The Experts below are selected from a list of 45108 Experts worldwide ranked by ideXlab platform
Federica Giardina - One of the best experts on this subject based on the ideXlab platform.
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getting more from heterogeneous hiv 1 surveillance data in a high Immigration Country estimation of incidence and undiagnosed population size using multiple biomarkers
International Journal of Epidemiology, 2019Co-Authors: Federica Giardina, Ethan Romeroseverson, Maria Axelsson, Veronica Svedhem, Thomas Leitner, Tom Britton, Jan AlbertAbstract:markdownabstractBACKGROUND: Most HIV infections originate from individuals who are undiagnosed and unaware of their infection. Estimation of this quantity from surveillance data is hard because there is incomplete knowledge about (i) the time between infection and diagnosis (TI) for the general population, and (ii) the time between Immigration and diagnosis for foreign-born persons. METHODS: We developed a new statistical method for estimating the incidence of HIV-1 and the number of undiagnosed people living with HIV (PLHIV), based on dynamic modelling of heterogeneous HIV-1 surveillance data. The methods consist of a Bayesian non-linear mixed effects model using multiple biomarkers to estimate TI of HIV-1-positive individuals, and a novel incidence estimator which distinguishes between endogenous and exogenous infections by modelling explicitly the probability that a foreign-born person was infected either before or after Immigration. The incidence estimator allows for direct calculation of the number of undiagnosed persons. The new methodology is illustrated combining heterogeneous surveillance data from Sweden between 2003 and 2015. RESULTS: A leave-one-out cross-validation study showed that the multiple-biomarker model was more accurate than single biomarkers (mean absolute error 1.01 vs ≥1.95). We estimate that 816 [95% credible interval (CI) 775-865] PLHIV were undiagnosed in 2015, representing a proportion of 10.8% (95% CI 10.3-11.4%) of all PLHIV. CONCLUSIONS: The proposed methodology will enhance the utility of standard surveillance data streams and will be useful to monitor progress towards and compliance with the 90-90-90 UNAIDS target.
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getting more from heterogeneous hiv 1 surveillance data in a high Immigration Country estimation of incidence and undiagnosed population size using multiple biomarkers
bioRxiv, 2018Co-Authors: Federica Giardina, Ethan Romeroseverson, Maria Axelsson, Veronica Svedhem, Thomas Leitner, Tom Britton, Jan AlbertAbstract:Background: Most HIV infections originate from individuals who are undiagnosed and unaware of their infection. Estimation of this quantity from surveillance data is hard because there is incomplete knowledge about i) the time between infection and diagnosis (TI) for the general population and ii) the time between Immigration and diagnosis for foreign-born persons. Development: We developed a new statistical method for estimating the number of undiagnosed people living with HIV (PLHIV) and the incidence of HIV-1 based on dynamic modeling of heterogenous HIV-1 surveillance data. We formulated a Bayesian non-linear mixed effects model using multiple biomarkers to estimate TI accounting for biomarker correlation and individual heterogeneities. We explicitly model the probability that an HIV-1 infected foreign-born person was infected either before or after Immigration to distinguish between endogenous and exogeneous incidence. The incidence estimator allows for direct calculation of the number of undiagnosed persons. Application: The model was applied to surveillance data in Sweden. The dynamic biomarker model was trained on longitudinal data from 31 treatment-naive patients with well-defined TI, using CD4 counts, BED serology, polymorphisms in HIV-1 pol sequences, and testing history. The multiple-biomarker model was more accurate than single biomarkers (mean absolute error 1.01 vs >= 1.95). We estimate that 813 (95% CI 780-862) PLHIV were undiagnosed in 2015, representing a proportion of 10.8% (95% CI 10.4-11.3%) of all PLHIV. Conclusions: The proposed methodology will enhance the utility of standard surveillance data streams and will be useful to monitor progress towards and compliance with the 90-90-90 UNAIDS target. Key words: HIV-1, BED assay, pol sequences, incidence estimation, undiagnosed HIV-1 infections.
Jan Albert - One of the best experts on this subject based on the ideXlab platform.
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getting more from heterogeneous hiv 1 surveillance data in a high Immigration Country estimation of incidence and undiagnosed population size using multiple biomarkers
International Journal of Epidemiology, 2019Co-Authors: Federica Giardina, Ethan Romeroseverson, Maria Axelsson, Veronica Svedhem, Thomas Leitner, Tom Britton, Jan AlbertAbstract:markdownabstractBACKGROUND: Most HIV infections originate from individuals who are undiagnosed and unaware of their infection. Estimation of this quantity from surveillance data is hard because there is incomplete knowledge about (i) the time between infection and diagnosis (TI) for the general population, and (ii) the time between Immigration and diagnosis for foreign-born persons. METHODS: We developed a new statistical method for estimating the incidence of HIV-1 and the number of undiagnosed people living with HIV (PLHIV), based on dynamic modelling of heterogeneous HIV-1 surveillance data. The methods consist of a Bayesian non-linear mixed effects model using multiple biomarkers to estimate TI of HIV-1-positive individuals, and a novel incidence estimator which distinguishes between endogenous and exogenous infections by modelling explicitly the probability that a foreign-born person was infected either before or after Immigration. The incidence estimator allows for direct calculation of the number of undiagnosed persons. The new methodology is illustrated combining heterogeneous surveillance data from Sweden between 2003 and 2015. RESULTS: A leave-one-out cross-validation study showed that the multiple-biomarker model was more accurate than single biomarkers (mean absolute error 1.01 vs ≥1.95). We estimate that 816 [95% credible interval (CI) 775-865] PLHIV were undiagnosed in 2015, representing a proportion of 10.8% (95% CI 10.3-11.4%) of all PLHIV. CONCLUSIONS: The proposed methodology will enhance the utility of standard surveillance data streams and will be useful to monitor progress towards and compliance with the 90-90-90 UNAIDS target.
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getting more from heterogeneous hiv 1 surveillance data in a high Immigration Country estimation of incidence and undiagnosed population size using multiple biomarkers
bioRxiv, 2018Co-Authors: Federica Giardina, Ethan Romeroseverson, Maria Axelsson, Veronica Svedhem, Thomas Leitner, Tom Britton, Jan AlbertAbstract:Background: Most HIV infections originate from individuals who are undiagnosed and unaware of their infection. Estimation of this quantity from surveillance data is hard because there is incomplete knowledge about i) the time between infection and diagnosis (TI) for the general population and ii) the time between Immigration and diagnosis for foreign-born persons. Development: We developed a new statistical method for estimating the number of undiagnosed people living with HIV (PLHIV) and the incidence of HIV-1 based on dynamic modeling of heterogenous HIV-1 surveillance data. We formulated a Bayesian non-linear mixed effects model using multiple biomarkers to estimate TI accounting for biomarker correlation and individual heterogeneities. We explicitly model the probability that an HIV-1 infected foreign-born person was infected either before or after Immigration to distinguish between endogenous and exogeneous incidence. The incidence estimator allows for direct calculation of the number of undiagnosed persons. Application: The model was applied to surveillance data in Sweden. The dynamic biomarker model was trained on longitudinal data from 31 treatment-naive patients with well-defined TI, using CD4 counts, BED serology, polymorphisms in HIV-1 pol sequences, and testing history. The multiple-biomarker model was more accurate than single biomarkers (mean absolute error 1.01 vs >= 1.95). We estimate that 813 (95% CI 780-862) PLHIV were undiagnosed in 2015, representing a proportion of 10.8% (95% CI 10.4-11.3%) of all PLHIV. Conclusions: The proposed methodology will enhance the utility of standard surveillance data streams and will be useful to monitor progress towards and compliance with the 90-90-90 UNAIDS target. Key words: HIV-1, BED assay, pol sequences, incidence estimation, undiagnosed HIV-1 infections.
Veronica Svedhem - One of the best experts on this subject based on the ideXlab platform.
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getting more from heterogeneous hiv 1 surveillance data in a high Immigration Country estimation of incidence and undiagnosed population size using multiple biomarkers
International Journal of Epidemiology, 2019Co-Authors: Federica Giardina, Ethan Romeroseverson, Maria Axelsson, Veronica Svedhem, Thomas Leitner, Tom Britton, Jan AlbertAbstract:markdownabstractBACKGROUND: Most HIV infections originate from individuals who are undiagnosed and unaware of their infection. Estimation of this quantity from surveillance data is hard because there is incomplete knowledge about (i) the time between infection and diagnosis (TI) for the general population, and (ii) the time between Immigration and diagnosis for foreign-born persons. METHODS: We developed a new statistical method for estimating the incidence of HIV-1 and the number of undiagnosed people living with HIV (PLHIV), based on dynamic modelling of heterogeneous HIV-1 surveillance data. The methods consist of a Bayesian non-linear mixed effects model using multiple biomarkers to estimate TI of HIV-1-positive individuals, and a novel incidence estimator which distinguishes between endogenous and exogenous infections by modelling explicitly the probability that a foreign-born person was infected either before or after Immigration. The incidence estimator allows for direct calculation of the number of undiagnosed persons. The new methodology is illustrated combining heterogeneous surveillance data from Sweden between 2003 and 2015. RESULTS: A leave-one-out cross-validation study showed that the multiple-biomarker model was more accurate than single biomarkers (mean absolute error 1.01 vs ≥1.95). We estimate that 816 [95% credible interval (CI) 775-865] PLHIV were undiagnosed in 2015, representing a proportion of 10.8% (95% CI 10.3-11.4%) of all PLHIV. CONCLUSIONS: The proposed methodology will enhance the utility of standard surveillance data streams and will be useful to monitor progress towards and compliance with the 90-90-90 UNAIDS target.
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getting more from heterogeneous hiv 1 surveillance data in a high Immigration Country estimation of incidence and undiagnosed population size using multiple biomarkers
bioRxiv, 2018Co-Authors: Federica Giardina, Ethan Romeroseverson, Maria Axelsson, Veronica Svedhem, Thomas Leitner, Tom Britton, Jan AlbertAbstract:Background: Most HIV infections originate from individuals who are undiagnosed and unaware of their infection. Estimation of this quantity from surveillance data is hard because there is incomplete knowledge about i) the time between infection and diagnosis (TI) for the general population and ii) the time between Immigration and diagnosis for foreign-born persons. Development: We developed a new statistical method for estimating the number of undiagnosed people living with HIV (PLHIV) and the incidence of HIV-1 based on dynamic modeling of heterogenous HIV-1 surveillance data. We formulated a Bayesian non-linear mixed effects model using multiple biomarkers to estimate TI accounting for biomarker correlation and individual heterogeneities. We explicitly model the probability that an HIV-1 infected foreign-born person was infected either before or after Immigration to distinguish between endogenous and exogeneous incidence. The incidence estimator allows for direct calculation of the number of undiagnosed persons. Application: The model was applied to surveillance data in Sweden. The dynamic biomarker model was trained on longitudinal data from 31 treatment-naive patients with well-defined TI, using CD4 counts, BED serology, polymorphisms in HIV-1 pol sequences, and testing history. The multiple-biomarker model was more accurate than single biomarkers (mean absolute error 1.01 vs >= 1.95). We estimate that 813 (95% CI 780-862) PLHIV were undiagnosed in 2015, representing a proportion of 10.8% (95% CI 10.4-11.3%) of all PLHIV. Conclusions: The proposed methodology will enhance the utility of standard surveillance data streams and will be useful to monitor progress towards and compliance with the 90-90-90 UNAIDS target. Key words: HIV-1, BED assay, pol sequences, incidence estimation, undiagnosed HIV-1 infections.
Tom Britton - One of the best experts on this subject based on the ideXlab platform.
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getting more from heterogeneous hiv 1 surveillance data in a high Immigration Country estimation of incidence and undiagnosed population size using multiple biomarkers
International Journal of Epidemiology, 2019Co-Authors: Federica Giardina, Ethan Romeroseverson, Maria Axelsson, Veronica Svedhem, Thomas Leitner, Tom Britton, Jan AlbertAbstract:markdownabstractBACKGROUND: Most HIV infections originate from individuals who are undiagnosed and unaware of their infection. Estimation of this quantity from surveillance data is hard because there is incomplete knowledge about (i) the time between infection and diagnosis (TI) for the general population, and (ii) the time between Immigration and diagnosis for foreign-born persons. METHODS: We developed a new statistical method for estimating the incidence of HIV-1 and the number of undiagnosed people living with HIV (PLHIV), based on dynamic modelling of heterogeneous HIV-1 surveillance data. The methods consist of a Bayesian non-linear mixed effects model using multiple biomarkers to estimate TI of HIV-1-positive individuals, and a novel incidence estimator which distinguishes between endogenous and exogenous infections by modelling explicitly the probability that a foreign-born person was infected either before or after Immigration. The incidence estimator allows for direct calculation of the number of undiagnosed persons. The new methodology is illustrated combining heterogeneous surveillance data from Sweden between 2003 and 2015. RESULTS: A leave-one-out cross-validation study showed that the multiple-biomarker model was more accurate than single biomarkers (mean absolute error 1.01 vs ≥1.95). We estimate that 816 [95% credible interval (CI) 775-865] PLHIV were undiagnosed in 2015, representing a proportion of 10.8% (95% CI 10.3-11.4%) of all PLHIV. CONCLUSIONS: The proposed methodology will enhance the utility of standard surveillance data streams and will be useful to monitor progress towards and compliance with the 90-90-90 UNAIDS target.
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getting more from heterogeneous hiv 1 surveillance data in a high Immigration Country estimation of incidence and undiagnosed population size using multiple biomarkers
bioRxiv, 2018Co-Authors: Federica Giardina, Ethan Romeroseverson, Maria Axelsson, Veronica Svedhem, Thomas Leitner, Tom Britton, Jan AlbertAbstract:Background: Most HIV infections originate from individuals who are undiagnosed and unaware of their infection. Estimation of this quantity from surveillance data is hard because there is incomplete knowledge about i) the time between infection and diagnosis (TI) for the general population and ii) the time between Immigration and diagnosis for foreign-born persons. Development: We developed a new statistical method for estimating the number of undiagnosed people living with HIV (PLHIV) and the incidence of HIV-1 based on dynamic modeling of heterogenous HIV-1 surveillance data. We formulated a Bayesian non-linear mixed effects model using multiple biomarkers to estimate TI accounting for biomarker correlation and individual heterogeneities. We explicitly model the probability that an HIV-1 infected foreign-born person was infected either before or after Immigration to distinguish between endogenous and exogeneous incidence. The incidence estimator allows for direct calculation of the number of undiagnosed persons. Application: The model was applied to surveillance data in Sweden. The dynamic biomarker model was trained on longitudinal data from 31 treatment-naive patients with well-defined TI, using CD4 counts, BED serology, polymorphisms in HIV-1 pol sequences, and testing history. The multiple-biomarker model was more accurate than single biomarkers (mean absolute error 1.01 vs >= 1.95). We estimate that 813 (95% CI 780-862) PLHIV were undiagnosed in 2015, representing a proportion of 10.8% (95% CI 10.4-11.3%) of all PLHIV. Conclusions: The proposed methodology will enhance the utility of standard surveillance data streams and will be useful to monitor progress towards and compliance with the 90-90-90 UNAIDS target. Key words: HIV-1, BED assay, pol sequences, incidence estimation, undiagnosed HIV-1 infections.
Thomas Leitner - One of the best experts on this subject based on the ideXlab platform.
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getting more from heterogeneous hiv 1 surveillance data in a high Immigration Country estimation of incidence and undiagnosed population size using multiple biomarkers
International Journal of Epidemiology, 2019Co-Authors: Federica Giardina, Ethan Romeroseverson, Maria Axelsson, Veronica Svedhem, Thomas Leitner, Tom Britton, Jan AlbertAbstract:markdownabstractBACKGROUND: Most HIV infections originate from individuals who are undiagnosed and unaware of their infection. Estimation of this quantity from surveillance data is hard because there is incomplete knowledge about (i) the time between infection and diagnosis (TI) for the general population, and (ii) the time between Immigration and diagnosis for foreign-born persons. METHODS: We developed a new statistical method for estimating the incidence of HIV-1 and the number of undiagnosed people living with HIV (PLHIV), based on dynamic modelling of heterogeneous HIV-1 surveillance data. The methods consist of a Bayesian non-linear mixed effects model using multiple biomarkers to estimate TI of HIV-1-positive individuals, and a novel incidence estimator which distinguishes between endogenous and exogenous infections by modelling explicitly the probability that a foreign-born person was infected either before or after Immigration. The incidence estimator allows for direct calculation of the number of undiagnosed persons. The new methodology is illustrated combining heterogeneous surveillance data from Sweden between 2003 and 2015. RESULTS: A leave-one-out cross-validation study showed that the multiple-biomarker model was more accurate than single biomarkers (mean absolute error 1.01 vs ≥1.95). We estimate that 816 [95% credible interval (CI) 775-865] PLHIV were undiagnosed in 2015, representing a proportion of 10.8% (95% CI 10.3-11.4%) of all PLHIV. CONCLUSIONS: The proposed methodology will enhance the utility of standard surveillance data streams and will be useful to monitor progress towards and compliance with the 90-90-90 UNAIDS target.
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getting more from heterogeneous hiv 1 surveillance data in a high Immigration Country estimation of incidence and undiagnosed population size using multiple biomarkers
bioRxiv, 2018Co-Authors: Federica Giardina, Ethan Romeroseverson, Maria Axelsson, Veronica Svedhem, Thomas Leitner, Tom Britton, Jan AlbertAbstract:Background: Most HIV infections originate from individuals who are undiagnosed and unaware of their infection. Estimation of this quantity from surveillance data is hard because there is incomplete knowledge about i) the time between infection and diagnosis (TI) for the general population and ii) the time between Immigration and diagnosis for foreign-born persons. Development: We developed a new statistical method for estimating the number of undiagnosed people living with HIV (PLHIV) and the incidence of HIV-1 based on dynamic modeling of heterogenous HIV-1 surveillance data. We formulated a Bayesian non-linear mixed effects model using multiple biomarkers to estimate TI accounting for biomarker correlation and individual heterogeneities. We explicitly model the probability that an HIV-1 infected foreign-born person was infected either before or after Immigration to distinguish between endogenous and exogeneous incidence. The incidence estimator allows for direct calculation of the number of undiagnosed persons. Application: The model was applied to surveillance data in Sweden. The dynamic biomarker model was trained on longitudinal data from 31 treatment-naive patients with well-defined TI, using CD4 counts, BED serology, polymorphisms in HIV-1 pol sequences, and testing history. The multiple-biomarker model was more accurate than single biomarkers (mean absolute error 1.01 vs >= 1.95). We estimate that 813 (95% CI 780-862) PLHIV were undiagnosed in 2015, representing a proportion of 10.8% (95% CI 10.4-11.3%) of all PLHIV. Conclusions: The proposed methodology will enhance the utility of standard surveillance data streams and will be useful to monitor progress towards and compliance with the 90-90-90 UNAIDS target. Key words: HIV-1, BED assay, pol sequences, incidence estimation, undiagnosed HIV-1 infections.
