magalhães Moreno, Cecilia Cordeiro da Silva, Anwar Musah, Aisha Aldosery, Lívia Márcia Mosso Dutra, Tércio Ambrizzi et al. |
| Citations | 23 (OpenAlex, at import time) |
| References | 123 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3389/fpubh.2022.900077 |
| Open access | gold |
| Keywords | arboviruses, machine learning, forecasting, systematic review, public health |
Importance rating
significant — thousands to millions affected
Abstract states arboviruses are Neglected Tropical Diseases posing public health challenges for countries worldwide; forecasting supports decision-making. Review only catalogues models, no numbers on disease burden.