geoENV VI Geostatistics for Environmental Applications by Amílcar Soares

By Amílcar Soares

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The covariance of the demographic variable in this paper, using non-conditional sequential Gaussian simulation. Each simulated normal score is then substituted by the value of same rank in the distribution of proportion of Hispanic females. To maintain the correlation among covariates, all three covariate maps were modified simultaneously. The operation was repeated 100 times, yielding 18 P. Goovaerts Table 1 Results of the correlation analysis of cervix cancer mortality rates and kriged risks with two putative covariates, as well as their interaction.

Kriging variance) is ignored in the analysis. 2 Stochastic Simulation of Cancer Mortality Risk Static maps of risk estimates and the associated prediction variance fail to depict the uncertainty attached to the spatial distribution of risk values and do not allow its propagation through local cluster analysis. Instead of a unique set of smooth risk estimates {ˆ r P K (vα ), α = 1, . . , N}, stochastic simulation aims to generate a set of L equally-probable realizations of the spatial distribution of risk values, {r (l) (vα ), α = 1, .

The Number of Initial Cases The number of cases in the non-epidemic period is very hard to estimate. Actually, we do not know if the start of an epidemic is caused by a small number of infected remaining from a previous epidemic, or whether it is due to infection coming from abroad. Since we start the assimilation at a time when we know, almost for sure, that the epidemic has not yet started anywhere, this number of infected represents a very small proportion of the population. Moreover, given that we inject randomly a few infected to prevent the extinction of the infection, even if that number is very small, there will be a compensation from this injection.

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