Design and Analysis of Prevalence Surveys for Neglected Tropical Diseases
Event details
| Date | 04.06.2021 |
| Hour | 16:15 › 17:30 |
| Speaker | Peter Diggle, Lancaster University |
| Location | Online |
| Category | Conferences - Seminars |
Joint work with Benjamin Amoah, Claudio Fronterre, Emanuele Giorgi, Olatunji Johnson
In low-resource settings, where disease registries do not exist, prevalence mapping relies on data collected form surveys of disease prevalence taken in a sample of the communities at risk within the region of interest, possibly supplemented by remotely sensed images that can act as proxies for environmental risk factors. A standard geostatistical model for data of this kind is a generalized linear mixed model,
Yi ~ Binomial(mi; P(xi)) log [P(x)/{(1- P(xi)}] = d(x)β + S(x),
where Yi is the number of positives in a sample of mi individuals at location xi, d(x) is a vector of spatially referenced explanatory variables available at any location x within the region of interest, and S(x) is a Gaussian process. In this talk, I will first review statistical methods and software associated with this standard model, then consider several methodological extensions and their applications to some Africa-wide control programmes for Neglected Tropical Diseases to demonstrate the very substantial gains in efficiency that can be obtained by comparison with currently used methods.
Diggle, P.J. and Giorgi, E. (2019). Model-based Geostatistics: Methods and Applications in Global Public Health. Boca Raton: CRC Press
Fronterre, C., Amoah, B., Giorgi, E., Stanton, M.C. and Diggle, P.J. (2020). Design and analysis of elimination surveys for neglected tropical diseases. Journal of Infectious Diseases DOI: 10.1093/infdis/jiz554
Diggle, P.J., Amoah, B., Fronterre, C., Giorgi, E. and Johnson, O. (2021). Rethinking NTD prevalence survey design and analysis: a geospatial paradigm. Transactions of the of the Royal Society of Tropical Medicine and Hygiene, 115, 208-210. doi:10.1093/trstmh/trab020
In low-resource settings, where disease registries do not exist, prevalence mapping relies on data collected form surveys of disease prevalence taken in a sample of the communities at risk within the region of interest, possibly supplemented by remotely sensed images that can act as proxies for environmental risk factors. A standard geostatistical model for data of this kind is a generalized linear mixed model,
Yi ~ Binomial(mi; P(xi)) log [P(x)/{(1- P(xi)}] = d(x)β + S(x),
where Yi is the number of positives in a sample of mi individuals at location xi, d(x) is a vector of spatially referenced explanatory variables available at any location x within the region of interest, and S(x) is a Gaussian process. In this talk, I will first review statistical methods and software associated with this standard model, then consider several methodological extensions and their applications to some Africa-wide control programmes for Neglected Tropical Diseases to demonstrate the very substantial gains in efficiency that can be obtained by comparison with currently used methods.
Diggle, P.J. and Giorgi, E. (2019). Model-based Geostatistics: Methods and Applications in Global Public Health. Boca Raton: CRC Press
Fronterre, C., Amoah, B., Giorgi, E., Stanton, M.C. and Diggle, P.J. (2020). Design and analysis of elimination surveys for neglected tropical diseases. Journal of Infectious Diseases DOI: 10.1093/infdis/jiz554
Diggle, P.J., Amoah, B., Fronterre, C., Giorgi, E. and Johnson, O. (2021). Rethinking NTD prevalence survey design and analysis: a geospatial paradigm. Transactions of the of the Royal Society of Tropical Medicine and Hygiene, 115, 208-210. doi:10.1093/trstmh/trab020
Practical information
- Informed public
- Free
Organizer
- Anthony Davison
Contact
- Maroussia Schaffner