Modified Mosquito Programs’ Surveillance Needs and An Image-Based Identification Tool to Address Them
Effective mosquito surveillance and control relies on rapid and accurate identification of mosquito vectors and confounding sympatric species. As adoption of modified mosquito (MM) control techniques has increased, the value of monitoring the success of interventions has gained recognition and has p...
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| Format: | Article |
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Frontiers Media S.A.
2022-02-01
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| Series: | Frontiers in Tropical Diseases |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fitd.2021.810062/full |
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| author | Jewell Brey Bala Murali Manoghar Sai Sudhakar Kiley Gersch Tristan Ford Margaret Glancey Jennifer West Sanket Padmanabhan Angela F. Harris Autumn Goodwin |
| author_facet | Jewell Brey Bala Murali Manoghar Sai Sudhakar Kiley Gersch Tristan Ford Margaret Glancey Jennifer West Sanket Padmanabhan Angela F. Harris Autumn Goodwin |
| author_sort | Jewell Brey |
| collection | DOAJ |
| description | Effective mosquito surveillance and control relies on rapid and accurate identification of mosquito vectors and confounding sympatric species. As adoption of modified mosquito (MM) control techniques has increased, the value of monitoring the success of interventions has gained recognition and has pushed the field away from traditional ‘spray and pray’ approaches. Field evaluation and monitoring of MM control techniques that target specific species require massive volumes of surveillance data involving species-level identifications. However, traditional surveillance methods remain time and labor-intensive, requiring highly trained, experienced personnel. Health districts often lack the resources needed to collect essential data, and conventional entomological species identification involves a significant learning curve to produce consistent high accuracy data. These needs led us to develop MosID: a device that allows for high-accuracy mosquito species identification to enhance capability and capacity of mosquito surveillance programs. The device features high-resolution optics and enables batch image capture and species identification of mosquito specimens using computer vision. While development is ongoing, we share an update on key metrics of the MosID system. The identification algorithm, tested internally across 16 species, achieved 98.4 ± 0.6% % macro F1-score on a dataset of known species, unknown species used in training, and species reserved for testing (species, specimens respectively: 12, 1302; 12, 603; 7, 222). Preliminary user testing showed specimens were processed with MosID at a rate ranging from 181-600 specimens per hour. We also discuss other metrics within technical scope, such as mosquito sex and fluorescence detection, that may further support MM programs. |
| format | Article |
| id | doaj-art-f7c1416bb747484b8cbf214b5a2c6a38 |
| institution | Kabale University |
| issn | 2673-7515 |
| language | English |
| publishDate | 2022-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Tropical Diseases |
| spelling | doaj-art-f7c1416bb747484b8cbf214b5a2c6a382025-02-10T12:18:50ZengFrontiers Media S.A.Frontiers in Tropical Diseases2673-75152022-02-01210.3389/fitd.2021.810062810062Modified Mosquito Programs’ Surveillance Needs and An Image-Based Identification Tool to Address ThemJewell Brey0Bala Murali Manoghar Sai Sudhakar1Kiley Gersch2Tristan Ford3Margaret Glancey4Jennifer West5Sanket Padmanabhan6Angela F. Harris7Autumn Goodwin8Vectech, LLC, Baltimore, MD, United StatesVectech, LLC, Baltimore, MD, United StatesVectech, LLC, Baltimore, MD, United StatesVectech, LLC, Baltimore, MD, United StatesVectech, LLC, Baltimore, MD, United StatesPlacer County Mosquito and Vector Control District, Roseville, CA, United StatesVectech, LLC, Baltimore, MD, United StatesVectech, LLC, Baltimore, MD, United StatesVectech, LLC, Baltimore, MD, United StatesEffective mosquito surveillance and control relies on rapid and accurate identification of mosquito vectors and confounding sympatric species. As adoption of modified mosquito (MM) control techniques has increased, the value of monitoring the success of interventions has gained recognition and has pushed the field away from traditional ‘spray and pray’ approaches. Field evaluation and monitoring of MM control techniques that target specific species require massive volumes of surveillance data involving species-level identifications. However, traditional surveillance methods remain time and labor-intensive, requiring highly trained, experienced personnel. Health districts often lack the resources needed to collect essential data, and conventional entomological species identification involves a significant learning curve to produce consistent high accuracy data. These needs led us to develop MosID: a device that allows for high-accuracy mosquito species identification to enhance capability and capacity of mosquito surveillance programs. The device features high-resolution optics and enables batch image capture and species identification of mosquito specimens using computer vision. While development is ongoing, we share an update on key metrics of the MosID system. The identification algorithm, tested internally across 16 species, achieved 98.4 ± 0.6% % macro F1-score on a dataset of known species, unknown species used in training, and species reserved for testing (species, specimens respectively: 12, 1302; 12, 603; 7, 222). Preliminary user testing showed specimens were processed with MosID at a rate ranging from 181-600 specimens per hour. We also discuss other metrics within technical scope, such as mosquito sex and fluorescence detection, that may further support MM programs.https://www.frontiersin.org/articles/10.3389/fitd.2021.810062/fullcomputer visionmosquito surveillanceartificial intelligence (AI)imagingspecies identificationmodified mosquito |
| spellingShingle | Jewell Brey Bala Murali Manoghar Sai Sudhakar Kiley Gersch Tristan Ford Margaret Glancey Jennifer West Sanket Padmanabhan Angela F. Harris Autumn Goodwin Modified Mosquito Programs’ Surveillance Needs and An Image-Based Identification Tool to Address Them Frontiers in Tropical Diseases computer vision mosquito surveillance artificial intelligence (AI) imaging species identification modified mosquito |
| title | Modified Mosquito Programs’ Surveillance Needs and An Image-Based Identification Tool to Address Them |
| title_full | Modified Mosquito Programs’ Surveillance Needs and An Image-Based Identification Tool to Address Them |
| title_fullStr | Modified Mosquito Programs’ Surveillance Needs and An Image-Based Identification Tool to Address Them |
| title_full_unstemmed | Modified Mosquito Programs’ Surveillance Needs and An Image-Based Identification Tool to Address Them |
| title_short | Modified Mosquito Programs’ Surveillance Needs and An Image-Based Identification Tool to Address Them |
| title_sort | modified mosquito programs surveillance needs and an image based identification tool to address them |
| topic | computer vision mosquito surveillance artificial intelligence (AI) imaging species identification modified mosquito |
| url | https://www.frontiersin.org/articles/10.3389/fitd.2021.810062/full |
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