Geostatistical modelling of schistosomiasis transmission in Africa

Schistosomiasis is a parasitic disease that is currently endemic in more than 70 countries with the bulk of infections concentrated in Africa. The interest in schistosomiasis has recently grown due to the commitment of substantial amounts of funding to the control of the so-called neglected tropical diseases. Targeting of interventions and allocation of financial resources should be driven by evidenced-based information on the spatial distribution and schistosomiasis burden estimates in order to increase cost-effectiveness and to meet local needs. Currently, decisions are mainly made based on crude schistosomiasis risk estimates which are largely obsolete due to ongoing control efforts, ecological transformations, demographic changes and improved hygiene, among other reasons.
Schistosomiasis transmission depends on the distribution of intermediate host snail species. This distribution is determined by climatic and other environmental conditions, such as temperature, precipitation or water flow velocity. Statistical models can be used to establish the relation between the aforementioned factors and schistosomiasis risk and to predict the risk at unobserved locations. Empirical risk mapping requires observed prevalence data distributed within the area of interest, however contemporary large-scale surveys are not available. To address this issue, the European Union (EU)-funded CONTRAST project initiated the development of the Global Neglected Tropical Disease (GNTD) database. To-date, the GNTD database is the most comprehensive schistosomiasis database in Africa.
Schistosomiasis prevalence data are spatially correlated, because locations in close proximity share common spatial exposures, which similarly influence transmission. Standard statistical models are not appropriate because they assume independence between locations, leading to imprecise parameter estimates and risk predictions. Geostatistical models assume take into account potential spatial correlation by introducing location-specific random effects. Bayesian model formulations, implemented via Markov chain Monte Carlo (MCMC) simulations methods, enable model fit overcoming the computational problems of likelihood-based methods.
Geostatistical model fit requires the repeated inversion of the correlation matrix of the spatial process. The size of this matrix increases with the number of locations and, for very large number of locations, matrix inversion is infeasible. An important aspect of geostatistical model fit is the choice of predictors driving schistosomiasis transmission. There are a number of environmental factors which are correlated, complicating model fit. Rigorous geostatistical variable selection has not yet been applied in spatial schistosomiasis epidemiology. Data compilations contain heterogeneous surveys across locations in terms of age groups involved and diagnostic methods used. The lack of prevalence data reported in standard age groups complicates the estimation of age-adjusted schistosomiasis risk. A common assumption of geostatistical models is that of isotropy implying that spatial correlation is a function of distance between locations irrespective of direction or location. However in schistosomiasis risk mapping, spatial correlation is likely to be related to the direction of river flow due to water-dependent intermediate host snail species. This might introduce directional dependency (anisotropy). Schistosomiasis tends to be present in areas with other neglected diseases. Cost-effective interventions call for an integrated disease control, which requires estimating of the geographical distribution of high co-endemicity. The co-endemic diseases might be correlated, however surveys screening for multiple diseases are not available over large geographical areas. There rather exist data from independent surveys screening for single diseases on different sets of individuals.
The main contributions of this thesis were (i) the development of Bayesian isotropic and anisotropic geostatistical models for high spatial resolution schistosomiasis risk mapping and prediction based on age-heterogeneous historical survey data collected over very large number of locations; (ii) the development of statistical methodology for assessing the geographical distribution of co-infection risk from independent single-disease surveys; and (iii) the estimation of location-specific schistosomiasis risk and number of infected people in 29 countries across West and eastern Africa. Hence, for first time, empirical model-based evidence of schistosomiasis risk and burden in those regions is provided. These estimates are of considerable importance for schistosomiasis control programs, as they indicate high-risk areas requiring interventions, allow calculations of the number of drugs required based on WHO guidelines, and provide baseline maps to assess effectiveness of interventions on the roadmap towards schistosomiasis elimination.