Introduction and Objectives The age structure and dynamics of mosquito populations are crucial for understanding their ability to spread diseases and assessing the effectiveness of anti-mosquito control measures. Available methods for estimating mosquito age are labour-intensive and imprecise. We developed a promising Supervised Machine Learning (SML) algorithm based on Mid-Infrared Spectrometry (MIRS) of semi-field reared Aedes albopictus specimens to predict the age of single adult females and males. We here present data on the validation of the accuracy of this approach on field samples and preliminary results on its application to assess the effectiveness of conventional and innovative control interventions. Materials and Methods Mid-Infrared spectra from 1,881 semi-field reared adults of known age were used to train the SLM algorithm and assess its accuracy when predicting mosquito age, either as a multilevel variable (with a resolution of 3, 6, and 9 day-long classes) or as a binary variable of epidemiological relevance (e.g. young vs older). The accuracy of the approach will be validated on field samples aged by morphological approaches. Samples collected in summer 2024 before and after pyrethroid treatments in Rome and during a SIT trial in Procida Island (Naples) will be processed by MIRS. Results, Discussion and Conclusion Results from semi-field females showed that the mean accuracy of SML-algorithm for 3-day age is 85% (78%-90%) for specimens <15-day old and 71% (52%-83%) for older ages. Mean accuracy increased to 85% (77-100%) when grouping females into 6-day long classes and to >96% when young (>3-day old) and older (>6-day old) females were compared. This high accuracy will be validated in the field by analysing the spectra of morphologically distinguished nulliparous and parous females. Results of collections carried out before and after anti-mosquito interventions will provide preliminary indications of the potential of MIRS approach in assessing their effectiveness.
How old are you? Assessing Aedes albopictus age by mid-infrared spectroscopy / Foti, Mattia; PAZMIÑO BETANCOURTH, Mauro; Micocci, Martina; Serini, Paola; Casas, Ivan; Salvemini, Marco; Caputo, Beniamino; Baldini, Francesco; DELLA TORRE, Alessandra. - (2024), pp. 122-122. (Intervento presentato al convegno ONE HEALTH IN ACTION: supporting and accelerating the bridging of the vertebrate and plant health communities tenutosi a Montpellier, France).
How old are you? Assessing Aedes albopictus age by mid-infrared spectroscopy
Mattia FOTI;Martina MICOCCI;Paola SERINI;Beniamino CAPUTO;Alessandra DELLA TORRE
2024
Abstract
Introduction and Objectives The age structure and dynamics of mosquito populations are crucial for understanding their ability to spread diseases and assessing the effectiveness of anti-mosquito control measures. Available methods for estimating mosquito age are labour-intensive and imprecise. We developed a promising Supervised Machine Learning (SML) algorithm based on Mid-Infrared Spectrometry (MIRS) of semi-field reared Aedes albopictus specimens to predict the age of single adult females and males. We here present data on the validation of the accuracy of this approach on field samples and preliminary results on its application to assess the effectiveness of conventional and innovative control interventions. Materials and Methods Mid-Infrared spectra from 1,881 semi-field reared adults of known age were used to train the SLM algorithm and assess its accuracy when predicting mosquito age, either as a multilevel variable (with a resolution of 3, 6, and 9 day-long classes) or as a binary variable of epidemiological relevance (e.g. young vs older). The accuracy of the approach will be validated on field samples aged by morphological approaches. Samples collected in summer 2024 before and after pyrethroid treatments in Rome and during a SIT trial in Procida Island (Naples) will be processed by MIRS. Results, Discussion and Conclusion Results from semi-field females showed that the mean accuracy of SML-algorithm for 3-day age is 85% (78%-90%) for specimens <15-day old and 71% (52%-83%) for older ages. Mean accuracy increased to 85% (77-100%) when grouping females into 6-day long classes and to >96% when young (>3-day old) and older (>6-day old) females were compared. This high accuracy will be validated in the field by analysing the spectra of morphologically distinguished nulliparous and parous females. Results of collections carried out before and after anti-mosquito interventions will provide preliminary indications of the potential of MIRS approach in assessing their effectiveness.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
