A municipality-level analysis of excess mortality in Italy in the period January-April 2020

BACKGROUND: the first confirmed cases of COVID-19 in WHO European Region was reported at the end of January 2020 and, from that moment, the epidemic has been speeding up and rapidly spreading across Europe. The health, social, and economic consequences of the pandemic are difficult to evaluate, since there are many scientific uncertainties and unknowns. OBJECTIVES: the main focus of this paper is on statistical methods for profiling municipalities by excess mortality, directly or indirectly caused by COVID-19. METHODS: the use of excess mortality for all causes has been advocated as a measure of impact less vulnerable to biases. In this paper, observed mortality for all causes at municipality level in Italy in the period January-April 2020 was compared to the mortality observed in the corresponding period in the previous 5 years (2015-2019). Mortality data were made available by the Ministry of Internal Affairs Italian National Resident Population Demographic Archive and the Italian National Institute of Statistics (Istat). For each municipality, the posterior predictive distribution under a hierarchical null model was obtained. From the posterior predictive distribution, we obtained excess death counts, attributable community rates and q-values. Full Bayesian models implemented via MCMC simulations were used. RESULTS: absolute number of excess deaths highlights the burden paid by major cities to the pandemic. The Attributable Community Rate provides a detailed picture of the spread of the pandemic among the municipalities of Lombardy, Piedmont, and Emilia-Romagna Regions. Using Q-values, it is clearly recognizable evidence of an excess of mortality from late February to April 2020 in a very geographically scattered number of municipalities. A trade-off between false discoveries and false non-discoveries shows the different values of public health actions. CONCLUSIONS: despite the variety of approaches to calculate excess mortality, this study provides an original methodological approach to profile municipalities with excess deaths accounting for spatial and temporal uncertainty

Effect of temperature in domestic refrigerators on fresh-cut Iceberg salad quality and waste

The evolution of different quality parameters (firmness, weight loss, colour changes, microbial counts, consumer rejection) of packed fresh-cut Iceberg salad was assessed at 4, 8 and 12 \ub0C to simulate domestic refrigerators running at different conditions. The increase in storage temperature did not affect salad firmness and weight loss but increased colour changes, microbial growth and consumer rejection. A survey among Italian consumers was also carried out and demonstrated that fresh-cut salad was mainly consumed within the first 5 days after purchasing. Consumer rejection data were combined with data relevant to the distribution of salad consumption over the days following product purchase, to estimate salad wasting risk. When salad was stored at 4 and 8 \ub0C, estimated wasted packages within the expiration date (7 days) were < 1%. By contrast, 13% of the packages was estimated to be wasted within 7 days of storage at 12 \ub0C. Quantification of wasting risk is a necessary information to identify efficient and sustainable interventions to tackle food waste. \ua9 2017 Elsevier Lt

A transitional non-parametric maximum pseudo-likelihood estimator for disease mapping

Abstract Non-parametric maximum likelihood estimators of relative risk have been proposed as an alternative to empirical Bayes or full Bayes approaches to disease mapping. They have the advantage of being relatively simple, the EM algorithm assures convergence and area classiÿcation is straightforward. However, they do not take into account spatial autocorrelation and have higher mean square error when the true underlying risk pattern is strongly spatially structured. Furthermore, the EM algorithm is sensible to starting values and could converge to local maxima. We review the transitional generalized linear models and propose a transitional non-parametric maximum pseudo-likelihood estimator for disease mapping. The usual kernel likelihood of the mixture models is replaced by the conditional density of the observed response for a single area given the values observed in adjacent areas. The estimation of the parameters is based on the EM algorithm, appropriately modiÿed to handle the problem of local maxima and to estimate the number of components of the mixture. A simulation study shows that the transitional non-parametric maximum pseudo-likelihood estimator performs similarly to full Bayes estimators

Big Data and Causality

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of big data has significantly influenced the causality analysis on various disciplines especially for the last decade due to the fact that the complexity and difficulty on identifying causality among big data has dramatically increased. Data mining, the process of uncovering hidden information from big data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world. The primary aim of this paper is to provide a concise review of the causality analysis in big data. To this end the paper reviews recent significant applications of data mining techniques in causality analysis covering a substantial quantity of research to date, presented in chronological order with an overview table of data mining applications in causality analysis domain as a reference directory