Does the COVID-19 affect food consumption patterns? A transaction cost perspective

The purpose of this research is to study transaction costs and their antecedents, in relation to the willingness to buy groceries online in Italy, and to observe the effect of Covid-19 is having in those. The study used a positivist deductive approach to the theory development. To evaluate the relations, we developed a PLS-SEM using SmartPLS version 3.3.3, and tested the model using WarpPLS 7.0. The pandemic's discomforts impact significantly the willingness to buy food online, and it is also a mediator between transaction costs and willingness to buy online. The findings may help those manufacturers struggling with low-performing e-commerce during the Covid-19 pandemic. When restrictions are enforced, manufacturers should take action to reduce the uncertainty associated with online shopping. From the political point of view, it highlights the need for institutional help in organising online supply chains.

A computational approach in the diagnostic process of COVID-19. Synergy between the laboratory and emergency department

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic.According to the CDC, RT-PCR in respiratory samples is the gold standard for confirming the disease, although it has practical limitations as time-consuming procedures and a high rate of false-negative results. Based on data collected at Careggi Hospital from April 7th-30th 2020,we aim to assess the accuracy of a COVID-19 diagnosis through classification methods based on blood tests and information collected at the ED. 971 pts with pre-specified features of suspected COVID-19 were enrolled;physicians prospectively dichotomized patients in COVID-19 likely/unlikely based on clinical features plus results of bedside imaging.Considering the limits of each method to classify a case COVID-19 positive, further evaluation was performed to form the COVID-19 final diagnosis, established after independent clinical review of 30-day follow-up data. Several classifiers were implemented, both parametric (Logistic Regression, LR;Quadratic Discriminant Analysis, QDA) and non-parametric (Random Forest, RF;Support Vector Machine;Neural Networks;K-nearest neighbour;Naive Bayes). Log transform was applied to some of the covariates and results compared with non transformed data.The dataset was divided in training and validation sets.Results based on validation sample show an AUC>0.8 for all classifiers. Best results are obtained applying RF, LR and QDA to a rebalanced sample using the SMOTE techniques on the log transformed data, showing an AUC of 0.890 (LR),0.896 (QDA) and 0.864 (RF). In parallel, best Sens and Spec are obtained via the above methods, the highest chieved by the LR (Sens 0.696;Spec 0.877). The rather high rate of false negative seems to be a feature inherently characterizing this classification problem.Good discriminatory power was shown for: WBC, Neut, AST, LDH, PCR, Na, IL-6 plus symptoms' information. Parametric models have the additional advantage of allowing a scientific interpretation.The performance of the classifiers with respect to the physician's gestalt and data validation are ongoing. The proposed classifiers show a good level of Sens.To improve Spec, a 3-level classification can be implemented;this tool can help in taking decisions when time and resources are scarce.