Development and effectiveness test of an internet-based real-time monitoring device for malaria parasite vector mosquitoes
4 affiliations
- 1Department of Pathogen Biology, Institute of Tropical Medicine, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
- 2Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Basic Medical Sciences, Hainan Academy of Medical Sciences, Hainan Medical University, Haikou, Hainan, China
- 3Chongqing Wanzhou District Center for Disease Control and Prevention, Chongqing, China
- 4Department of Population Health and Disease Prevention, Joe C Wen School of Population and Public Health, University of California at Irvine, Irvine, California, United States of America
Abstract
Background: Malaria remains a major global health threat, and effective vector surveillance is essential for timely vector control. However, surveillance systems are challenged by behavioral shifts in Anopheles mosquitoes, labor-intensive procedures, and delays in real-time data transmission. Existing electronic monitoring tools often suffer from low sensitivity and specificity, creating an urgent need for automated, high-precision alternatives. Methods: We developed the “Black Box,” a 3D-printed, internet-enabled device integrating multispectral light, thermal simulation, chemical attractants, and photocatalytic materials with automated counting and wireless data transmission. Laboratory trials demonstrated trapping rates of 94.00%–95.00% for Anopheles sinensis, An. stephensi, and An. anthropophagus, with data consistency exceeding 97.00%. Semi-field tests yielded trapping rates of 73.60%–90.80% and consistency above 92.00%. In field deployments, the device captured 5,109 mosquitoes with a 94.17% consistency rate and captured more Anopheles than the light trap during the field observation period. Real-time data revealed distinct bimodal activity peaks at dawn and dusk. Among the 112 field-captured female An. sinensis, 21 (18.75%; exact 95% CI, 12.60%-26.97%) were blood-fed, indicating that blood-fed mosquitoes may occur in outdoor collections. Conclusions: The Black Box provides a highly efficient, automated solution for real-time malaria vector surveillance. By delivering accurate, timely ecological data, it supports dynamic risk assessment and enables targeted interventions, addressing key limitations of current control programs. Author summary: Monitoring malaria-vector mosquitoes is essential for directing control measures, but commonly used methods require substantial field labor, may expose collectors to mosquito bites, and often delay the availability of surveillance data. We developed the Black Box, an internet-connected mosquito trap that combines light, heat, chemical attractants, photocatalytic material, automated counting, and wireless data transmission. We evaluated the device under laboratory, semi-field, and field conditions. It captured three major malaria-vector species efficiently in controlled experiments and maintained close agreement between physical mosquito counts and records transmitted to the cloud platform. During field deployment, the device continuously recorded mosquito activity and revealed higher activity at night and peaks around dawn and dusk. These data show that an automated system can provide timely information on mosquito abundance and activity while reducing dependence on repeated manual counting. The current device cannot identify individual Anopheles species automatically, so captured mosquitoes still require morphological examination. Further development and testing across seasons and regions will be needed before species-level automated identification and wider operational use can be established.
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BibTeX
@article{han2026development,
title = {{Development and effectiveness test of an internet-based real-time monitoring device for malaria parasite vector mosquitoes}},
author = {Chenyu Han and Yiquan Cai and Huijuan Yang and Yiji Li and Xuejian Zhang and Xiaoming Wang and Yixuan Duan and Hanren Liu and Lei Zuo and Guiyun Yan and Xiao-Guang Chen},
journal = {PLOS Neglected Tropical Diseases},
year = {2026},
month = oct,
doi = {10.1371/journal.pntd.0014778},
url = {https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0014778},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Development and effectiveness test of an internet-based real-time monitoring device for malaria parasite vector mosquitoes
AU - Chenyu Han
AU - Yiquan Cai
AU - Huijuan Yang
AU - Yiji Li
AU - Xuejian Zhang
AU - Xiaoming Wang
AU - Yixuan Duan
AU - Hanren Liu
AU - Lei Zuo
AU - Guiyun Yan
AU - Xiao-Guang Chen
PY - 2026
DA - 2026/10/09
JO - PLOS Neglected Tropical Diseases
DO - 10.1371/journal.pntd.0014778
UR - https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0014778
AB - Background: Malaria remains a major global health threat, and effective vector surveillance is essential for timely vector control. However, surveillance systems are challenged by behavioral shifts in Anopheles mosquitoes, labor-intensive procedures, and delays in real-time data transmission. Existing electronic monitoring tools often suffer from low sensitivity and specificity, creating an urgent need for automated, high-precision alternatives. Methods: We developed the “Black Box,” a 3D-printed, internet-enabled device integrating multispectral light, thermal simulation, chemical attractants, and photocatalytic materials with automated counting and wireless data transmission. Laboratory trials demonstrated trapping rates of 94.00%–95.00% for Anopheles sinensis, An. stephensi, and An. anthropophagus, with data consistency exceeding 97.00%. Semi-field tests yielded trapping rates of 73.60%–90.80% and consistency above 92.00%. In field deployments, the device captured 5,109 mosquitoes with a 94.17% consistency rate and captured more Anopheles than the light trap during the field observation period. Real-time data revealed distinct bimodal activity peaks at dawn and dusk. Among the 112 field-captured female An. sinensis, 21 (18.75%; exact 95% CI, 12.60%-26.97%) were blood-fed, indicating that blood-fed mosquitoes may occur in outdoor collections. Conclusions: The Black Box provides a highly efficient, automated solution for real-time malaria vector surveillance. By delivering accurate, timely ecological data, it supports dynamic risk assessment and enables targeted interventions, addressing key limitations of current control programs. Author summary: Monitoring malaria-vector mosquitoes is essential for directing control measures, but commonly used methods require substantial field labor, may expose collectors to mosquito bites, and often delay the availability of surveillance data. We developed the Black Box, an internet-connected mosquito trap that combines light, heat, chemical attractants, photocatalytic material, automated counting, and wireless data transmission. We evaluated the device under laboratory, semi-field, and field conditions. It captured three major malaria-vector species efficiently in controlled experiments and maintained close agreement between physical mosquito counts and records transmitted to the cloud platform. During field deployment, the device continuously recorded mosquito activity and revealed higher activity at night and peaks around dawn and dusk. These data show that an automated system can provide timely information on mosquito abundance and activity while reducing dependence on repeated manual counting. The current device cannot identify individual Anopheles species automatically, so captured mosquitoes still require morphological examination. Further development and testing across seasons and regions will be needed before species-level automated identification and wider operational use can be established.
SN - 1935-2735
ER -