This paper compares analytically and empirically the frequentist and Bayesian measures of error in small area estimation. The model postulated is the nested error regression model which allows for random small area effects to represent the joint effect of small area characteristics that are not accounted for by the ®xed regressor variables. When the variance components are known, then, under a uniform prior for the regression coef®cients and normality of the error terms, the frequentist and the Bayesian approaches yield the same predictors and prediction mean-squared errors (MSEs) (de®ned accordingly). When the variance components are unknown, it is common practice to replace the unknown variances by sample estimates in the expressions for the optimal predictors, so that the resulting empirical predictors remain the same under the two approaches. The use of this paradigm requires, however, modi®cations to the expressions of the prediction MSE to account for the extra variability induced by the need to estimate the variance components. The main focus of this paper is to review and compare the modi®cations to prediction MSEs proposed in the literature under the two approaches, with special emphasis on the orders of the bias of the resulting approximations to the true MSEs. Some new approximations based on Monte Carlo simulation are also proposed and compared with the existing methods. The advantage of these approximations is their simplicity and generality. Finite sample frequentist properties of the various methods are explored by a simulation study. The main conclusions of this study are that the use of second-order bias corrections generally yields better results in terms of the bias of the MSE approximations and the coverage properties of con®dence intervals for the small area means. The Bayesian methods are found to have good frequentist properties, but they can be inferior to the frequentist methods. The second-order approximations under both approaches have, however, larger variances than the corresponding ®rst-order approximations which in most cases result in higher MSEs of the MSE approximations.
Countries across West Africa began reporting COVID-19 cases in February 2020. By March, the pandemic began disrupting activities to control and eliminate neglected tropical diseases (NTDs) as health ministries ramped up COVID-19–related policies and prevention measures. This was followed by interim guidance from the WHO in April 2020 to temporarily pause mass drug administration (MDA) and community-based surveys for NTDs. While the pandemic was quickly evolving worldwide, in most of West Africa, governments and health ministries took quick action to implement mitigation measures to slow the spread. The U.S. Agency for International Development (USAID) Act to End NTDs | West program (Act | West) began liaising with national NTD programs in April 2020 to pave a path toward the eventual resumption of activities. This process consisted of first collecting and analyzing COVID-19 epidemiological data, policies, and standard operating procedures across the program’s 11 countries. The program then developed an NTD activity restart matrix that compiled essential considerations to restart activities. By December 2020, all 11 countries in Act | West safely restarted MDA and certain surveys to monitor NTD prevalence or intervention impact. Preliminary results show satisfactory MDA program coverage, meaning that enough people are taking the medicine to keep countries on track toward achieving their NTD disease control and elimination goals, and community perceptions have remained positive. The purpose of this article is to share the lessons and best practices that have emerged from the adoption of strategies to limit the spread of the novel coronavirus during MDA and other program activities.
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