Neural network modelling for estimating linear and nonlinear influences of meteo-climatic variables on Sergentomyia minuta abundance using small datasets
In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we ap...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Pasini, Antonello [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Übergeordnetes Werk: |
Enthalten in: GLOMERULAR FEATURES OF CARDIO-RENAL SYNDROME, A CASE CONTROLLED STUDY - 2014, an international journal on ecoinformatics and computational ecology, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:56 ; year:2020 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.ecoinf.2020.101055 |
|---|
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| 520 | |a In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we apply a neural network model (specifically developed for modelling relationships among variables in small datasets) to estimate the population abundance of S. minuta starting from meteo-climatic variables only, during three capturing seasons (2014–2016) in an Italian site. The results show that we are able to explain a wide majority of the variance in the data of population density (R2 = 0.632). This is obtained through the application of a neural model driven in input by averaged mean temperature, relative humidity and temperature at 10 cm belowground during oviposition, larval and adult stages. A modelling pruning activity shows the major role of humidity in driving the number of captures, but also an important nonlinear role of temperature, which highlights the importance of possible heat waves on population density of S. minuta. | ||
| 520 | |a In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we apply a neural network model (specifically developed for modelling relationships among variables in small datasets) to estimate the population abundance of S. minuta starting from meteo-climatic variables only, during three capturing seasons (2014–2016) in an Italian site. The results show that we are able to explain a wide majority of the variance in the data of population density (R2 = 0.632). This is obtained through the application of a neural model driven in input by averaged mean temperature, relative humidity and temperature at 10 cm belowground during oviposition, larval and adult stages. A modelling pruning activity shows the major role of humidity in driving the number of captures, but also an important nonlinear role of temperature, which highlights the importance of possible heat waves on population density of S. minuta. | ||
| 700 | 1 | |a Amendola, Stefano |4 oth | |
| 700 | 1 | |a Giacomi, Angelo |4 oth | |
| 700 | 1 | |a Calderini, Pietro |4 oth | |
| 700 | 1 | |a Barlozzari, Giulia |4 oth | |
| 700 | 1 | |a Macrì, Gladia |4 oth | |
| 700 | 1 | |a Pombi, Marco |4 oth | |
| 700 | 1 | |a Gabrielli, Simona |4 oth | |
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10.1016/j.ecoinf.2020.101055 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000918.pica (DE-627)ELV049460072 (ELSEVIER)S1574-9541(20)30005-4 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 530 VZ 52.56 bkl Pasini, Antonello verfasserin aut Neural network modelling for estimating linear and nonlinear influences of meteo-climatic variables on Sergentomyia minuta abundance using small datasets 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we apply a neural network model (specifically developed for modelling relationships among variables in small datasets) to estimate the population abundance of S. minuta starting from meteo-climatic variables only, during three capturing seasons (2014–2016) in an Italian site. The results show that we are able to explain a wide majority of the variance in the data of population density (R2 = 0.632). This is obtained through the application of a neural model driven in input by averaged mean temperature, relative humidity and temperature at 10 cm belowground during oviposition, larval and adult stages. A modelling pruning activity shows the major role of humidity in driving the number of captures, but also an important nonlinear role of temperature, which highlights the importance of possible heat waves on population density of S. minuta. In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we apply a neural network model (specifically developed for modelling relationships among variables in small datasets) to estimate the population abundance of S. minuta starting from meteo-climatic variables only, during three capturing seasons (2014–2016) in an Italian site. The results show that we are able to explain a wide majority of the variance in the data of population density (R2 = 0.632). This is obtained through the application of a neural model driven in input by averaged mean temperature, relative humidity and temperature at 10 cm belowground during oviposition, larval and adult stages. A modelling pruning activity shows the major role of humidity in driving the number of captures, but also an important nonlinear role of temperature, which highlights the importance of possible heat waves on population density of S. minuta. Amendola, Stefano oth Giacomi, Angelo oth Calderini, Pietro oth Barlozzari, Giulia oth Macrì, Gladia oth Pombi, Marco oth Gabrielli, Simona oth Enthalten in Elsevier GLOMERULAR FEATURES OF CARDIO-RENAL SYNDROME, A CASE CONTROLLED STUDY 2014 an international journal on ecoinformatics and computational ecology Amsterdam [u.a.] (DE-627)ELV022626204 volume:56 year:2020 pages:0 https://doi.org/10.1016/j.ecoinf.2020.101055 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.56 Regenerative Energieformen alternative Energieformen VZ AR 56 2020 0 |
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10.1016/j.ecoinf.2020.101055 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000918.pica (DE-627)ELV049460072 (ELSEVIER)S1574-9541(20)30005-4 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 530 VZ 52.56 bkl Pasini, Antonello verfasserin aut Neural network modelling for estimating linear and nonlinear influences of meteo-climatic variables on Sergentomyia minuta abundance using small datasets 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we apply a neural network model (specifically developed for modelling relationships among variables in small datasets) to estimate the population abundance of S. minuta starting from meteo-climatic variables only, during three capturing seasons (2014–2016) in an Italian site. The results show that we are able to explain a wide majority of the variance in the data of population density (R2 = 0.632). This is obtained through the application of a neural model driven in input by averaged mean temperature, relative humidity and temperature at 10 cm belowground during oviposition, larval and adult stages. A modelling pruning activity shows the major role of humidity in driving the number of captures, but also an important nonlinear role of temperature, which highlights the importance of possible heat waves on population density of S. minuta. In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we apply a neural network model (specifically developed for modelling relationships among variables in small datasets) to estimate the population abundance of S. minuta starting from meteo-climatic variables only, during three capturing seasons (2014–2016) in an Italian site. The results show that we are able to explain a wide majority of the variance in the data of population density (R2 = 0.632). This is obtained through the application of a neural model driven in input by averaged mean temperature, relative humidity and temperature at 10 cm belowground during oviposition, larval and adult stages. A modelling pruning activity shows the major role of humidity in driving the number of captures, but also an important nonlinear role of temperature, which highlights the importance of possible heat waves on population density of S. minuta. Amendola, Stefano oth Giacomi, Angelo oth Calderini, Pietro oth Barlozzari, Giulia oth Macrì, Gladia oth Pombi, Marco oth Gabrielli, Simona oth Enthalten in Elsevier GLOMERULAR FEATURES OF CARDIO-RENAL SYNDROME, A CASE CONTROLLED STUDY 2014 an international journal on ecoinformatics and computational ecology Amsterdam [u.a.] (DE-627)ELV022626204 volume:56 year:2020 pages:0 https://doi.org/10.1016/j.ecoinf.2020.101055 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.56 Regenerative Energieformen alternative Energieformen VZ AR 56 2020 0 |
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10.1016/j.ecoinf.2020.101055 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000918.pica (DE-627)ELV049460072 (ELSEVIER)S1574-9541(20)30005-4 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 530 VZ 52.56 bkl Pasini, Antonello verfasserin aut Neural network modelling for estimating linear and nonlinear influences of meteo-climatic variables on Sergentomyia minuta abundance using small datasets 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we apply a neural network model (specifically developed for modelling relationships among variables in small datasets) to estimate the population abundance of S. minuta starting from meteo-climatic variables only, during three capturing seasons (2014–2016) in an Italian site. The results show that we are able to explain a wide majority of the variance in the data of population density (R2 = 0.632). This is obtained through the application of a neural model driven in input by averaged mean temperature, relative humidity and temperature at 10 cm belowground during oviposition, larval and adult stages. A modelling pruning activity shows the major role of humidity in driving the number of captures, but also an important nonlinear role of temperature, which highlights the importance of possible heat waves on population density of S. minuta. In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we apply a neural network model (specifically developed for modelling relationships among variables in small datasets) to estimate the population abundance of S. minuta starting from meteo-climatic variables only, during three capturing seasons (2014–2016) in an Italian site. The results show that we are able to explain a wide majority of the variance in the data of population density (R2 = 0.632). This is obtained through the application of a neural model driven in input by averaged mean temperature, relative humidity and temperature at 10 cm belowground during oviposition, larval and adult stages. A modelling pruning activity shows the major role of humidity in driving the number of captures, but also an important nonlinear role of temperature, which highlights the importance of possible heat waves on population density of S. minuta. Amendola, Stefano oth Giacomi, Angelo oth Calderini, Pietro oth Barlozzari, Giulia oth Macrì, Gladia oth Pombi, Marco oth Gabrielli, Simona oth Enthalten in Elsevier GLOMERULAR FEATURES OF CARDIO-RENAL SYNDROME, A CASE CONTROLLED STUDY 2014 an international journal on ecoinformatics and computational ecology Amsterdam [u.a.] (DE-627)ELV022626204 volume:56 year:2020 pages:0 https://doi.org/10.1016/j.ecoinf.2020.101055 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.56 Regenerative Energieformen alternative Energieformen VZ AR 56 2020 0 |
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10.1016/j.ecoinf.2020.101055 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000918.pica (DE-627)ELV049460072 (ELSEVIER)S1574-9541(20)30005-4 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 530 VZ 52.56 bkl Pasini, Antonello verfasserin aut Neural network modelling for estimating linear and nonlinear influences of meteo-climatic variables on Sergentomyia minuta abundance using small datasets 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we apply a neural network model (specifically developed for modelling relationships among variables in small datasets) to estimate the population abundance of S. minuta starting from meteo-climatic variables only, during three capturing seasons (2014–2016) in an Italian site. The results show that we are able to explain a wide majority of the variance in the data of population density (R2 = 0.632). This is obtained through the application of a neural model driven in input by averaged mean temperature, relative humidity and temperature at 10 cm belowground during oviposition, larval and adult stages. A modelling pruning activity shows the major role of humidity in driving the number of captures, but also an important nonlinear role of temperature, which highlights the importance of possible heat waves on population density of S. minuta. In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we apply a neural network model (specifically developed for modelling relationships among variables in small datasets) to estimate the population abundance of S. minuta starting from meteo-climatic variables only, during three capturing seasons (2014–2016) in an Italian site. The results show that we are able to explain a wide majority of the variance in the data of population density (R2 = 0.632). This is obtained through the application of a neural model driven in input by averaged mean temperature, relative humidity and temperature at 10 cm belowground during oviposition, larval and adult stages. A modelling pruning activity shows the major role of humidity in driving the number of captures, but also an important nonlinear role of temperature, which highlights the importance of possible heat waves on population density of S. minuta. Amendola, Stefano oth Giacomi, Angelo oth Calderini, Pietro oth Barlozzari, Giulia oth Macrì, Gladia oth Pombi, Marco oth Gabrielli, Simona oth Enthalten in Elsevier GLOMERULAR FEATURES OF CARDIO-RENAL SYNDROME, A CASE CONTROLLED STUDY 2014 an international journal on ecoinformatics and computational ecology Amsterdam [u.a.] (DE-627)ELV022626204 volume:56 year:2020 pages:0 https://doi.org/10.1016/j.ecoinf.2020.101055 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.56 Regenerative Energieformen alternative Energieformen VZ AR 56 2020 0 |
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10.1016/j.ecoinf.2020.101055 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000918.pica (DE-627)ELV049460072 (ELSEVIER)S1574-9541(20)30005-4 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 530 VZ 52.56 bkl Pasini, Antonello verfasserin aut Neural network modelling for estimating linear and nonlinear influences of meteo-climatic variables on Sergentomyia minuta abundance using small datasets 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we apply a neural network model (specifically developed for modelling relationships among variables in small datasets) to estimate the population abundance of S. minuta starting from meteo-climatic variables only, during three capturing seasons (2014–2016) in an Italian site. The results show that we are able to explain a wide majority of the variance in the data of population density (R2 = 0.632). This is obtained through the application of a neural model driven in input by averaged mean temperature, relative humidity and temperature at 10 cm belowground during oviposition, larval and adult stages. A modelling pruning activity shows the major role of humidity in driving the number of captures, but also an important nonlinear role of temperature, which highlights the importance of possible heat waves on population density of S. minuta. In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we apply a neural network model (specifically developed for modelling relationships among variables in small datasets) to estimate the population abundance of S. minuta starting from meteo-climatic variables only, during three capturing seasons (2014–2016) in an Italian site. The results show that we are able to explain a wide majority of the variance in the data of population density (R2 = 0.632). This is obtained through the application of a neural model driven in input by averaged mean temperature, relative humidity and temperature at 10 cm belowground during oviposition, larval and adult stages. A modelling pruning activity shows the major role of humidity in driving the number of captures, but also an important nonlinear role of temperature, which highlights the importance of possible heat waves on population density of S. minuta. Amendola, Stefano oth Giacomi, Angelo oth Calderini, Pietro oth Barlozzari, Giulia oth Macrì, Gladia oth Pombi, Marco oth Gabrielli, Simona oth Enthalten in Elsevier GLOMERULAR FEATURES OF CARDIO-RENAL SYNDROME, A CASE CONTROLLED STUDY 2014 an international journal on ecoinformatics and computational ecology Amsterdam [u.a.] (DE-627)ELV022626204 volume:56 year:2020 pages:0 https://doi.org/10.1016/j.ecoinf.2020.101055 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.56 Regenerative Energieformen alternative Energieformen VZ AR 56 2020 0 |
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GLOMERULAR FEATURES OF CARDIO-RENAL SYNDROME, A CASE CONTROLLED STUDY |
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neural network modelling for estimating linear and nonlinear influences of meteo-climatic variables on sergentomyia minuta abundance using small datasets |
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Neural network modelling for estimating linear and nonlinear influences of meteo-climatic variables on Sergentomyia minuta abundance using small datasets |
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In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we apply a neural network model (specifically developed for modelling relationships among variables in small datasets) to estimate the population abundance of S. minuta starting from meteo-climatic variables only, during three capturing seasons (2014–2016) in an Italian site. The results show that we are able to explain a wide majority of the variance in the data of population density (R2 = 0.632). This is obtained through the application of a neural model driven in input by averaged mean temperature, relative humidity and temperature at 10 cm belowground during oviposition, larval and adult stages. A modelling pruning activity shows the major role of humidity in driving the number of captures, but also an important nonlinear role of temperature, which highlights the importance of possible heat waves on population density of S. minuta. |
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In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we apply a neural network model (specifically developed for modelling relationships among variables in small datasets) to estimate the population abundance of S. minuta starting from meteo-climatic variables only, during three capturing seasons (2014–2016) in an Italian site. The results show that we are able to explain a wide majority of the variance in the data of population density (R2 = 0.632). This is obtained through the application of a neural model driven in input by averaged mean temperature, relative humidity and temperature at 10 cm belowground during oviposition, larval and adult stages. A modelling pruning activity shows the major role of humidity in driving the number of captures, but also an important nonlinear role of temperature, which highlights the importance of possible heat waves on population density of S. minuta. |
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In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we apply a neural network model (specifically developed for modelling relationships among variables in small datasets) to estimate the population abundance of S. minuta starting from meteo-climatic variables only, during three capturing seasons (2014–2016) in an Italian site. The results show that we are able to explain a wide majority of the variance in the data of population density (R2 = 0.632). This is obtained through the application of a neural model driven in input by averaged mean temperature, relative humidity and temperature at 10 cm belowground during oviposition, larval and adult stages. A modelling pruning activity shows the major role of humidity in driving the number of captures, but also an important nonlinear role of temperature, which highlights the importance of possible heat waves on population density of S. minuta. |
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Neural network modelling for estimating linear and nonlinear influences of meteo-climatic variables on Sergentomyia minuta abundance using small datasets |
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Amendola, Stefano Giacomi, Angelo Calderini, Pietro Barlozzari, Giulia Macrì, Gladia Pombi, Marco Gabrielli, Simona |
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