Giardina, Federica. Bayesian spatial models applied to malaria epidemiology. 2013, PhD Thesis, University of Basel, Faculty of Science.
Restricted to Repository staff only until 16 December 2018.
Official URL: http://edoc.unibas.ch/diss/DissB_11661
Binomial (ZIB) models were developed to produce spatially explicit parasitaemia risk estimates and number of infected children below the age of 5 years in Senegal. Geostatistical ZIB models were able to account for the large number of zero-prevalence survey locations (70%) in the Senegalese MIS 2008 dataset. Model validation
confirmed that the ZIB model had a better predictive ability than the standard Binomial analogue. Bayesian
variable selection methods were incorporated within a geostatistical framework in order to choose the best set of environmental and climatic covariates associated with the parasitaemia risk. Several ITN coverage indicators were calculated to assess the effectiveness of interventions.Chapter 3 explores different modelling specifications of zero-altered models and suggests model formulations in a geostatistical setting. In particular, the work addresses the problem of selecting variables and assessing the need of incorporating a spatial structure in the modelling of the mixing probability and the non-degenerate distribution. Specific prior distributions for spatial process selection based on non-zero random effects variances are proposed and analyzed through a set of simulated data. The proposed approach was applied to obtain simultaneous estimation of suitability of malaria transmission and of conditional risk in Senegal. The renewed interest in malaria eradication suggests that more sparse data will be produced from parasitological as well as entomological surveys.The impact of environmental predictors on malaria risk is commonly modeled as a linear effect, constant throughout the study area. However, more flexible functional forms, such as piece-wise linear or splines may be required to capture non-linear relationships between the predictors and malaria risk. The area under study is often large, covered by different regions, (e.g. ecological zones) and the relationship between the disease and its risk factors may not be constant across the area. Furthermore, the spatial correlation is likely to vary not only as a function of distance but also of their geographic position. Chapter 4 develops Bayesian spatial variable selection methods with spike-and-slab prior structure that allow the choice of different predictors and their functional forms in non-stationary geostatistical models for mapping disease survey data. Penalized spline effects are re-parameterized as mixed effects terms and their selection is based on non-zero random effects variance identification. Spatially varying weights are proposed to achieve smoothness across irregularly shaped regions. These methods are applied on the analysis of data from the Mali DHS to obtain spatially explicit estimates of the disease burden in the country.Few studies have linked malaria survey data with Remote Sensing (RS)-derived land cover/use (LC) variables. Chapter 5 assesses the effect of the spatial resolution of RS-derived environmental variables on malaria risk estimation in Mozambique. A proximity measure to define LC variables to be included as covariate in a geostatistical model for malaria risk is proposed and applied to the Mozambican DHS dataset in 2011. The model was validated using a LC layer at 5 m resolution produced by MALAREO, a Seventh Framework Programme (FP7) funded project which covered part of Mozambique during 2010-2012, and freely available Remote sensing sources. The predictive performance was compared.When prevalence estimation relies on the compilation of historical data, surveys are commonly heterogeneous in season and sampled population (age groups). In Chapter 6, age and time heterogeneity between surveys is addressed by proposing a general formulation that couples spatial statistical models and mathematical transmission models allowing uncertainty incorporation. The proposed methodology is applied to obtain age/season-specific high resolution disease risk estimates in Zambia. By 2013, six African countries had completed two rounds of MIS: Angola, Liberia, Mozambique, Rwanda, Senegal, and Tanzania. In Chapter 7, a spatio-temporal analysis was performed to estimate changes
in malaria parasitemia risk across these countries. Additionally, the coverage and effectiveness of control measures (i.e., ITN and IRS) was quantified at national and subnational level in reducing malaria risk, after taking into account climatic factors. The analysis was performed with a Bayesian geostatistical model and spatially varying coefficients to study disease/interventions associations. Bayesian variable selection procedures were developed to select
the most relevant ITN measure in reducing malaria risk and spatial kriging over the study area was performed to produce intervention coverage maps. For the first time, smooth maps of probability of decrease in parasitemia were produced.The methods described throughout this thesis may not be applied directly from field practitioners or NMCP personnel, since they require specialized knowledge. However, we are currently working on the implementation of the models with entirely free softwares and user-friendly interfaces to be distributed to the NMCPs and facilitate their work in monitoring and evaluating the progress in the fight of the disease.
|Advisors:||Tanner, Marcel and Vounatsou, Penelope and Biggeri, Annibale|
|Faculties and Departments:||09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Health Interventions > Malaria Vaccines (Tanner)|
|Bibsysno:||Link to catalogue|
|Number of Pages:||1 Online-Ressource (xxviii, 168 Seiten)|
|Last Modified:||30 Jun 2016 10:59|
|Deposited On:||23 May 2016 08:11|
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