Spatio-Temporal Spread Pattern of COVID-19 in Italy

Abstract

This paper investigates the spatio-temporal spread pattern of COVID-19 in Italy, during the first wave of infections, from February to October 2020. Disease mappings of the virus infections by using the Besag–York–Mollié model and some spatio-temporal extensions are provided. This modeling framework, which includes a temporal component, allows the studying of the time evolution of the spread pattern among the 107 Italian provinces. The focus is on the effect of citizens’ mobility patterns, represented here by the three distinct phases of the Italian virus first wave, identified by the Italian government, also characterized by the lockdown period. Results show the effectiveness of the lockdown action and an inhomogeneous spatial trend that characterizes the virus spread during the first wave. Furthermore, the results suggest that the temporal evolution of each province’s cases is independent of the temporal evolution of the other ones, meaning that the contagions and temporal trend may be caused by some province-specific aspects rather than by the subjects’ spatial movements.

Spatio-temporal distribution pattern of COVID-19 in the Northern Italy during the first-wave scenario: The role of the highway network

“… Giuliani et al (2020) proposed an endemic-epidemic time-series mixed-effects generalized linear model for areal disease counts: they found that containment policies limited the spread to nearby areas. D’Angelo et al (2021) investigated the spatio-temporal spread pattern of COVID-19 in Italy from February to October 2020: they confirmed the conclusions of Giuliani et al (2020) , and suggested that suggests the temporal evolution of cases in each province does not depend on the temporal evolution of the other provinces.…”

Bayesian Imputation of COVID-19 Positive Test Counts for Nowcasting Under Reporting Lag

“…Generally, LTLAs which are spatially close may exhibit similar trends in the number of positive tests, and so we may expect a spatial coupling between neighbouring LTLAs to yield performance improvements. The most common models for dealing with spatial dependence are typically based on GMRFs; the Besag York Mollié (BYM) model (Besag et al, 1991 ) posits spatial dependence through such a field, and although we do not employ this spatial structure here, a number of algorithms for posterior inference under this construction (Morris et al, 2019 ; Rue et al, 2009 ) have been used to study COVID‐19 (D'Angelo et al, 2021 ). We note however that in this work we have observed mixed reliability in the spatial dependence of reporting rates.…”

Spatiotemporal Associations between Local Safety Level Index and COVID-19 Infection Risks across Capital Regions in South Korea

“…The convolution/Besag–York–Mollié (BYM) model, which is a sum of the intrinsic conditional autoregressive (ICAR) prior for and unstructured random effect, , is widely adopted to account for spatial correlation [ 38 ]. Regarding the ICAR structure, is the vector of the spatially structured latent effects excluding (i.e., ) and refers to the set of neighboring regions for municipality, s , that allows us to define , the number of neighbors of the s -th geographical unit [ 39 , 40 ]. This formulation indicates that for any s , the conditional expectation of is a weighted mean of its neighboring municipalities, with those being closer to s in some sense will have a higher contribution to that mean.…”

“…This scaled re-parametrization of the conventional BYM model ensures we can verify the relative contribution of each component on the overall spatial correlation. Meanwhile, to address temporally structured random effects, ( ), we adopted a nonparametric random walk of order 1 (RW1) process [ 39 , 40 , 44 , 45 ]. …”