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Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability
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| Zeitschriftentitel: | Biometrics |
|---|---|
| Personen und Körperschaften: | , , |
| In: | Biometrics, 69, 2013, 3, S. 683-692 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
|
| Schlagwörter: |
| author_facet |
Cui, Na Chen, Yuguo Small, Dylan S. Cui, Na Chen, Yuguo Small, Dylan S. |
|---|---|
| author |
Cui, Na Chen, Yuguo Small, Dylan S. |
| spellingShingle |
Cui, Na Chen, Yuguo Small, Dylan S. Biometrics Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability |
| author_sort |
cui, na |
| spelling |
Cui, Na Chen, Yuguo Small, Dylan S. 0006-341X 1541-0420 Oxford University Press (OUP) Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability http://dx.doi.org/10.1111/biom.12050 <jats:title>Summary</jats:title><jats:sec><jats:label /><jats:p>Understanding the infection and recovery rate from parasitic infections is valuable for public health planning. Two challenges in modeling these rates are (1) infection status is only observed at discrete times even though infection and recovery take place in continuous time and (2) detectability of infection is imperfect. We address these issues through a Bayesian hierarchical model based on a random effects Weibull distribution. The model incorporates heterogeneity of the infection and recovery rate among individuals and allows for imperfect detectability. We estimate the model by a Markov chain Monte Carlo algorithm with data augmentation. We present simulation studies and an application to an infection study about the parasite <jats:italic>Giardia lamblia</jats:italic> among children in Kenya.</jats:p></jats:sec> Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability Biometrics |
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| title |
Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability |
| title_unstemmed |
Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability |
| title_full |
Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability |
| title_fullStr |
Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability |
| title_full_unstemmed |
Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability |
| title_short |
Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability |
| title_sort |
modeling parasite infection dynamics when there is heterogeneity and imperfect detectability |
| topic |
Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability |
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http://dx.doi.org/10.1111/biom.12050 |
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2013 |
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683-692 |
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<jats:title>Summary</jats:title><jats:sec><jats:label /><jats:p>Understanding the infection and recovery rate from parasitic infections is valuable for public health planning. Two challenges in modeling these rates are (1) infection status is only observed at discrete times even though infection and recovery take place in continuous time and (2) detectability of infection is imperfect. We address these issues through a Bayesian hierarchical model based on a random effects Weibull distribution. The model incorporates heterogeneity of the infection and recovery rate among individuals and allows for imperfect detectability. We estimate the model by a Markov chain Monte Carlo algorithm with data augmentation. We present simulation studies and an application to an infection study about the parasite <jats:italic>Giardia lamblia</jats:italic> among children in Kenya.</jats:p></jats:sec> |
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| author | Cui, Na, Chen, Yuguo, Small, Dylan S. |
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| description | <jats:title>Summary</jats:title><jats:sec><jats:label /><jats:p>Understanding the infection and recovery rate from parasitic infections is valuable for public health planning. Two challenges in modeling these rates are (1) infection status is only observed at discrete times even though infection and recovery take place in continuous time and (2) detectability of infection is imperfect. We address these issues through a Bayesian hierarchical model based on a random effects Weibull distribution. The model incorporates heterogeneity of the infection and recovery rate among individuals and allows for imperfect detectability. We estimate the model by a Markov chain Monte Carlo algorithm with data augmentation. We present simulation studies and an application to an infection study about the parasite <jats:italic>Giardia lamblia</jats:italic> among children in Kenya.</jats:p></jats:sec> |
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| institution | DE-D275, DE-Bn3, DE-Brt1, DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229 |
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| spelling | Cui, Na Chen, Yuguo Small, Dylan S. 0006-341X 1541-0420 Oxford University Press (OUP) Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability http://dx.doi.org/10.1111/biom.12050 <jats:title>Summary</jats:title><jats:sec><jats:label /><jats:p>Understanding the infection and recovery rate from parasitic infections is valuable for public health planning. Two challenges in modeling these rates are (1) infection status is only observed at discrete times even though infection and recovery take place in continuous time and (2) detectability of infection is imperfect. We address these issues through a Bayesian hierarchical model based on a random effects Weibull distribution. The model incorporates heterogeneity of the infection and recovery rate among individuals and allows for imperfect detectability. We estimate the model by a Markov chain Monte Carlo algorithm with data augmentation. We present simulation studies and an application to an infection study about the parasite <jats:italic>Giardia lamblia</jats:italic> among children in Kenya.</jats:p></jats:sec> Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability Biometrics |
| spellingShingle | Cui, Na, Chen, Yuguo, Small, Dylan S., Biometrics, Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability, Applied Mathematics, General Agricultural and Biological Sciences, General Immunology and Microbiology, General Biochemistry, Genetics and Molecular Biology, General Medicine, Statistics and Probability |
| title | Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability |
| title_full | Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability |
| title_fullStr | Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability |
| title_full_unstemmed | Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability |
| title_short | Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability |
| title_sort | modeling parasite infection dynamics when there is heterogeneity and imperfect detectability |
| title_unstemmed | Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability |
| topic | Applied Mathematics, General Agricultural and Biological Sciences, General Immunology and Microbiology, General Biochemistry, Genetics and Molecular Biology, General Medicine, Statistics and Probability |
| url | http://dx.doi.org/10.1111/biom.12050 |