Publication Type
Journal Article
Version
publishedVersion
Publication Date
3-2021
Abstract
Machine learning (ML) techniques excel at forecasting, clustering, and classification tasks, making them valuable for various aspects of mosquito control. In this literature review, we selected 120 papers relevant to the current state of ML for mosquito control in urban settings. The reviewed work covers several different methodologies, objectives, and evaluation criteria from various environmental contexts. We first divided the existing papers into geospatial, visual, or audio categories. For each category, we analyzed the machine learning pipeline, from dataset creation to model performance. We conclude with a discussion of the challenges and opportunities for further research. While the reviewed ML methods in mosquito control are promising, we recommend a) increased use of crowdsourced and citizen science data, b) a standardized and open ML pipeline for reproducible results, and c) research that incorporates advances in ML. With these suggestions, ML techniques could lead to effective mosquito control in urban environments.
Keywords
Vector control, Machine learning, Mosquitoes, Dengue, Malaria, Urban data science
Discipline
Pharmacology, Toxicology and Environmental Health | Public Health
Research Areas
Integrative Research Areas
Publication
Ecological Informatics
Volume
61
First Page
1
Last Page
14
ISSN
1574-9541
Identifier
10.1016/J.ECOINF.2021.101241
Publisher
Elsevier
Citation
JOSHI, Ananya and MILLER, Clayton.
Review of machine learning techniques for mosquito control in urban environments. (2021). Ecological Informatics. 61, 1-14.
Available at: https://ink.library.smu.edu.sg/cis_research/618
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/J.ECOINF.2021.101241