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In this article, we study the problem of estimating the prevalence rate of a disease in a geographical area, based on data collected from a sample of locations within this area. If there are several locations with zero incidence of the disease, the usual estimators are not suitable and so we develop a new estimator, together with an unbiased estimator of its variance, which may be appropriately used in such situations. An application of this estimator is illustrated with data from a large-scale survey, which was carried out in the city of Kolkata, India, to estimate the prevalence rate of stroke. We show that spatial modelling may be used to smooth the observed data before applying our proposed estimator. Our computations show that this smoothing helps to reduce the coefficient of variation and such a model-cum-design-based procedure is useful for estimating the prevalence rate. This method may of course be used in other similar situations. [ABSTRACT FROM AUTHOR]

It is difficult to enumerate the people in India who are engaged in various small-scale industries in the unorganized sector because they are concentrated in small regional pockets. In estimating separately the total numbers of workers earning principally through ten respective single-industries in the unorganized small-scale sector in a specific district in rural India, through numerical illustrations we have two observations to report: (1) A traditional stratified two-stage sampling scheme is ineffective for some of the industries because of failures to capture the earners concentrated in priorly unknown locations. (2) An adaptive sampling scheme extending the initial sample by appropriate ‘network’ formations based on well-defined ‘neighbourhoods’ brings about dramatic improvements exploiting clustering tendencies of earners by different industries. [Copyright &y& Elsevier]