An AI-based gravitrap surveillance for spatial interaction analysis in predicting aedes risk
Abstract Background Dengue fever is transmitted to humans through bites of Aedes mosquito vectors. Therefore, controlling the Aedes population can decrease the incidence and block transmission of dengue fever and other diseases transmitted by these mosquito species. In many countries, gravitraps are...
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| Format: | Article |
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BMC
2025-08-01
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| Series: | International Journal of Health Geographics |
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| Online Access: | https://doi.org/10.1186/s12942-025-00403-z |
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| author | Hsiang-Yu Yuan Pei-Sheng Lin Wei-Liang Liu Tzai-Hung Wen Yu-Chun Lu Chun-Hong Chen Li‑Wei Chen |
| author_facet | Hsiang-Yu Yuan Pei-Sheng Lin Wei-Liang Liu Tzai-Hung Wen Yu-Chun Lu Chun-Hong Chen Li‑Wei Chen |
| author_sort | Hsiang-Yu Yuan |
| collection | DOAJ |
| description | Abstract Background Dengue fever is transmitted to humans through bites of Aedes mosquito vectors. Therefore, controlling the Aedes population can decrease the incidence and block transmission of dengue fever and other diseases transmitted by these mosquito species. In many countries, gravitraps are used to monitor mosquito vector densities, but this approach usually underestimates the population of Aedes mosquitoes. Moreover, literature on the spatio-temporal dynamics of Aedes populations in a single city is limited. For example, in Kaohsiung of Taiwan, population densities vary substantially between villages, and the city government has relatively limited resources to deploy gravitraps. Therefore, a well-defined index should be developed to reflect the spatial–temporal dynamics of adult Aedes mosquitoes in urban environments. This would allow reduction of sources and removal of vector habitats under various situations. Methods An artificial intelligence (AI) surveillance based on an auto-Markov model with a non-parametric permutation test is proposed. The auto-Markov model takes neighborhood effects into consideration, and can therefore adjust spatial–temporal risks dynamically in various seasons and environmental background. Information from neighboring villages is incorporated into the model to enhance precision of risk prediction. Results The proposed AI gravitrap index integrates the auto-Markov and disease mapping models to enhance sensitivity in risk prediction for Aedes densities. Simulation studies and cross-validation analysis indicated that the AI index could be more efficient than traditional indices in assessing risk levels. This means that using the AI index could also reduce allocation cost for gravitraps. Moreover, since the auto-Markov model accommodates spatial–temporal dependence, a risk map by the AI index could reflect spatial–temporal dynamics for Aedes densities more accurate. Conclusions The AI gravitrap index can dynamically update risk levels by the auto-Markov model with an unsupervised permutation test. The proposed index thus has flexibility to apply in various cities with different environmental background and weather conditions. Furthermore, a risk map by the AI index could provide guidance for policymakers to prevent dengue epidemics. |
| format | Article |
| id | doaj-art-88b0cbc85e49404dbc82eadd990277f9 |
| institution | DOAJ |
| issn | 1476-072X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | International Journal of Health Geographics |
| spelling | doaj-art-88b0cbc85e49404dbc82eadd990277f92025-08-20T03:06:04ZengBMCInternational Journal of Health Geographics1476-072X2025-08-0124111410.1186/s12942-025-00403-zAn AI-based gravitrap surveillance for spatial interaction analysis in predicting aedes riskHsiang-Yu Yuan0Pei-Sheng Lin1Wei-Liang Liu2Tzai-Hung Wen3Yu-Chun Lu4Chun-Hong Chen5Li‑Wei Chen6Department of Biomedical Sciences, City University of Hong Kong, College of BiomedicineInstitute of Population Health Sciences, National Health Research InstitutesNational Mosquito-Borne Diseases Control Research Center, National Health Research InstitutesDepartment of Geography, National Taiwan UniversityInstitute of Population Health Sciences, National Health Research InstitutesNational Mosquito-Borne Diseases Control Research Center, National Health Research InstitutesInstitute of Population Health Sciences, National Health Research InstitutesAbstract Background Dengue fever is transmitted to humans through bites of Aedes mosquito vectors. Therefore, controlling the Aedes population can decrease the incidence and block transmission of dengue fever and other diseases transmitted by these mosquito species. In many countries, gravitraps are used to monitor mosquito vector densities, but this approach usually underestimates the population of Aedes mosquitoes. Moreover, literature on the spatio-temporal dynamics of Aedes populations in a single city is limited. For example, in Kaohsiung of Taiwan, population densities vary substantially between villages, and the city government has relatively limited resources to deploy gravitraps. Therefore, a well-defined index should be developed to reflect the spatial–temporal dynamics of adult Aedes mosquitoes in urban environments. This would allow reduction of sources and removal of vector habitats under various situations. Methods An artificial intelligence (AI) surveillance based on an auto-Markov model with a non-parametric permutation test is proposed. The auto-Markov model takes neighborhood effects into consideration, and can therefore adjust spatial–temporal risks dynamically in various seasons and environmental background. Information from neighboring villages is incorporated into the model to enhance precision of risk prediction. Results The proposed AI gravitrap index integrates the auto-Markov and disease mapping models to enhance sensitivity in risk prediction for Aedes densities. Simulation studies and cross-validation analysis indicated that the AI index could be more efficient than traditional indices in assessing risk levels. This means that using the AI index could also reduce allocation cost for gravitraps. Moreover, since the auto-Markov model accommodates spatial–temporal dependence, a risk map by the AI index could reflect spatial–temporal dynamics for Aedes densities more accurate. Conclusions The AI gravitrap index can dynamically update risk levels by the auto-Markov model with an unsupervised permutation test. The proposed index thus has flexibility to apply in various cities with different environmental background and weather conditions. Furthermore, a risk map by the AI index could provide guidance for policymakers to prevent dengue epidemics.https://doi.org/10.1186/s12942-025-00403-zAedes indexAI methodAuto-Markov modelDengue preventionGravitrapSpatial–temporal patterns |
| spellingShingle | Hsiang-Yu Yuan Pei-Sheng Lin Wei-Liang Liu Tzai-Hung Wen Yu-Chun Lu Chun-Hong Chen Li‑Wei Chen An AI-based gravitrap surveillance for spatial interaction analysis in predicting aedes risk International Journal of Health Geographics Aedes index AI method Auto-Markov model Dengue prevention Gravitrap Spatial–temporal patterns |
| title | An AI-based gravitrap surveillance for spatial interaction analysis in predicting aedes risk |
| title_full | An AI-based gravitrap surveillance for spatial interaction analysis in predicting aedes risk |
| title_fullStr | An AI-based gravitrap surveillance for spatial interaction analysis in predicting aedes risk |
| title_full_unstemmed | An AI-based gravitrap surveillance for spatial interaction analysis in predicting aedes risk |
| title_short | An AI-based gravitrap surveillance for spatial interaction analysis in predicting aedes risk |
| title_sort | ai based gravitrap surveillance for spatial interaction analysis in predicting aedes risk |
| topic | Aedes index AI method Auto-Markov model Dengue prevention Gravitrap Spatial–temporal patterns |
| url | https://doi.org/10.1186/s12942-025-00403-z |
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