Modelling the dynamic basic reproduction number of dengue based on MOI of Aedes albopictus derived from a multi-site field investigation in Guangzhou, a subtropical region
Background More than half of the global population lives in areas at risk of dengue (DENV) transmission. Developing an efficient risk prediction system can help curb dengue outbreaks, but multiple variables, including mosquito-based surveillance indicators, still constrain our understanding. Mosquit...
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| Autor*in: |
Guo, Xiang [verfasserIn] Li, Li [verfasserIn] Ren, Wenwen [verfasserIn] Hu, Minling [verfasserIn] Li, Ziyao [verfasserIn] Zeng, Shu [verfasserIn] Liu, Xiaohua [verfasserIn] Wang, Yuji [verfasserIn] Xie, Tian [verfasserIn] Yin, Qingqing [verfasserIn] Wei, Yuehong [verfasserIn] Luo, Lei [verfasserIn] Shi, Benyun [verfasserIn] Wang, Chunmei [verfasserIn] Wu, Rangke [verfasserIn] Yang, Zhicong [verfasserIn] Chen, Xiao-Guang [verfasserIn] Zhou, Xiaohong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2024 |
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| Übergeordnetes Werk: |
Enthalten in: Parasites & vectors - BioMed Central, 2008, 17(2024), 1 vom: 21. Feb. |
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| Übergeordnetes Werk: |
volume:17 ; year:2024 ; number:1 ; day:21 ; month:02 |
| Links: |
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| DOI / URN: |
10.1186/s13071-024-06121-y |
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| Katalog-ID: |
SPR054858178 |
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| 245 | 1 | 0 | |a Modelling the dynamic basic reproduction number of dengue based on MOI of Aedes albopictus derived from a multi-site field investigation in Guangzhou, a subtropical region |
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| 520 | |a Background More than half of the global population lives in areas at risk of dengue (DENV) transmission. Developing an efficient risk prediction system can help curb dengue outbreaks, but multiple variables, including mosquito-based surveillance indicators, still constrain our understanding. Mosquito or oviposition positive index (MOI) has been utilized in field surveillance to monitor the wild population density of Aedes albopictus in Guangzhou since 2005. Methods Based on the mosquito surveillance data using Mosq-ovitrap collection and human landing collection (HLC) launched at 12 sites in Guangzhou from 2015 to 2017, we established a MOI-based model of the basic dengue reproduction number (R0) using the classical Ross-Macdonald framework combined with a linear mixed-effects model. Results During the survey period, the mean MOI and adult mosquito density index (ADI) using HLC for Ae. albopictus were 12.96 ± 17.78 and 16.79 ± 55.92, respectively. The R0 estimated from the daily ADI ($ ADI_{D} $) showed a significant seasonal variation. A 10-unit increase in MOI was associated with 1.08-fold (95% CI 1.05, 1.11) $ ADI_{D} $ and an increase of 0.14 (95% CI 0.05, 0.23) in the logarithmic transformation of R0. MOI-based R0 of dengue varied by month and average monthly temperature. During the active period of Ae. albopictus from April to November in Guangzhou region, a high risk of dengue outbreak was predicted by the MOI-based R0 model, especially from August to October, with the predicted R0 > 1. Meanwhile, from December to March, the estimates of MOI-based R0 were < 1. Conclusions The present study enriched our knowledge about mosquito-based surveillance indicators and indicated that the MOI of Ae. albopictus could be valuable for application in estimating the R0 of dengue using a statistical model. The MOI-based R0 model prediction of the risk of dengue transmission varied by month and temperature in Guangzhou. Our findings lay a foundation for further development of a complex efficient dengue risk prediction system. Graphical Abstract | ||
| 650 | 4 | |a Dengue |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Surveillance system |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Basic reproduction number |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Li, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Ren, Wenwen |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Minling |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Ziyao |e verfasserin |4 aut | |
| 700 | 1 | |a Zeng, Shu |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Xiaohua |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yuji |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Tian |e verfasserin |4 aut | |
| 700 | 1 | |a Yin, Qingqing |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Yuehong |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Shi, Benyun |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Chunmei |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Rangke |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Zhicong |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Xiao-Guang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Xiaohong |e verfasserin |4 aut | |
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10.1186/s13071-024-06121-y doi (DE-627)SPR054858178 (SPR)s13071-024-06121-y-e DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid Guo, Xiang verfasserin aut Modelling the dynamic basic reproduction number of dengue based on MOI of Aedes albopictus derived from a multi-site field investigation in Guangzhou, a subtropical region 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background More than half of the global population lives in areas at risk of dengue (DENV) transmission. Developing an efficient risk prediction system can help curb dengue outbreaks, but multiple variables, including mosquito-based surveillance indicators, still constrain our understanding. Mosquito or oviposition positive index (MOI) has been utilized in field surveillance to monitor the wild population density of Aedes albopictus in Guangzhou since 2005. Methods Based on the mosquito surveillance data using Mosq-ovitrap collection and human landing collection (HLC) launched at 12 sites in Guangzhou from 2015 to 2017, we established a MOI-based model of the basic dengue reproduction number (R0) using the classical Ross-Macdonald framework combined with a linear mixed-effects model. Results During the survey period, the mean MOI and adult mosquito density index (ADI) using HLC for Ae. albopictus were 12.96 ± 17.78 and 16.79 ± 55.92, respectively. The R0 estimated from the daily ADI ($ ADI_{D} $) showed a significant seasonal variation. A 10-unit increase in MOI was associated with 1.08-fold (95% CI 1.05, 1.11) $ ADI_{D} $ and an increase of 0.14 (95% CI 0.05, 0.23) in the logarithmic transformation of R0. MOI-based R0 of dengue varied by month and average monthly temperature. During the active period of Ae. albopictus from April to November in Guangzhou region, a high risk of dengue outbreak was predicted by the MOI-based R0 model, especially from August to October, with the predicted R0 > 1. Meanwhile, from December to March, the estimates of MOI-based R0 were < 1. Conclusions The present study enriched our knowledge about mosquito-based surveillance indicators and indicated that the MOI of Ae. albopictus could be valuable for application in estimating the R0 of dengue using a statistical model. The MOI-based R0 model prediction of the risk of dengue transmission varied by month and temperature in Guangzhou. Our findings lay a foundation for further development of a complex efficient dengue risk prediction system. Graphical Abstract Dengue (dpeaa)DE-He213 Surveillance system (dpeaa)DE-He213 Basic reproduction number (dpeaa)DE-He213 Li, Li verfasserin aut Ren, Wenwen verfasserin aut Hu, Minling verfasserin aut Li, Ziyao verfasserin aut Zeng, Shu verfasserin aut Liu, Xiaohua verfasserin aut Wang, Yuji verfasserin aut Xie, Tian verfasserin aut Yin, Qingqing verfasserin aut Wei, Yuehong verfasserin aut Luo, Lei verfasserin aut Shi, Benyun verfasserin aut Wang, Chunmei verfasserin aut Wu, Rangke verfasserin aut Yang, Zhicong verfasserin aut Chen, Xiao-Guang verfasserin aut Zhou, Xiaohong verfasserin aut Enthalten in Parasites & vectors BioMed Central, 2008 17(2024), 1 vom: 21. Feb. (DE-627)558690076 (DE-600)2409480-8 1756-3305 nnns volume:17 year:2024 number:1 day:21 month:02 https://dx.doi.org/10.1186/s13071-024-06121-y X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 1 21 02 |
| spelling |
10.1186/s13071-024-06121-y doi (DE-627)SPR054858178 (SPR)s13071-024-06121-y-e DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid Guo, Xiang verfasserin aut Modelling the dynamic basic reproduction number of dengue based on MOI of Aedes albopictus derived from a multi-site field investigation in Guangzhou, a subtropical region 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background More than half of the global population lives in areas at risk of dengue (DENV) transmission. Developing an efficient risk prediction system can help curb dengue outbreaks, but multiple variables, including mosquito-based surveillance indicators, still constrain our understanding. Mosquito or oviposition positive index (MOI) has been utilized in field surveillance to monitor the wild population density of Aedes albopictus in Guangzhou since 2005. Methods Based on the mosquito surveillance data using Mosq-ovitrap collection and human landing collection (HLC) launched at 12 sites in Guangzhou from 2015 to 2017, we established a MOI-based model of the basic dengue reproduction number (R0) using the classical Ross-Macdonald framework combined with a linear mixed-effects model. Results During the survey period, the mean MOI and adult mosquito density index (ADI) using HLC for Ae. albopictus were 12.96 ± 17.78 and 16.79 ± 55.92, respectively. The R0 estimated from the daily ADI ($ ADI_{D} $) showed a significant seasonal variation. A 10-unit increase in MOI was associated with 1.08-fold (95% CI 1.05, 1.11) $ ADI_{D} $ and an increase of 0.14 (95% CI 0.05, 0.23) in the logarithmic transformation of R0. MOI-based R0 of dengue varied by month and average monthly temperature. During the active period of Ae. albopictus from April to November in Guangzhou region, a high risk of dengue outbreak was predicted by the MOI-based R0 model, especially from August to October, with the predicted R0 > 1. Meanwhile, from December to March, the estimates of MOI-based R0 were < 1. Conclusions The present study enriched our knowledge about mosquito-based surveillance indicators and indicated that the MOI of Ae. albopictus could be valuable for application in estimating the R0 of dengue using a statistical model. The MOI-based R0 model prediction of the risk of dengue transmission varied by month and temperature in Guangzhou. Our findings lay a foundation for further development of a complex efficient dengue risk prediction system. Graphical Abstract Dengue (dpeaa)DE-He213 Surveillance system (dpeaa)DE-He213 Basic reproduction number (dpeaa)DE-He213 Li, Li verfasserin aut Ren, Wenwen verfasserin aut Hu, Minling verfasserin aut Li, Ziyao verfasserin aut Zeng, Shu verfasserin aut Liu, Xiaohua verfasserin aut Wang, Yuji verfasserin aut Xie, Tian verfasserin aut Yin, Qingqing verfasserin aut Wei, Yuehong verfasserin aut Luo, Lei verfasserin aut Shi, Benyun verfasserin aut Wang, Chunmei verfasserin aut Wu, Rangke verfasserin aut Yang, Zhicong verfasserin aut Chen, Xiao-Guang verfasserin aut Zhou, Xiaohong verfasserin aut Enthalten in Parasites & vectors BioMed Central, 2008 17(2024), 1 vom: 21. Feb. (DE-627)558690076 (DE-600)2409480-8 1756-3305 nnns volume:17 year:2024 number:1 day:21 month:02 https://dx.doi.org/10.1186/s13071-024-06121-y X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 1 21 02 |
| allfields_unstemmed |
10.1186/s13071-024-06121-y doi (DE-627)SPR054858178 (SPR)s13071-024-06121-y-e DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid Guo, Xiang verfasserin aut Modelling the dynamic basic reproduction number of dengue based on MOI of Aedes albopictus derived from a multi-site field investigation in Guangzhou, a subtropical region 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background More than half of the global population lives in areas at risk of dengue (DENV) transmission. Developing an efficient risk prediction system can help curb dengue outbreaks, but multiple variables, including mosquito-based surveillance indicators, still constrain our understanding. Mosquito or oviposition positive index (MOI) has been utilized in field surveillance to monitor the wild population density of Aedes albopictus in Guangzhou since 2005. Methods Based on the mosquito surveillance data using Mosq-ovitrap collection and human landing collection (HLC) launched at 12 sites in Guangzhou from 2015 to 2017, we established a MOI-based model of the basic dengue reproduction number (R0) using the classical Ross-Macdonald framework combined with a linear mixed-effects model. Results During the survey period, the mean MOI and adult mosquito density index (ADI) using HLC for Ae. albopictus were 12.96 ± 17.78 and 16.79 ± 55.92, respectively. The R0 estimated from the daily ADI ($ ADI_{D} $) showed a significant seasonal variation. A 10-unit increase in MOI was associated with 1.08-fold (95% CI 1.05, 1.11) $ ADI_{D} $ and an increase of 0.14 (95% CI 0.05, 0.23) in the logarithmic transformation of R0. MOI-based R0 of dengue varied by month and average monthly temperature. During the active period of Ae. albopictus from April to November in Guangzhou region, a high risk of dengue outbreak was predicted by the MOI-based R0 model, especially from August to October, with the predicted R0 > 1. Meanwhile, from December to March, the estimates of MOI-based R0 were < 1. Conclusions The present study enriched our knowledge about mosquito-based surveillance indicators and indicated that the MOI of Ae. albopictus could be valuable for application in estimating the R0 of dengue using a statistical model. The MOI-based R0 model prediction of the risk of dengue transmission varied by month and temperature in Guangzhou. Our findings lay a foundation for further development of a complex efficient dengue risk prediction system. Graphical Abstract Dengue (dpeaa)DE-He213 Surveillance system (dpeaa)DE-He213 Basic reproduction number (dpeaa)DE-He213 Li, Li verfasserin aut Ren, Wenwen verfasserin aut Hu, Minling verfasserin aut Li, Ziyao verfasserin aut Zeng, Shu verfasserin aut Liu, Xiaohua verfasserin aut Wang, Yuji verfasserin aut Xie, Tian verfasserin aut Yin, Qingqing verfasserin aut Wei, Yuehong verfasserin aut Luo, Lei verfasserin aut Shi, Benyun verfasserin aut Wang, Chunmei verfasserin aut Wu, Rangke verfasserin aut Yang, Zhicong verfasserin aut Chen, Xiao-Guang verfasserin aut Zhou, Xiaohong verfasserin aut Enthalten in Parasites & vectors BioMed Central, 2008 17(2024), 1 vom: 21. Feb. (DE-627)558690076 (DE-600)2409480-8 1756-3305 nnns volume:17 year:2024 number:1 day:21 month:02 https://dx.doi.org/10.1186/s13071-024-06121-y X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 1 21 02 |
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10.1186/s13071-024-06121-y doi (DE-627)SPR054858178 (SPR)s13071-024-06121-y-e DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid Guo, Xiang verfasserin aut Modelling the dynamic basic reproduction number of dengue based on MOI of Aedes albopictus derived from a multi-site field investigation in Guangzhou, a subtropical region 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background More than half of the global population lives in areas at risk of dengue (DENV) transmission. Developing an efficient risk prediction system can help curb dengue outbreaks, but multiple variables, including mosquito-based surveillance indicators, still constrain our understanding. Mosquito or oviposition positive index (MOI) has been utilized in field surveillance to monitor the wild population density of Aedes albopictus in Guangzhou since 2005. Methods Based on the mosquito surveillance data using Mosq-ovitrap collection and human landing collection (HLC) launched at 12 sites in Guangzhou from 2015 to 2017, we established a MOI-based model of the basic dengue reproduction number (R0) using the classical Ross-Macdonald framework combined with a linear mixed-effects model. Results During the survey period, the mean MOI and adult mosquito density index (ADI) using HLC for Ae. albopictus were 12.96 ± 17.78 and 16.79 ± 55.92, respectively. The R0 estimated from the daily ADI ($ ADI_{D} $) showed a significant seasonal variation. A 10-unit increase in MOI was associated with 1.08-fold (95% CI 1.05, 1.11) $ ADI_{D} $ and an increase of 0.14 (95% CI 0.05, 0.23) in the logarithmic transformation of R0. MOI-based R0 of dengue varied by month and average monthly temperature. During the active period of Ae. albopictus from April to November in Guangzhou region, a high risk of dengue outbreak was predicted by the MOI-based R0 model, especially from August to October, with the predicted R0 > 1. Meanwhile, from December to March, the estimates of MOI-based R0 were < 1. Conclusions The present study enriched our knowledge about mosquito-based surveillance indicators and indicated that the MOI of Ae. albopictus could be valuable for application in estimating the R0 of dengue using a statistical model. The MOI-based R0 model prediction of the risk of dengue transmission varied by month and temperature in Guangzhou. Our findings lay a foundation for further development of a complex efficient dengue risk prediction system. Graphical Abstract Dengue (dpeaa)DE-He213 Surveillance system (dpeaa)DE-He213 Basic reproduction number (dpeaa)DE-He213 Li, Li verfasserin aut Ren, Wenwen verfasserin aut Hu, Minling verfasserin aut Li, Ziyao verfasserin aut Zeng, Shu verfasserin aut Liu, Xiaohua verfasserin aut Wang, Yuji verfasserin aut Xie, Tian verfasserin aut Yin, Qingqing verfasserin aut Wei, Yuehong verfasserin aut Luo, Lei verfasserin aut Shi, Benyun verfasserin aut Wang, Chunmei verfasserin aut Wu, Rangke verfasserin aut Yang, Zhicong verfasserin aut Chen, Xiao-Guang verfasserin aut Zhou, Xiaohong verfasserin aut Enthalten in Parasites & vectors BioMed Central, 2008 17(2024), 1 vom: 21. Feb. (DE-627)558690076 (DE-600)2409480-8 1756-3305 nnns volume:17 year:2024 number:1 day:21 month:02 https://dx.doi.org/10.1186/s13071-024-06121-y X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 1 21 02 |
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10.1186/s13071-024-06121-y doi (DE-627)SPR054858178 (SPR)s13071-024-06121-y-e DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid Guo, Xiang verfasserin aut Modelling the dynamic basic reproduction number of dengue based on MOI of Aedes albopictus derived from a multi-site field investigation in Guangzhou, a subtropical region 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background More than half of the global population lives in areas at risk of dengue (DENV) transmission. Developing an efficient risk prediction system can help curb dengue outbreaks, but multiple variables, including mosquito-based surveillance indicators, still constrain our understanding. Mosquito or oviposition positive index (MOI) has been utilized in field surveillance to monitor the wild population density of Aedes albopictus in Guangzhou since 2005. Methods Based on the mosquito surveillance data using Mosq-ovitrap collection and human landing collection (HLC) launched at 12 sites in Guangzhou from 2015 to 2017, we established a MOI-based model of the basic dengue reproduction number (R0) using the classical Ross-Macdonald framework combined with a linear mixed-effects model. Results During the survey period, the mean MOI and adult mosquito density index (ADI) using HLC for Ae. albopictus were 12.96 ± 17.78 and 16.79 ± 55.92, respectively. The R0 estimated from the daily ADI ($ ADI_{D} $) showed a significant seasonal variation. A 10-unit increase in MOI was associated with 1.08-fold (95% CI 1.05, 1.11) $ ADI_{D} $ and an increase of 0.14 (95% CI 0.05, 0.23) in the logarithmic transformation of R0. MOI-based R0 of dengue varied by month and average monthly temperature. During the active period of Ae. albopictus from April to November in Guangzhou region, a high risk of dengue outbreak was predicted by the MOI-based R0 model, especially from August to October, with the predicted R0 > 1. Meanwhile, from December to March, the estimates of MOI-based R0 were < 1. Conclusions The present study enriched our knowledge about mosquito-based surveillance indicators and indicated that the MOI of Ae. albopictus could be valuable for application in estimating the R0 of dengue using a statistical model. The MOI-based R0 model prediction of the risk of dengue transmission varied by month and temperature in Guangzhou. Our findings lay a foundation for further development of a complex efficient dengue risk prediction system. Graphical Abstract Dengue (dpeaa)DE-He213 Surveillance system (dpeaa)DE-He213 Basic reproduction number (dpeaa)DE-He213 Li, Li verfasserin aut Ren, Wenwen verfasserin aut Hu, Minling verfasserin aut Li, Ziyao verfasserin aut Zeng, Shu verfasserin aut Liu, Xiaohua verfasserin aut Wang, Yuji verfasserin aut Xie, Tian verfasserin aut Yin, Qingqing verfasserin aut Wei, Yuehong verfasserin aut Luo, Lei verfasserin aut Shi, Benyun verfasserin aut Wang, Chunmei verfasserin aut Wu, Rangke verfasserin aut Yang, Zhicong verfasserin aut Chen, Xiao-Guang verfasserin aut Zhou, Xiaohong verfasserin aut Enthalten in Parasites & vectors BioMed Central, 2008 17(2024), 1 vom: 21. Feb. (DE-627)558690076 (DE-600)2409480-8 1756-3305 nnns volume:17 year:2024 number:1 day:21 month:02 https://dx.doi.org/10.1186/s13071-024-06121-y X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 1 21 02 |
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Enthalten in Parasites & vectors 17(2024), 1 vom: 21. Feb. volume:17 year:2024 number:1 day:21 month:02 |
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Modelling the dynamic basic reproduction number of dengue based on MOI of Aedes albopictus derived from a multi-site field investigation in Guangzhou, a subtropical region |
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Modelling the dynamic basic reproduction number of dengue based on MOI of Aedes albopictus derived from a multi-site field investigation in Guangzhou, a subtropical region |
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Guo, Xiang Li, Li Ren, Wenwen Hu, Minling Li, Ziyao Zeng, Shu Liu, Xiaohua Wang, Yuji Xie, Tian Yin, Qingqing Wei, Yuehong Luo, Lei Shi, Benyun Wang, Chunmei Wu, Rangke Yang, Zhicong Chen, Xiao-Guang Zhou, Xiaohong |
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modelling the dynamic basic reproduction number of dengue based on moi of aedes albopictus derived from a multi-site field investigation in guangzhou, a subtropical region |
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Modelling the dynamic basic reproduction number of dengue based on MOI of Aedes albopictus derived from a multi-site field investigation in Guangzhou, a subtropical region |
| abstract |
Background More than half of the global population lives in areas at risk of dengue (DENV) transmission. Developing an efficient risk prediction system can help curb dengue outbreaks, but multiple variables, including mosquito-based surveillance indicators, still constrain our understanding. Mosquito or oviposition positive index (MOI) has been utilized in field surveillance to monitor the wild population density of Aedes albopictus in Guangzhou since 2005. Methods Based on the mosquito surveillance data using Mosq-ovitrap collection and human landing collection (HLC) launched at 12 sites in Guangzhou from 2015 to 2017, we established a MOI-based model of the basic dengue reproduction number (R0) using the classical Ross-Macdonald framework combined with a linear mixed-effects model. Results During the survey period, the mean MOI and adult mosquito density index (ADI) using HLC for Ae. albopictus were 12.96 ± 17.78 and 16.79 ± 55.92, respectively. The R0 estimated from the daily ADI ($ ADI_{D} $) showed a significant seasonal variation. A 10-unit increase in MOI was associated with 1.08-fold (95% CI 1.05, 1.11) $ ADI_{D} $ and an increase of 0.14 (95% CI 0.05, 0.23) in the logarithmic transformation of R0. MOI-based R0 of dengue varied by month and average monthly temperature. During the active period of Ae. albopictus from April to November in Guangzhou region, a high risk of dengue outbreak was predicted by the MOI-based R0 model, especially from August to October, with the predicted R0 > 1. Meanwhile, from December to March, the estimates of MOI-based R0 were < 1. Conclusions The present study enriched our knowledge about mosquito-based surveillance indicators and indicated that the MOI of Ae. albopictus could be valuable for application in estimating the R0 of dengue using a statistical model. The MOI-based R0 model prediction of the risk of dengue transmission varied by month and temperature in Guangzhou. Our findings lay a foundation for further development of a complex efficient dengue risk prediction system. Graphical Abstract © The Author(s) 2024 |
| abstractGer |
Background More than half of the global population lives in areas at risk of dengue (DENV) transmission. Developing an efficient risk prediction system can help curb dengue outbreaks, but multiple variables, including mosquito-based surveillance indicators, still constrain our understanding. Mosquito or oviposition positive index (MOI) has been utilized in field surveillance to monitor the wild population density of Aedes albopictus in Guangzhou since 2005. Methods Based on the mosquito surveillance data using Mosq-ovitrap collection and human landing collection (HLC) launched at 12 sites in Guangzhou from 2015 to 2017, we established a MOI-based model of the basic dengue reproduction number (R0) using the classical Ross-Macdonald framework combined with a linear mixed-effects model. Results During the survey period, the mean MOI and adult mosquito density index (ADI) using HLC for Ae. albopictus were 12.96 ± 17.78 and 16.79 ± 55.92, respectively. The R0 estimated from the daily ADI ($ ADI_{D} $) showed a significant seasonal variation. A 10-unit increase in MOI was associated with 1.08-fold (95% CI 1.05, 1.11) $ ADI_{D} $ and an increase of 0.14 (95% CI 0.05, 0.23) in the logarithmic transformation of R0. MOI-based R0 of dengue varied by month and average monthly temperature. During the active period of Ae. albopictus from April to November in Guangzhou region, a high risk of dengue outbreak was predicted by the MOI-based R0 model, especially from August to October, with the predicted R0 > 1. Meanwhile, from December to March, the estimates of MOI-based R0 were < 1. Conclusions The present study enriched our knowledge about mosquito-based surveillance indicators and indicated that the MOI of Ae. albopictus could be valuable for application in estimating the R0 of dengue using a statistical model. The MOI-based R0 model prediction of the risk of dengue transmission varied by month and temperature in Guangzhou. Our findings lay a foundation for further development of a complex efficient dengue risk prediction system. Graphical Abstract © The Author(s) 2024 |
| abstract_unstemmed |
Background More than half of the global population lives in areas at risk of dengue (DENV) transmission. Developing an efficient risk prediction system can help curb dengue outbreaks, but multiple variables, including mosquito-based surveillance indicators, still constrain our understanding. Mosquito or oviposition positive index (MOI) has been utilized in field surveillance to monitor the wild population density of Aedes albopictus in Guangzhou since 2005. Methods Based on the mosquito surveillance data using Mosq-ovitrap collection and human landing collection (HLC) launched at 12 sites in Guangzhou from 2015 to 2017, we established a MOI-based model of the basic dengue reproduction number (R0) using the classical Ross-Macdonald framework combined with a linear mixed-effects model. Results During the survey period, the mean MOI and adult mosquito density index (ADI) using HLC for Ae. albopictus were 12.96 ± 17.78 and 16.79 ± 55.92, respectively. The R0 estimated from the daily ADI ($ ADI_{D} $) showed a significant seasonal variation. A 10-unit increase in MOI was associated with 1.08-fold (95% CI 1.05, 1.11) $ ADI_{D} $ and an increase of 0.14 (95% CI 0.05, 0.23) in the logarithmic transformation of R0. MOI-based R0 of dengue varied by month and average monthly temperature. During the active period of Ae. albopictus from April to November in Guangzhou region, a high risk of dengue outbreak was predicted by the MOI-based R0 model, especially from August to October, with the predicted R0 > 1. Meanwhile, from December to March, the estimates of MOI-based R0 were < 1. Conclusions The present study enriched our knowledge about mosquito-based surveillance indicators and indicated that the MOI of Ae. albopictus could be valuable for application in estimating the R0 of dengue using a statistical model. The MOI-based R0 model prediction of the risk of dengue transmission varied by month and temperature in Guangzhou. Our findings lay a foundation for further development of a complex efficient dengue risk prediction system. Graphical Abstract © The Author(s) 2024 |
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Modelling the dynamic basic reproduction number of dengue based on MOI of Aedes albopictus derived from a multi-site field investigation in Guangzhou, a subtropical region |
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Li, Li Ren, Wenwen Hu, Minling Li, Ziyao Zeng, Shu Liu, Xiaohua Wang, Yuji Xie, Tian Yin, Qingqing Wei, Yuehong Luo, Lei Shi, Benyun Wang, Chunmei Wu, Rangke Yang, Zhicong Chen, Xiao-Guang Zhou, Xiaohong |
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A 10-unit increase in MOI was associated with 1.08-fold (95% CI 1.05, 1.11) $ ADI_{D} $ and an increase of 0.14 (95% CI 0.05, 0.23) in the logarithmic transformation of R0. MOI-based R0 of dengue varied by month and average monthly temperature. During the active period of Ae. albopictus from April to November in Guangzhou region, a high risk of dengue outbreak was predicted by the MOI-based R0 model, especially from August to October, with the predicted R0 > 1. Meanwhile, from December to March, the estimates of MOI-based R0 were < 1. Conclusions The present study enriched our knowledge about mosquito-based surveillance indicators and indicated that the MOI of Ae. albopictus could be valuable for application in estimating the R0 of dengue using a statistical model. The MOI-based R0 model prediction of the risk of dengue transmission varied by month and temperature in Guangzhou. Our findings lay a foundation for further development of a complex efficient dengue risk prediction system. 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