Spatiotemporal heterogeneity of SARS-CoV-2 diffusion at the city level using geographically weighted Poisson regression model: The case of Bologna, Italy

Abstract

This paper aimed to analyse the spatio-temporal patterns of the diffusion of SARS-CoV-2, the virus causing coronavirus 2019 (COVID-19, in the city of Bologna, the capital and largest city of the Emilia-Romagna Region in northern Italy. The study took place from February 1st, 2020 to November 20th, 2021 and accounted for space, sociodemographic characteristics and health conditions of the resident population. A second goal was to derive a model for the level of risk of being infected by SARS-CoV-2 and to identify and measure the place-specific factors associated with the disease and its determinants. Spatial heterogeneity was tested by comparing global Poisson regression (GPR) and local geographically weighted Poisson regression (GWPR) models. The key findings were that different city areas were impacted differently during the first three epidemic waves. The area-to-area influence was estimated to exert its effect over an area with 4.7 km radius. Spatio-temporal heterogeneity patterns were found to be independent of the sociodemographic and the clinical characteristics of the resident population. Significant single-individual risk factors for detected SARS-CoV-2 infection cases were old age, hypertension, diabetes and co-morbidities. More specifically, in the global model, the average SARS-CoV-2 infection rate decreased 0.93-fold in the 21–65 years age group compared to the >65 years age group, whereas hypertension, diabetes, and any other co-morbidities (present vs absent), increased 1.28-, 1.39- and 1.15-fold, respectively. The local GWPR model had a better fit better than GPR. Due to the global geographical distribution of the pandemic, local estimates are essential for mitigating or strengthening security measures.

Exploring the Relationship between Melioidosis Morbidity Rate and Local Environmental Indicators Using Remotely Sensed Data

“…The kernel size is determined by the number of observations, with the distance adapted to the density of the nearest neighbours, resulting in a non-uniform spatial weighting shape. This assertion is supported by several previous studies [50][51][52]. The results demonstrate the percentage deviance, explaining the potential relationship between environmental indicators and the morbidity rate of melioidosis in each tambon.…”

Wastewater-Based Epidemiology for SARS-CoV-2 in Northern Italy: A Spatiotemporal Model

“…The W i coefficient, which is constant in time and variable across space, was defined as the ratio between a specific infection rate for area i ( A i ) and the infection rate for the entire study area ( A TOT ) (Equation (4)). To establish A i and A TOT , the Poisson regression was employed, and the regression coefficients ( β 0 , β 1 ,.. β n ) were derived from a study conducted in the municipality of Bologna from February 2020 to November 2021 [ 30 ] ( Table S4 ). The variables ( x 1 , x 2 … x n ) were calculated for each area and represent the fractions of the population belonging to the following classes: age (0–21, 21–65, >65), sex (M/F), family size (1, 2, 3, >3), and comorbidities (hypertension, diabetes, and the “other comorbidities” considered in the study [ 30 ]).…”

Wastewater-Based Epidemiology for SARS-CoV-2 in Northern Italy: A Spatiotemporal Model

“…4). To establish Ai and ATOT, Poisson regression was employed, and the regression coefficients (β0,β1,..βn) were derived from a study conducted in the municipality of Bologna from February 2020 to November 2021 [30] (Table S4). The variables (x1, x2… xn) were calculated for each area and represent the fractions of the population belonging to the following classes: age (0-21, 21-65, >65), sex (M/F), family size (1, 2, 3, >3), and comorbidities (hypertension, diabetes and the "other comorbidities" considered in the study [30]).…”