The diffusion of COVID-19 across Italian provinces: a spatial dynamic panel data approach with common factors

This study adopts a spatial dynamic panel data model with common factors and a connectivity matrix based on cross-province population flows to help explain the spread of COVID-19 infections across Italian provinces during the period 2020–21. We find that an increase in the infections in a province has a positive and statistically significant effect on neighbours' infections, which highlights the relevance of spatial spillover effects. This finding is robust to several robustness checks. Furthermore, we investigate cross-provincial transmission heterogeneity using a heterogeneous spatial dynamic panel, which provides novel insights into the diffusion patterns of the disease. © 2023 Regional Studies Association.

Treatment of allergic rhinitis in pandemic times. Consensus statement from medical societies of Latin America and the Caribbean

Allergic rhinitis has been increasing in Latin American countries, leading to a growing population of patients who need medical treatment for this respiratory condition. Its similarity to COVID-19 in terms of symptoms and the possibility of concurrence with it, make allergic rhinitis of particular interest to health systems. The countries of Latin America and the Caribbean have been particularly vulnerable due to multiple challenges, including high poverty rates, limited access to medical care and limitations in the provision of basic health services, as well as the absence of guidelines of treatment for allergic rhinitis in a pandemic situation. With the aim of to provide essential management for multidisciplinary teams in Latin America and the Caribbean regarding the evaluation and treatment of allergic rhinitis during the COVID-19 pandemic, published scientific literature on the treatment of allergic rhinitis and COVID-19 was reviewed, and the opinion of leading professionals from scientific societies in the region was considered. The different measures to avoid infections and the different treatment strategies were analyzed, with an emphasis on intranasal therapy and treatment with allergy vaccines. A position statement was formulated with the intention of maintaining continuity of medical service in the context of a pandemic and minimizing the spread, infection and complication associated with severe acute respiratory syndrome coronavirus 2 in patients undergoing or starting treatment for allergic rhinitis. Copyright © 2022, Finlay Ediciones. All rights reserved.

Machine Learning Analysis in the Prediction of Diabetes Mellitus: A Systematic Review of the Literature

In recent years, diabetes mellitus has increased its prevalence in the global landscape, and currently, due to COVID-19, people with diabetes mellitus are the most likely to develop a critical picture of this disease. In this study, we performed a systematic review of 55 researches focused on the prediction of diabetes mellitus and its different types, collected from databases such as IEEE Xplore, Scopus, ScienceDirect, IOPscience, EBSCOhost and Wiley. The results obtained show that one of the models based on support vector machine algorithms achieved 100% accuracy in disease prediction. The vast majority of the investigations used the Weka platform as a modeling tool, but it is worth noting that the best-performing models were developed in MATLAB (100%) and RStudio (99%). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

On the link between temperature and regional COVID-19 severity: Evidence from Italy

This study analyzes the link between temperature and COVID-19 incidence in a sample of Italian regions during the period that covers the first epidemic wave of 2020. To that end, Bayesian model averaging techniques are used to analyze the relevance of temperature together with a set of additional climatic, demographic, social, and health policy factors. The robustness of individual predictors is measured through posterior inclusion probabilities. The empirical analysis provides conclusive evidence on the role played by temperature given that it appears as one of the most relevant determinants reducing regional coronavirus disease 2019 (COVID-19) severity. The strong negative link observed in our baseline analysis is robust to the specification of priors, the scale of analysis, the correction of measurement errors in the data due to under-reporting, the time window considered, and the inclusion of spatial effects in the model. In a second step, we compute relative importance metrics that decompose the variability explained by the model. We find that cross-regional temperature differentials explain a large share of the observed variation on the number of infections.