Introduction Time-to-event data (survival data) analysis consists of the study of the time until a specific event, such as malaria, takes place. In many real-life contexts, time-to-event data including time-to- malaria are grouped into clusters by districts, villages, and so on. To model such clustered survival data, frailty models (Duchateau and Janssen, 2008) have become increasingly popular. These models extend the univariate survival models by adding random effects to the model to describe the data dependencies. In the case of the malaria survival data considered, spatial clustering occurs: adjacent households share similar environmental and social factors resulting in disease-relevant spatial dependencies (Fig 1 and 2). Since the analysis of spatially correlated survival data has been barely studied, there is a need to extend the available classical survival models to spatial frailty models to explore the malaria data further, thereby gaining insight in the disease dynamics of malaria around the Gilgel Gibe dam. The Statistical Analysis of Spatially Correlated Survival Data Using a Malaria Dataset, around Gilgel Gibe Dam, Ethiopia. PhD Student: Yehenew Getachew (JU) The objectives of this on-going PhD study are to: 1.Explore the available approaches in modelling time-to- event data, within the context of spatial dependencies and clustering; 2. Develop spatially correlated survival models, that can be used in modelling the pattern of malaria incidence among children living around Gilgel Gibe area using important covariates, while accounting for spatial correlation within as well as between villages. The malaria dataset used, comprises time-to-malaria of 2,080 children less than 10 years of age occurring during the years followed every 3-days, together with relevant covariate information. Residential addresses including the latitude, longitude and altitude of the study subjects were also recorded. This data offers the possibility of using spatial models to discern spatial patterns of malaria, and how these patterns distribute over the study area. Arc GIS is being utilized to develop accurate maps of the study area. Further, different spatial covariance structures will be explored in simulations and real dataset on time-to-malaria giving due attention to the various implementation issues of our models. Comparative analysis among the proposed competing models will be performed using different appropriate computational techniques. PhD research activities accomplished to date are: o Visualizations of the study areas, including Gilgel Gibe dam and the 2080 households are finalized using Arc GIS (Fig. 2). o Various existing & promising modelling approaches to model spatially correlated survival data have been explored and their suitability to our time-to-malaria dataset is tested. o Possible extension of such existing spatial frailty models is being carried out and at each step, we are testing our proposed and/or existing models with the help of simulated datasets generated using SAS IML and R statistical software. Fig 2: Gilgel Gibe dam and clustered study households Duchateau, L. and Janssen, P. (2008). The frailty model. Springer Verlag, 318 pages. This is PhD work done in the context of the IUC-JU project. Contacts: PhD: Supervisor: Materials and Methods References Current Progress Supervisor: Prof. Dr. Luc Duchateau (UGent) Fig 1. Distribution of endemic malaria in Africa and Ethiopia, respectively.