Optimal two-stage spatial sampling design for estimating critical parameters of SARS-CoV-2 epidemic: Efficiency versus feasibility

The COVID-19 pandemic presents an unprecedented clinical and healthcare challenge for the many medical researchers who are attempting to prevent its worldwide spread. It also presents a challenge for statisticians involved in designing appropriate sampling plans to estimate the crucial parameters of the pandemic. These plans are necessary for monitoring and surveillance of the phenomenon and evaluating health policies. In this respect, we can use spatial information and aggregate data regarding the number of verifed infections (either hospitalized or in compulsory quarantine) to improve the standard two-stage sampling design broadly adopted for studying human populations. We present an optimal spatial sampling design based on spatially balanced sampling techniques. We prove its relative performance analytically in comparison to other competing sampling plans, and we also study its properties through a series of Monte Carlo experiments. Considering the optimal theoretical properties of the proposed sampling plan and its feasibility, we discuss suboptimal designs that approximate well optimality and are more readily applicable

Utilizzazione degli ampliamenti del campione per la stima entro piccole aree

La nota si sofferma sull'ampliamento della numerosit\ue0 del campione adottato per la rilevazione delle forze di lavoro. L'ampliamento d\ue0 la possibilit\ue0 di ottenere stime attendibili per sottoinsiemi di popolazione minuti o per aree territoriali ridotte in dimensione e popolazione

First proposal of a Business Architecture to refine the model elaborated by the Sponsorship on Standardisation

This deliverable aims at illustrating a proposal of a Business Architecture (BA) model that can be considered as a starting point to refine the approach produced by the Sponsorship on Standardisation. This renewed model should be shared between the ESSnet on standardisation project members and can represent a first step towards the development of a BA characterised by common frameworks and principles within each Institute/Organisation, so as to work in a more efficient and optimised way

A Business Architecture Model to foster standardisation in official statistics

Business Architecture (BA) is called to play a central role in a programme as complex as that of modernisation and standardisation of the official statistical information production. This study aims at illustrating a BA model for achieving a unity of views, so as to ensure the strategic alignment in each part of an Organisation and to carry out an innovation consistent with the standardisation objective that should be reached. This BAmodel individuates four different business lines (Strategy; Corporate support; Production; Capability) and is led by common infrastructures and principles that become instruments and guidelines for the implementation of each business line group of actions. Both principles and infrastructures facilitate and enhance the standardisation process

Business Architecture Principles to Foster Industrialisation and Standardisation at the Italian National Institute of Statistics

The Italian National Institute of Statistics has recently adopted the common vision proposed for the European Statistic al System, envisaging a different production process of the statistical information, based on its standardisation and industrialisation, thus achieving higher efficiency and quality levels together with lower respondent burden. In order to ensure a proper governance of the transition process, a rigorous definition of the “to be” model is needed: for this purpose, Istat has defined a Business Architecture model, clearly stating the characteristics of the statistical value chain, the way the activities in the different domains interact, the general principles informing the whole process, and the infrastructures required so as to ensure its optimality

Business Architecture model within an official statistical context

Several Official Statistical Institutions/Organisations are currently facing commitments towards modernisation and standardisation of their work processes, both from an organisational and a production-related point of view. In this context, this paper aims at illustrating a reference Business Architecture (BA) model that can be used to enhance this relevant evolution phase and represents a first step towards the development of a sharable common framework, so as to work in a more efficient and optimised way

Observed and estimated prevalence of Covid-19 in Italy: How to estimate the total cases from medical swabs data

During the Covid-19 pandemic in Italy, official data are collected with medical swabs following a pure convenience criterion which, at least in an early phase, has privileged the exam of patients showing evident symptoms. However, there are evidences of a very high proportion of asymptomatic patients. In this situation, in order to estimate the real number of infected (and to estimate the lethality rate), it should be necessary to run a properly designed sample survey through which it would be possible to calculate the probability of inclusion and hence draw sound probabilistic inference. Unfortunately, the survey run by the Italian Statistical Institute encountered many field difficulties. Some researchers proposed estimates of the total prevalence based on various approaches, including epidemiologic models, time series and the analysis of data collected in countries that faced the epidemic in earlier times. In this paper, we propose to estimate the prevalence of Covid-19 in Italy by reweighting the available official data published by the Istituto Superiore di Sanità so as to obtain a more representative sample of the Italian population. Reweighting is a procedure commonly used to artificially modify the sample composition so as to obtain a distribution which is more similar to the population. In this paper, we will use post-stratification of the official data, in order to derive the weights necessary for reweighting the sample results, using age and gender as post-stratification variables, thus obtaining more reliable estimation of prevalence and lethality. Specifically, for Italy, we obtain a prevalence of 9%. The proposed methodology represents a reasonable approximation while waiting for more reliable data obtained with a properly designed national sample survey and that it could be further improved if more data were made available