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The authors acknowledge the people who gave their comments on their article "Bivariate Binomial Spatial Modeling of Loa Ioa Prevalence in Tropical Africa" published within the journal. They agree to some comments on the spatial sparsity of the data used in their study. They think that the suggestion of using a hierarchical model with village and region random effects instead of continuous spatial smoothing is a good point.

Clinger and Van Ness have shown how to sample a stochastic process in a periodic manner such that the autocorrelation function for that process can be estimated at all lags. We derive approximate confidence intervals for these estimates, focusing on low-order autoregressive moving average processes. Our methods are illustrated with an example from plant disease epidemiology. [ABSTRACT FROM AUTHOR]

The author comments on the article "Bivariate Binomial Spatial Modeling of Loa Ioa Prevalence in Tropical Africa," by Ciprian M. Crainiceanu, Peter J. Diggle and Barry Rowlingson published within the journal. He offers his congratulations to the authors for writing a thought-provoking article that addresses problems in public health. He believes that the strategy of moving between a complex Bayesian spatial analysis relying on MCMC and a fast approximation is appealing.

The author comments on the article "Bivariate Binomial Spatial Modeling of Loa Ioa Prevalence in Tropical Africa," by Ciprian M. Crainiceanu, Peter J. Diggle and Barry Rowlingson published within the journal. She thinks that the authors were able to demonstrate the potential for a field tool for Loa Ioa risk mapping that is practical and realizable. She commends the importance and relevance of the study on the Loa Ioa prevalence.