Geostatistical methods for disease mapping and visualisation using data from spatio‐temporally referenced prevalence surveys
In this paper, we set out general principles and develop geostatistical methods for the analysis of data from spatio‐temporally referenced prevalence surveys. Our objective is to provide a tutorial guide that can be used in order to identify parsimonious geostatistical models for prevalence mapping....
| Main Authors: | , , , |
|---|---|
| Format: | Journal article |
| Published: |
Wiley
2018
|
| _version_ | 1797100513910587392 |
|---|---|
| author | Giorgi, E Diggle, P Snow, R Noor, A |
| author_facet | Giorgi, E Diggle, P Snow, R Noor, A |
| author_sort | Giorgi, E |
| collection | OXFORD |
| description | In this paper, we set out general principles and develop geostatistical methods for the analysis of data from spatio‐temporally referenced prevalence surveys. Our objective is to provide a tutorial guide that can be used in order to identify parsimonious geostatistical models for prevalence mapping. A general variogram‐based Monte Carlo procedure is proposed to check the validity of the modelling assumptions. We describe and contrast likelihood‐based and Bayesian methods of inference, showing how to account for parameter uncertainty under each of the two paradigms. We also describe extensions of the standard model for disease prevalence that can be used when stationarity of the spatio‐temporal covariance function is not supported by the data. We discuss how to define predictive targets and argue that exceedance probabilities provide one of the most effective ways to convey uncertainty in prevalence estimates. We describe statistical software for the visualisation of spatio‐temporal predictive summaries of prevalence through interactive animations. Finally, we illustrate an application to historical malaria prevalence data from 1 334 surveys conducted in Senegal between 1905 and 2014. |
| first_indexed | 2024-03-07T05:38:37Z |
| format | Journal article |
| id | oxford-uuid:e4caf390-8caf-40e2-a451-5b5f98837016 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T05:38:37Z |
| publishDate | 2018 |
| publisher | Wiley |
| record_format | dspace |
| spelling | oxford-uuid:e4caf390-8caf-40e2-a451-5b5f988370162022-03-27T10:19:09ZGeostatistical methods for disease mapping and visualisation using data from spatio‐temporally referenced prevalence surveysJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:e4caf390-8caf-40e2-a451-5b5f98837016Symplectic Elements at OxfordWiley2018Giorgi, EDiggle, PSnow, RNoor, AIn this paper, we set out general principles and develop geostatistical methods for the analysis of data from spatio‐temporally referenced prevalence surveys. Our objective is to provide a tutorial guide that can be used in order to identify parsimonious geostatistical models for prevalence mapping. A general variogram‐based Monte Carlo procedure is proposed to check the validity of the modelling assumptions. We describe and contrast likelihood‐based and Bayesian methods of inference, showing how to account for parameter uncertainty under each of the two paradigms. We also describe extensions of the standard model for disease prevalence that can be used when stationarity of the spatio‐temporal covariance function is not supported by the data. We discuss how to define predictive targets and argue that exceedance probabilities provide one of the most effective ways to convey uncertainty in prevalence estimates. We describe statistical software for the visualisation of spatio‐temporal predictive summaries of prevalence through interactive animations. Finally, we illustrate an application to historical malaria prevalence data from 1 334 surveys conducted in Senegal between 1905 and 2014. |
| spellingShingle | Giorgi, E Diggle, P Snow, R Noor, A Geostatistical methods for disease mapping and visualisation using data from spatio‐temporally referenced prevalence surveys |
| title | Geostatistical methods for disease mapping and visualisation using data from spatio‐temporally referenced prevalence surveys |
| title_full | Geostatistical methods for disease mapping and visualisation using data from spatio‐temporally referenced prevalence surveys |
| title_fullStr | Geostatistical methods for disease mapping and visualisation using data from spatio‐temporally referenced prevalence surveys |
| title_full_unstemmed | Geostatistical methods for disease mapping and visualisation using data from spatio‐temporally referenced prevalence surveys |
| title_short | Geostatistical methods for disease mapping and visualisation using data from spatio‐temporally referenced prevalence surveys |
| title_sort | geostatistical methods for disease mapping and visualisation using data from spatio temporally referenced prevalence surveys |
| work_keys_str_mv | AT giorgie geostatisticalmethodsfordiseasemappingandvisualisationusingdatafromspatiotemporallyreferencedprevalencesurveys AT digglep geostatisticalmethodsfordiseasemappingandvisualisationusingdatafromspatiotemporallyreferencedprevalencesurveys AT snowr geostatisticalmethodsfordiseasemappingandvisualisationusingdatafromspatiotemporallyreferencedprevalencesurveys AT noora geostatisticalmethodsfordiseasemappingandvisualisationusingdatafromspatiotemporallyreferencedprevalencesurveys |